title_s
stringlengths 2
79
| title_dl
stringlengths 0
200
| source_url
stringlengths 13
64
| authors
listlengths 0
10
| snippet_s
stringlengths 0
291
| text
stringlengths 21
100k
| date
timestamp[ns]date 1926-02-14 00:00:00
2030-07-14 00:00:00
| publish_date_dl
stringlengths 0
10
| url
stringlengths 15
590
| matches
listlengths 1
278
|
|---|---|---|---|---|---|---|---|---|---|
Artificial intelligence in health care | Deloitte Insights
|
Smart use of artificial intelligence in health care
|
https://www2.deloitte.com
|
[
"Principal",
"Deloitte Consulting Llp",
"Deloitte Risk",
"Financial Advisory",
"Deputy Manager",
"Deloitte Center For Health Solutions",
"Kumar Chebrolu",
"Dan Ressler",
"Hemnabh Varia"
] |
For the health care industry, AI-enabled solutions can provide immediate returns through cost reduction, help with new product development, ...
|
Executive summary
Artificial intelligence (AI) is already delivering on making aspects of health care more efficient. Over time it will likely be essential to supporting clinical and other applications that result in more insightful and effective care and operations. AI has multiple use cases throughout health plan, pharmacy benefit manager (PBM), and health system enterprises today, and with more interoperable and secure data, it is likely to be a critical engine behind analytics, insights, and the decision-making process. Enterprises that lean into adoption are likely to gain immediate returns through cost reduction and gain competitive advantage over the longer term as they use AI to transform their products and services to better engage with consumers.
Deloitte conducted the State of AI survey in late 2019 which featured questions around how organizations are adopting, benefiting from, and managing AI technologies by industry. This survey was conducted before COVID-19 significantly impacted the United States. The survey found that:
Health care organizations vary significantly in their AI investments: Seventy-five percent of large organizations (annual revenue of over US$10 billion) invested over US$50 million in AI projects/technologies, while approximately 95% of mid-sized organizations (annual revenue of US$5 billion to US$10 billion) invested under US$50 million. Seventy-three percent of all organizations expected to increase their funding in 2020.
Top outcomes health care organizations are trying to achieve through AI are making processes more efficient (34%), enhancing existing products and services (27%), and lowering costs (26%).
Respondents from health care organizations reported that their main concerns about risk with AI were the cost of the technologies (36%), integrating AI into the organization (30%), and implementation issues, including AI risks and data issues (28%).
The current pandemic overwhelmed health systems and exposed limitations in delivering care and reducing health care costs. The period from March 2020 saw an unprecedented shift to virtual health, fueled by necessity and regulatory flexibility.1 The pandemic opened the aperture for digital technologies such as AI to solve problems and highlighted the importance of AI. Even though the survey was fielded before the public health crisis, some of the outcomes and challenges that health care organizations had in using AI prior to the pandemic will likely continue to be instructive as health systems, health plans, and PBMs develop their new AI investment strategies.
Health systems were challenged by historic lower revenues due to nonurgent care and were forced to scale back during the pandemic. They can expect to gain advantage by using AI for applications to support cost savings as they transform.
Health plans and, in the longer-term, health systems, can use AI-enabled solutions to gain insights, develop new products and services, and better engage with consumers. Health plans can also use AI to proactively detect and manage fraud, waste, and abuse, resulting in recovered payments and cost avoidance, saving them millions and improving patient care.
This could well include an expansion of AI’s reach into clinical and back-office applications. Even as health care organizations step up their investments into data and analytics with AI, they should pair these with a robust security and data governance strategy.
Introduction
AI is gaining traction in health care, starting with automating manual and other processes, and the number of use cases and sophistication in the use of the technology is growing. In our vision of the Future of Health, we view radically interoperable data as central to the promise of more consumer-focused, prevention-oriented care, and analytics as critical to using the vast data that will be generated by ubiquitous sources. AI has already become embedded into analytics and is likely to become even more so in the future.
AI uses algorithms and machine learning (ML) to analyze and interpret data, deliver personalized experiences, and automate repetitive and expensive health care operations. These functions have the potential to augment the work of both operational and clinical staff in decision-making, reduce the time spent in administrative tasks, and allow humans to focus on more challenging, interesting, and impactful management and clinical work.
Today, health care organizations experience pervasive problems across their value chains, spanning every process on the continuum from care to cure. In the future, health care organizations that apply AI across every process from care to cure can improve the health and well-being of consumers. Deloitte’s Cognitive Care to Cure solution is an AI-powered, cloud-based, digital health care solution-as-a-service that can be applied across the health care value chain to improve operational efficiencies, reduce costs, and support better health outcomes for consumers (figure 1). These are all on the same platform and can offer efficiency to organizations looking for multiple solutions. They are also available as individual services, allowing health care organizations to choose from a menu of offerings that meet their needs and strategy.
| 2020-10-22T00:00:00 |
https://www2.deloitte.com/us/en/insights/industry/health-care/artificial-intelligence-in-health-care.html
|
[
{
"date": "2020/10/22",
"position": 55,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 61,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 56,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 55,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 54,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 57,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 56,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 57,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 56,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 51,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 56,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 57,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 57,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 57,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 58,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 59,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/10/22",
"position": 56,
"query": "artificial intelligence healthcare"
}
] |
|
The Employment Consequences of Robots: Firm-level Evidence
|
The Employment Consequences of Robots: Firm-level Evidence
|
https://www150.statcan.gc.ca
|
[] |
2017), leading to both labour displacement and wage reductions (Bessen et al. 2019). Using a measure of robot penetration at the industry level ...
|
Skip to text
Text begins
Abstract
As a new general-purpose technology, robots have the potential to radically transform industries and employment. In contrast to previous studies at the industry level that predicted dramatic employment declines, this study finds that investments in robotics are associated with increases in total firm employment, but decreases in the total number of managers. It also finds that robot investments are associated with an increase in the span of control for managers remaining within the organization. This study provides evidence that robot adoption is not motivated by the desire to reduce labour costs, but is instead related to improving product and service quality. These findings are consistent with the notion that robots reduce variance in production processes, diminishing the need for managers to monitor workers to ensure production quality. Decreases in managerial headcount may also arise from changes in workforce composition. This study finds that investments in robotics are associated with decreases in employment for middle-skilled workers, but increases in employment for low-skilled and high-skilled workers, potentially changing managerial activities required by the firm. With respect to organizational change, this study shows that robots predict both the centralization and the decentralization of decision-making authority, but decision rights in either case are reassigned away from the managerial level of the hierarchy. This contrasts with previous studies on information technology that have generally found decentralizing effects on decision-making authority within organizations. Overall, the results of this study suggest that the impact of robots on employment and organizational practices is more nuanced than previous studies have shown.
Executive summary
Fears of artificially intelligent machines have lingered in the human imagination for thousands of years. Greek myths like those of Talus or Pandora told of artificial beings created by the gods wreaking chaos and destruction when they were sent to live among mortals on earth. Recent breakthroughs in artificial intelligence have expanded the production potential of machines. At the same time, this has focused attention on the potential for robots to wreak havoc on labour markets. Machines imbued with humanlike judgment and flexibility threaten to displace human workers from many of the tasks they currently perform in the economy.
However, it is possible that the impact of robots will not be very different from previous waves of automation that created enough tasks for humans to compensate for the workers that new machines displaced. Although switching workers to other tasks was often fraught and not all of them could benefit, past automation generated a roughly constant share of rapidly increasing output.
Whether robotic automation will lead to a permanent decline in the role of labour—or play out like its non-robotic predecessors—depends on how firms reorganize production after adopting robots. This study uses newly compiled firm-level administrative data from 1996 to 2017 to examine how Canadian firms that adopt robotic technology change their production processes and what happens to their workers when they do.
The study finds that robot adoption in Canada is not motivated by the desire to reduce labour costs, but instead by the desire to improve product and service quality. These improvements are associated with higher productivity. In contrast to previous studies at the industry level that predicted dramatic employment declines, this paper finds that investments in robotics are associated with increases in total employment in adopting firms. However, it also finds that these firms organize production around fewer managers, with each supervising more workers. These findings are consistent with the notion that robots reduce variance in production processes, reducing the need for managers to monitor workers to ensure production quality.
Decreases in managerial headcount may also arise because of changing managerial roles associated with changes in workforce composition. Investments in robotics are associated with decreasing middle-skilled employment alongside increases in the low-skilled and high-skilled workforce. Robots predict both the centralization (toward owners) and the decentralization (toward production workers) of decision-making authority, but decision rights in either case are reassigned away from the managerial level of the hierarchy. This contrasts with prior studies on information technology that have generally found decentralizing effects on decision-making authority within organizations.
Overall, the results of this study suggest that the impact of robot adoption on employment has not been apocalyptic for labour overall. However, changes in organizational practices associated with robot adoption will require a different mix of skills than many parts of the economy currently employ.
1 Introduction
This study examines how employment and organizations have changed in response to robot adoption. As robotics and artificial intelligence (AI) become increasingly used by firms as the next engine of innovation and productivity growth, their effects on labour, firm practices and productivity have become a subject of growing importance. According to extensive anecdotal evidence in the media, robots reduce overall employment and exacerbate income inequality, as rapid advancements in vision, speech, natural language processing and prediction capabilities have achieved parity with or exceed human capabilities across a range of tasks. These technological advancements have shifted the comparative advantage from humans to machines for a growing list of occupations (Brynjolfsson and Mitchell 2017; Felten, Raj and Seamans 2019; Frey and Osborne 2017), potentially leaving human labour with substantially fewer activities that can add value (Brynjolfsson and McAfee 2014; Ford 2015). This technology-based labour substitution may displace a significant proportion of the overall workforce, despite generating productivity gains (Acemoglu and Restrepo 2020; Autor and Salomons 2017; Ford 2015). If true, robot adoption is likely to cause significant changes in how firms organize production activities and manage their human capital (Bidwell 2013; Puranam, Alexy and Reitzig 2014; Zammuto, Griffith, Majchrzak and, Dougherty 2007).
Recent empirical studies that used data at the industry or geographic region levels found that robots were associated with drastic declines in overall employment (Acemoglu and Restrepo 2020; Dinlersoz and Wolf 2018; Graetz and Michaels 2018; Mann and Püttmann 2017). However, it has also been argued that robots are similar to past generations of general-purpose technologies (GPTs) that ultimately increased labour demand. In this competing view, even as labour is displaced, the new jobs created will more than compensate for the jobs lost (Autor and Salomons 2017). Preliminary evidence based on firm-level data supports this view and shows that robot-adopting firms become more productive and ultimately increase total employment (Koch, Manuylov and Smolka 2019). These new jobs are likely to complement robots, suggesting a compositional change in labour within firms. As robots offer new capabilities that differ from prior information technology (IT) investments (Brynjolfsson and Mitchell 2017), changes in human capital and the organization of production activities may also differ from those caused by IT and reflect those that are complementary to robots.
This study uses comprehensive data on businesses in the Canadian economy from 2000 to 2015 to show that robots are associated with increases in total employment, but the effect is not uniform across workers. Investments in robotics predict substantial declines in managerial employment, despite increases in non-managerial employment. This finding contrasts with prior IT that could not easily replace managerial and professional work (Autor, Katz and Kearney 2006; Autor, Levy and Murnane 2003; David and Dorn 2013; Dustmann, Ludsteck and Schönberg 2009; Murnane, Levy and Autor 1999). There is evidence that robots may affect managerial employment in two ways. First, robots may directly reduce the need to monitor and supervise workers, as they can substantially diminish human errors in the production process. Because worker supervision accounts for a substantial portion of work done by managers (Hales 1986), demand for managerial labour to supervise workers may decline with robot adoption. Second, robots may also indirectly affect managerial employment by changing the types of workers needed. Although the total number of non-managerial employees increases with robot adoption, this study also found that robot investments predict decreases in the employment of middle-skilled workers and increases in the employment of low-skilled and high-skilled labour. These changes in labour composition may lead to a decrease in managers (Malone 2003; Mintzberg 2013). Consistent with the findings of an increase in non-managerial employees and a decrease in the number of managers, this study found that robot investments predicted an increase in the span of control for managers remaining within the organization.
This study examined the motivations for robot adoption by firms, and the findings indicate that robot investment is not associated with the strategic importance of reducing labour costs, but is instead associated with an increase in the strategic importance of improving product and service quality. With regard to the allocation of decision-making authority within organizations, this study found that robot investments predicted both the centralization and the decentralization of decision-making authority away from the managerial level of the hierarchy. This suggests that, not only has managerial headcount decreased, but their decision-making authority has also diminished. This is different from earlier studies that found that IT generally led to the decentralization of decision-making rights (Acemoglu et al. 2007; Bresnahan, Brynjolfsson and Hitt 2002). Overall, the results show that changes in employment are related to complementary changes in organizational practices that are critical to the effective use of robots.
This study provides the most comprehensive evidence possible at the level of individual businesses on the employment and organizational effects of robot investments. The wide range of outcomes examined—employment, labour composition, span of control, strategic priorities and allocation of decision-making rights—suggests that robots have a substantive effect on both employment and the organization of production in different ways than previous technologies. This analysis also provides a deeper data-driven examination of how robots can change employment and organizational practices that are difficult to capture using country- and industry-level data (Raj and Seamans 2018). More broadly, the results of this study suggest that looking at individual organizations in detail can provide useful insights to the important debate about the consequences of robots for labour and organizations.
2 Theoretical considerations
The adoption of GPTs is often associated with substantial and widespread productivity gains across different sectors of the economy (Bresnahan and Trajtenberg 1995). To maximize the value of GPTs , firms must substantially reorganize their work activities and change the nature of work and human capital requirements (Autor, Levy and Murnane 2003; Bresnahan, Brynjolfsson and Hitt 2002; Brynjolfsson, Rock and Syverson 2018). As a recent and rapidly proliferating GPT (Brynjolfsson, Rock and Syverson 2018; Cockburn, Henderson and Stern 2018), robots have the potential to transform employment, firm practices and the economy (Agrawal, Gans and Goldfarb 2018; McAfee and Brynjolfsson 2017).
2.1 Robots and total employment
The effect of robots on employment is still undetermined. Research examining the effect of robots on labour is still nascent, with only a few studies examining the substitutability of robots on work (Acemoglu and Restrepo 2020; Arntz, Gregory and Zierahn 2016; Frey and Osborne 2017; Mann and Püttmann 2017; Manyika et al. 2017). However, most of these preliminary studies predict dire consequences resulting from the labour displacement attributable to robot adoption. For example, Frey and Osborne (2017) found that up to 47% of all jobs in the United States could be displaced. Using a task-based approach that divided each occupation into a set of concrete tasks, Organisation for Economic Co-operation and Development researchers found that 70% of tasks performed by labour could be automated (Arntz, Gregory and Zierahn 2016). Other studies that used the task-based approach found that over 50% of work tasks were vulnerable to automation (Manyika et al. 2017), leading to both labour displacement and wage reductions (Bessen et al. 2019). Using a measure of robot penetration at the industry level in the United States, Acemoglu and Restrepo (2020) found that one robot could replace roughly six people. Graetz and Michaels (2018) used similar data on robot adoption for 17 countries and also found robot adoption to be associated with a reduction in work hours for low-skilled labour.
The findings of these initial studies are in stark contrast with earlier generations of technologies that have been found to increase employment in conjunction with productivity, ultimately leading to labour’s share of productivity remaining constant. Instead of reducing employment, robots may positively affect employment through (1) productivity increases from labour substitution inducing demand for other goods and services that require non-automated tasks; (2) capital deepening that increases the effectiveness of robots, which can increase productivity without further reducing labour; and (3) the creation of new tasks or increased demand for existing tasks that are complementary to those of robots (Acemoglu and Restrepo 2018; Brynjolfsson, Rock and Syverson 2018). Initial results from surveys of Spanish manufacturing firms suggest that organizations that adopt robots experience both productivity and employment gains (Koch, Manuylov and Smolka 2019).
These differing results are attributable in part to difficulties in observing these countervailing effects in an entire economy using data at the industry and geographic region levels. Studies at these levels of analysis cannot clearly examine how firms use robotics to substitute or complement labour. As prior literature examining the link between IT and productivity has shown, analysis at more aggregated levels can often lead to markedly different conclusions from empirical studies conducted at the firm level (Bresnahan, Brynjolfsson and Hitt 2002; Brynjolfsson and Hitt 1996). These differences can arise from the substantial heterogeneity in productivity growth across firms that cannot be clearly observed at the industry level or other aggregated levels of analysis (Syverson 2004). For example, robot-adopting firms may experience productivity and employment gains while non-adopting firms in the same industry experience employment and productivity losses. If this is true, even if robots are observed to cause employment losses at the industry level, it remains unclear whether robots displace workers within robot-adopting firms or whether workers are instead displaced in non-adopting firms because of a decrease in competitiveness. Without a clear understanding of these underlying mechanisms, it is particularly challenging to make meaningful inferences, with similar empirical issues hampering early attempts to understand the effects of IT investment on organizations. Ultimately, it was critical to obtain more precise measurement of both IT and organizational capabilities at the firm level to resolve the IT –productivity paradox discovered by earlier studies and uncover the factors behind the heterogeneous effects of IT on firm outcomes (Brynjolfsson, Hitt and Yang 2002). This study uses a firm-level measure of robot investments for the population of firms in Canada to empirically investigate the competing hypotheses of whether robot-adopting firms increase or decrease employment in firms.
H1a: Robot investments are associated with increases in total employment.
H1b: Robot investments are associated with decreases in total employment.
2.2 Robots and non-managerial employment
Regardless of the effect on total employment, workforce composition is likely to change with robot adoption as demand for different skills changes within the firm. This is similar to what occurred in prior generations of skill-biased technological change. For example, the rise of IT in the late 1990s led to a reduction in the demand for low-skill and middle-skill occupations as routine tasks became automated, and a corresponding increase in demand for non-routine and cognitively challenging tasks, including managing employees (Autor, Katz and Kearney 2006; Autor, Levy and Murnane 2003; Card and DiNardo 2002; Murnane, Levy and Autor 1999). Similar to these studies, low-skilled workers were defined in this study as those working in occupations requiring a high school degree or less, middle-skilled workers were defined as those working in occupations requiring vocational or trades accreditation or an associate degree, and high-skilled workers were defined as those working in occupations requiring at least an undergraduate university degree. Although it has been argued that non-routine and cognitively challenging tasks are difficult to automate (Autor, Levy and Murnane 2003; Murnane, Levy and Autor 1999), the increasing sophistication of robots is likely to automate tasks that were previously unaffected by automation.
With advances in vision, speech and prediction capabilities, robotics has advanced beyond automating simple routine tasks, and robots have now become capable of performing more cognitively complex work, as well as tasks involving specific types of manual dexterity. Middle-skilled workers are more likely to perform these tasks that robots are becoming more able to automate. For example, in the health care and pharmaceutical industries, robots have been used to handle and prepare materials, follow complex protocols to prepare and analyze samples, and help coordinate patient care without human intervention (Gombolay et al. 2018). Firms with significant warehousing operations have also experienced similar effects. Robots have automated a large range of warehousing logistics activities by effectively transporting objects between locations without human intervention. By relieving humans of lifting and handling awkward, heavy objects during inventory management, robots not only avoid injuries but also provide consistency in product quality and decrease overall delivery time. In manufacturing, industrial robots can substantially reduce variance in product quality. Machine vision enables robots in the automotive industry to consistently install and weld parts onto car bodies with a high degree of precision, minimizing errors in the production process. This can involve difficult manual manipulations such as 360-degree multi-arm rotations with many repetitions. Robots can be programmed to perform these tasks precisely over a long period of time. As a result, robots can substantially reduce both unintended human errors, such as those arising from fatigue, and deliberate actions, such as gaming production quotas, that have previously impeded productivity and effective management (Helper and Henderson 2014).
These illustrative examples suggest that robots can automate certain complex tasks that were primarily the responsibility of middle-skilled workers, including technicians, machinists and operations personnel from a variety of industries that are responsible for following complex protocols to ensure production quality. These tasks may also involve certain types of manual dexterity that require significant learning over time for humans. With robots, many of these tasks can be automated using algorithms, eliminating human errors and the need to provide training for these skills. By reducing production quality variance, robots can decrease the demand for middle-skilled work, as these tasks are vulnerable to robot-based automation.
H2: Robot investments are associated with decreases in middle-skilled employment.
However, investments in robotics may also create demand for human labour and tasks that complement robots. While demand for middle-skilled work may decrease through direct substitution, demand for complementary work—either lower or higher skilled—may increase with robot adoption. For firms that redesign their production processes to leverage the capabilities that robots can offer, productivity may increase, ultimately leading to increases in employment for specific types of workers. Despite recent technological advances, robots are often unable to fully automate most production processes. For many of these so-called residual tasks, human labour remains a more efficient and cost-effective solution (Autor, Levy and Murnane 2003; Brynjolfsson and Mitchell 2017). For example, Elon Musk famously scaled back investments in automation in the Tesla factory and reintroduced human workers after too much automation slowed the production of the Model 3 electric vehicle and delayed its market launch (Hawkins 2018). To use robots effectively, human capital must also be reorganized and reassigned to assist with production. For example, Amazon significantly redesigned work in its warehouses to use its Kiva Robotic systems effectively. As part of this redesign, robots are used to travel between locations within the warehouse, but human workers pick and pack the products delivered by the robots. In this case, instead of using middle-skilled workers to manage inventory by walking from shelf to shelf to examine and handle products, robots and algorithms can automate this process and bring inventory to human workers directly. These human workers then pick up the items and place them into shipping boxes. Researchers have also systematically matched occupations to what machine learning can do and found that many of the manual skills performed by low-skilled labour cannot be replaced easily with technology (Brynjolfsson and Mitchell 2017; Felten, Raj and Seamans 2019). While machine learning is not identical to robot technology, robotics relies heavily on machine learning to make inferences, which can be a useful indicator of the potential impact of robots on work.
Current evidence suggests that, although robots can increase manual dexterity for certain tasks, they cannot yet effectively perform many manual tasks that humans can do easily. As a result, productivity increases arising from robot investments will lead to increases in demand for low-skilled workers doing these residual tasks.
H3: Robot investments are associated with increases in low-skilled employment.
Demand for high-skilled workers may also increase with robot adoption. As illustrated in the example of how Amazon reorganized warehouse work activities after robot adoption, the majority of productivity gains from technology adoption come from the complementary redesign of work (Bresnahan, Brynjolfsson and Hitt 2002; Hammer 1990). Implementing the necessary process improvements and work reorganization requires highly skilled professionals (Bresnahan, Brynjolfsson and Hitt 2002; Hammer 1990; Helper and Henderson 2014; Huselid and Becker 1997; Ichniowski, Shaw and Prennushi 1997), some of whom are needed to program, repair, customize and work with robots (Acemoglu and Restrepo 2020; Autor and Salomons 2018; Brynjolfsson and Mitchell 2017).
However, demand for high-skilled workers may also increase for those that do not work with robots directly, as automating certain routine tasks can free up resources to engage in more cognitively complex tasks. For example, when hospitals adopt robots to lift patients out of beds, nurses are not only relieved of the physical strain of tasks that are more likely to cause injuries, but are also given more time to interact with patients and participate in clinical treatment (Gombolay et al. 2018). Similarly, by algorithmically providing pills and other medications to patients directly (Bepko, Moore and Coleman 2009), nurses can spend more time ensuring compliance and making other clinical decisions. In the manufacturing sector, where a majority of the routine production process is done by robots and low-skilled labour, time and resources can be freed up for high-skilled professionals to design and market new products and optimize production processes (Felten, Raj and Seamans 2019). Programmable robots can also increase a firm’s flexibility to serve different types of orders and provide a greater range of products. This can further increase the demand for high-skilled workers who can design a wider variety of products.
Consistent with these findings, Autor and Dorn (2009) found that investments in computer technologies over the last several decades contributed to the widespread increase in high-skilled jobs involving creative, problem-solving and coordination tasks. Similarly, Felten, Raj and Seamans (2019) found that investments in AI were correlated with the increased employment of high-skilled workers such as software engineers. Therefore, the employment of high-skilled workers is also expected to increase after robot adoption.
H4: Robot investments are associated with increases in high-skilled employment.
2.3 Robots and managerial employment
Managerial employment may also change significantly with robot adoption. When production is automated using robotics, human errors are substantially reduced and variance in production quality decreases (Verl 2019). Unlike humans, robots can precisely perform the same complex process repeatedly for long periods of time without experiencing fatigue, resulting in both productivity increases and fewer errors in the production process. Agency problems arising from information asymmetries also do not exist with robots, as they do not operate in their own self-interest the way humans might in work settings (Eisenhardt 1989; Hong, Kueng and Yang 2019; Jensen and Meckling 1976). Because of the substantial cost of employee monitoring for firms (Dickens et al. 1989, 1990) and considerable time spent by managers monitoring employee activities (Hales 1986, 1999), using robots in the production process can substantially reduce the need to monitor work effort and quality closely. Through both a reduction in production process variance and a lack of agency costs associated with managing robots, the level of monitoring required to ensure production quality is likely to decline. Because monitoring and control constitute a significant portion of managerial activities (Kolbjørnsrud, Amico and Thomas 2016), the demand for managerial labour is likely to decrease after robot adoption.
While robots can reduce demand for managers by decreasing the need to monitor employees during the production process, they may also affect managerial work by changing the composition of non-managerial employees within the organization. If robot adoption is associated with a decline in middle-skilled workers and an increase in high-skilled and low-skilled workers, managerial activities may change for the newly transformed workforce. Managing low-skilled workers can be very different from managing other types of employees, as low-skilled work is typically more standardized and—consequently—easier to monitor and evaluate than higher-skilled work (Mintzberg 1980; Perrow 1967). Furthermore, an individual manager can potentially supervise many more employees if digital tools automate aspects of the monitoring process for standardized work. For example, technology can be used to organize and report the output of simple routine tasks and even make predictions about work outcomes (Aral, Brynjolfsson and Wu 2012), especially for standardized work where inputs and outputs can be specified and clearly measured (Brynjolfsson and Mitchell 2017). In the case of Amazon, the productivity of warehouse workers is tracked in real time and an automated system generates recommendations for employee warnings and terminations when productivity targets are not met. Having an objective measure of productivity recorded using automation technology also reduces disruptive conflicts between managers and subordinates, as objective productivity measures are more difficult to dispute (Scully 2000; Wu 2013). As the proportion of low-skilled workers in the organization’s workforce increases, fewer managers may be needed within the organization.
In addition to differences in managing low-skilled work, managing high-skilled professionals is also likely to differ from managing middle-skilled workers. High-skilled workers often engage in more cognitively challenging tasks that provide higher added value, such as product design and production optimization. Managing these types of workers is likely to differ substantially from managing workers doing routine manual tasks (MacDuffie 1997; Parker and Slaughter 1988). Supervising low-skilled and middle-skilled workers primarily involves ensuring employees arrive on time, verifying compliance with rules and regulations, monitoring employees’ work procedures and output, issuing commands, and training employees to do their job properly (Helper and Henderson 2014; Taylor 1911). In comparison, employees who do more cognitively complex work are often experts themselves in dealing with problems outside routine operations and can resolve production problems better than their managers (Helper, MacDuffie and Sabel 2000; Kenny and Florida 1993). These employees are often empowered to make more decisions because they are more capable than their managers of solving relevant problems (Huselid and Becker 1997; Ichniowski, Shaw and Prennushi 1997). As a result, managing these employees may involve less direct issuing of commands and more advising and empowerment of employees to solve problems (Malone 2003; Mintzberg 1973, 2013).
While it is expected that the span of control for managing low-skilled workers will increase, this expected change is ambiguous when the subordinates in question are high-skilled workers. If workers require more advising and coaching from managers, managerial span of control may decrease (Malone 2003, 2004). It has also been argued that high-skilled workers pose unique challenges to the efficiency of organizational hierarchies because of their greater need for communication and conflict resolution, which can be mitigated by decreasing span of control (Bell 1967; Meyer 1968). However, the effective use of high-skilled labour often leads to granting them greater autonomy (Bresnahan, Brynjolfsson and Hitt 2002), potentially increasing the span of control (Simon 1946). Previous literature examining the relationship between skill composition changes and span of control in the presence of technology adoption has been limited, but the evidence that is available generally finds net positive effects on span of control (Scott, O’Shaughnessy and Cappelli 1994). If decreases in the demand for managerial labour arising from reduced monitoring requirements and skill composition changes dominate potential increases because of productivity gains, demand for managerial labour may ultimately decline. Based on these arguments, it is expected that managerial employment will decrease with robot adoption.
H5: Robot investments are associated with decreases in managerial employment.
3 Data and measures
3.1 Data
To measure robot investment at the firm level, this study uses data provided by the Canada Border Services Agency (CBSA) that capture the purchases of robots imported by Canadian firms from 1996 to 2017. The global production of robotics hardware is highly concentrated in relatively few countries, such as Japan, Germany, the United States and—increasingly—China. In comparison, Canada does not produce a meaningful quantity of robotics hardware domestically. Therefore it must import robots from foreign producers, which makes it possible to use data on import transactions to measure robot adoption by firms. For all import transactions, the CBSA classifies goods according to Harmonized System (HS) codes, and it classifies industrial robots separately from other types of technology, machinery and equipment. In addition to the HS code, the name of the exporting firm, product country of origin, name and address of the importing firm, business number of the importing firm (a unique government-issued identifier for Canadian businesses), and value of the transaction are recorded. See Dixon (2020) for details on the construction of these data.
Because this study uses import data, the definition of “robot” is ultimately based on what type of import transactions are being classified as robots. As a starting point, the International Federation of Robotics (IFR) defines industrial robots as having the characteristics of being (1) automatically controlled, (2) reprogrammable, (3) a multipurpose manipulator in three or more axes, and (4) used in industrial automation applications. The IFR provides a number of examples of robots and their primary functions in its published material and on its website. This material includes activities such as assembly, welding, painting, packaging, picking and placing, and handling materials for metal casting. In principle, firms that are members of an IFR -affiliated industry association are likely to use a definition of robot that is consistent with the IFR .
To examine the measure of robotics investment in greater detail, searches were manually conducted in the public domain for transactions accounting for 95.0% of the total value of robot purchases in the data. Members of IFR -affiliated industry associations (e.g., Robotics Industry Association, Japan Robot Association) accounted for 58.4% of the total value of imports in the data. Firms that were not robotics association members but that advertised selling the same type of robots accounted for another 13.3% of the import value. Most often, these firms were specialized in installing and integrating robots that were actually produced by association members. An additional 2.0% of the total transaction value involved exporting firms that were not affiliated with a robotics industry association but manufactured robots for scientific laboratories. According to the data, these robots were imported primarily by firms in the health care industry and were used to automate a variety of repetitive tasks in biology and chemistry research, such as pipetting.
An additional 19.0% of the total value was attributable to importing firms in robot-intensive industries—primarily the automotive industry, but also machine tools and plastics manufacturing. Some firms in these industries are members of robotics industry associations, but the data in this study have more comprehensive coverage of firms that invest in robots. Because of the well-documented prevalence of robot use in these industries and from examining the types of robots used by the importing firms in these transactions, it was possible to infer that these transactions reflected investments in robotics similar to those involving robotics association members. For the remaining 2.3% of the import value, firm websites confirmed that robots were being used in a variety of activities, including performing repairs and handling materials in hazardous environments (e.g., pipelines and nuclear power plants), and were also being used in construction and demolition.
The robot investment data used in this study were merged with two datasets maintained by Statistics Canada that contain measures of firm characteristics: (1) the National Accounts Longitudinal Microdata File (NALMF), a panel dataset that contains measures of aggregate firm-level employment and economic inputs derived from taxfiling data from 2000 to 2015, and (2) the Workplace and Employee Survey (WES), which was developed and administered by the former Business and Labour Market Analysis Division and Labour Statistics Division at Statistics Canada. The WES consists of both an employer component, which contains comprehensive information on employment and management practices at the organizational level, and a linked employee component, which measures individual-level job characteristics and activities. The employer survey sample is a random stratified sample in a panel structure. It is representative of the population of business establishments in the Canadian economy in each year. For the employee sample, individual employees were randomly chosen within each organization and surveyed for two consecutive years, with Statistics Canada resampling individuals from each organization after each two-year cycle was completed. The WES employer survey data used in this study are for 2001 to 2006, while the WES employee survey data used represent employees followed from 2001 to 2002 and 2003 to 2004.
Several adjustments to both the NALMF and WES samples were made to more precisely capture firms of sufficient size that purchased robots with the intention of implementing them as an end user for production. Only firms with at least 10 employees were included in this study, and firms in the finance and insurance sector (North American Industry Classification System [NAICS] code 52) and the real estate and rental and leasing sector ( NAICS code 53) were removed, as they were found to be involved primarily in leasing robots to other firms and comprised a negligible percentage of total robot imports into Canada. Firms in service industries that were involved in programming imported robots for the purpose of reselling them to other firms ( NAICS codes 5413, 5414, 5415 and 5416) and firms in the wholesale trade sector ( NAICS code 41) were also removed. In the final data used for analysis, the NALMF sample contained a total of 168,729 firms, the WES employer sample contained 3,981 business establishments and the WES employee sample contained 7,958 individual employees.
3.2 Robot capabilities
Dixon (2020) found that robots were especially active in the automotive and machinery and equipment assembly sectors, as well as in the plastics processing, and minerals and metals manufacturing industries.
In automotive manufacturing, robots are usually organized along a structured assembly line to fetch and position parts; fasten, rivet or weld parts together; and apply coatings or paint to the assembled parts. Robots are also prominent in the electronics assembly industry, where “pick-and-place” robots select circuits and place them on circuit boards or silicon wafers. They handle small, delicate parts with precision, selecting among different types and pressing them onto circuit boards. They can also visually inspect circuit boards, test the connections and etch circuit boards. Robots may also be involved in packaging finished products. In addition to improving quality, one of the main motivations for adopting robots in the electronics industry is the increase in flexibility they provide in serving different orders, as they can switch from large volume orders to smaller batches.
Robots are also used extensively in the processing of plastics, where they primarily perform secondary machine-tending roles. They also apply labels and move parts to other areas where they are further modified or packaged for shipment. In the injection moulding of plastic parts and packaging materials, they are also used to select items and apply labels. Overall, in the plastics processing industry, robots can replace a substantial proportion of repetitive manual labour.
In minerals and metals manufacturing, robots are involved in loading and unloading metal blanks into computer numerical control machine tools, repositioning semi-finished parts during the machining process and deburring afterwards. A primary motivation for robot adoption by firms in die-casting industries is the improvement of worker safety. Foundries are dangerous work environments in which robots—or workers—are subjected to intense heat and toxic fumes. Once moulded, the parts then need to be cooled, modified and inspected. Robots can control for quality in all of these steps. When the quality of the moulded parts depends on the skill of individual workers, robots offer much greater consistency. Individuals working alongside robots are also able to work much more safely and efficiently.
In addition to these industry-specific applications, palletizing is a ubiquitous application that robots can facilitate across many industries. Robots can recognize, pick up, orient and stack packages on pallets. They can also move easily between various quantities of packages of different sizes and varieties. Combined with the ability to control for quality, robots can efficiently place items in packages and seal and label them with machine-readable codes. This not only increases efficiency and precision, but also reduces injuries associated with palletizing large objects.
3.3 Measures
Below are the measures that were used in the main baseline tests:
Robot investment: A measure of robot capital stock was created by using the data that capture imports of robotics hardware and adding all robot purchases by each firm recorded in each year. To adjust the robot capital stock measure for economic depreciation, a useful life of 12 years was assumed based on IFR guidance.
Employee count, hiring and departures: To measure the total number of employees within the firm, the total count of employees provided in the NALMF data for each firm-year was used. This count was obtained from payroll deduction remittance forms submitted by all Canadian firms to the Canada Revenue Agency. Counts of managerial and non-managerial employees were recorded as responses in each year of the WES employer survey. The total number of new employee hires and departures was also recorded for each year of the survey data for both managerial and non-managerial employees. Non-managerial employee headcount was also reported by skill type (i.e., middle-skilled, low-skilled and high-skilled).
Strategic importance of labour cost reductions and quality improvements: To measure the strategic importance of labour cost reductions and quality improvements to the firm, a section of the WES employer survey asking respondents to “please rate the following factors with respect to their relative importance in your workplace general business strategy” for the years 2001, 2003 and 2005 was used. Respondents were asked to choose the importance of each factor on a Likert scale with the following possible responses: (1) not applicable, (2) not important, (3) slightly important, (4) important, (5) very important and (6) crucial. For this study, the factors of reducing labour costs and improving product and service quality were considered separately for analysis. For the measure of strategic priority of each factor, the values of (2) on the Likert scale were redefined to be equal to (1), and the scale was reset to ascend from 1 to 5, as an increase from the original (1) to (2) and vice versa does not clearly capture the changes in strategic priority this study aims to measure.
Decision-making authority for training and choice of production technology: The WES employer survey data contain detailed information on decision-making authority for tasks in different layers of the organizational hierarchy. This information was drawn from survey questions similar to those used by Bresnahan, Brynjolfsson and Hitt (2002), and Bloom et al. (2014) to measure worker autonomy. The survey asked “who normally makes decisions with respect to the following activities?” For this study, the activities of training and choice of production technology were considered, as they are directly relevant to the firm’s investments in human capital and use of robotics for productivity. For the 2003 and 2005 waves of the survey, survey respondents were given the following five possible responses to the question on who makes decisions: (1) non-managerial employees, (2) work supervisors, (3) senior managers, (4) individuals or groups outside the workplace (typically corporate headquarters for multi-establishment firms), and (5) business owners. To create distinct categories that correspond to hierarchical levels within organizations, three dummy variables were used. Each variable was equal to 1 if decision-making authority over the task was assigned to (1) non-managerial employees, (2) work supervisors or senior managers (to capture managerial employees), or (3) business owners or corporate headquarters.
Supervisor span of control: To capture supervisor span of control, the WES employee survey asked individual respondents whether they “supervise the work of employees on a day-to-day basis,” and, if so, to report the total number of employees who either report to them directly or who report to their subordinates. In this study, this total count was used as a measure of supervisor span of control, and only managers who were not promoted during the two-year period they were followed in the data were considered.
Work schedule unpredictability: To assess the unpredictability of employees’ work schedules, the WES employee survey asked respondents “how far in advance do you know your weekly hours of work?,” with the following possible responses: (1) always known, (2) more than one month (more than 31 days), (3) one month (22 to 31 days), (4) 3 weeks (15 to 21 days), (5) 2 weeks (8 to 14 days), (6) 1 to 7 days and (7) less than 1 day. For the main measure of work schedule unpredictability, the numerical value associated with each response was used, with increasing values denoting a shorter time period in which employees know their work schedule in advance.
Controls: A number of control variables were also used in this analysis. In all NALMF and WES employer sample specifications, organization fixed effects were included to address concerns of unobserved heterogeneity across firms and year fixed effects to control for aggregate shocks and trends. In the WES employee sample regressions, models were also estimated including individual employee fixed effects. This study controlled for organization size, which was measured by logged total assets in the NALMF sample, logged total revenues in the WES employer sample and logged total employees in the WES employee sample. A dummy variable control for firms with multiple business units in the NALMF sample and for organizations that were part of a multi-establishment firm in the WES employer sample was included. In the WES employer sample analysis, separate dummy variables were used to control for business establishments with an organized union or that implemented outsourcing as an organizational change.
4 Empirical strategy
A primary concern in estimating the effect of robotics is that robot adoption is unlikely to be random, which could potentially bias the coefficient estimates. This issue is addressed in two ways in addition to the robustness tests conducted.
First, for the total employment regression (using the NALMF sample), robot investment is instrumented for using the percentage of workers in each four-digit NAICS code in occupations with high manual dexterity and low verbal ability in 1995 multiplied by the inverse of the median price per robot in Canada for each year. Measures of occupation-level manual dexterity and verbal ability were obtained from the Career Handbook 2003, a dataset created by Employment and Social Development Canada, which contains ratings of the level of manual dexterity and verbal ability associated with over 920 distinct occupations on a four-point scale. High and low levels were defined as the top and bottom two points on the scale, respectively. The median price per robot for each year in Canada was calculated from the import data provided by the CBSA . The percentage of workers in each four-digit NAICS code in occupations with high manual dexterity and low verbal ability in 1995 provides a cross-sectional measure of industries that have a higher proportion of workers who may engage in activities that more closely match the capabilities of robots, which is multiplied by the inverse median robot price in Canada to create a time-varying instrumental variable. As robot prices decrease over time, industries with a higher percentage of workers doing work similar to the capabilities of robots are presumably more likely to adopt them. This is used as an instrument to argue that both cross-sectional industry employment composition in 1995 and the national median price of robots serve as plausibly exogenous predictors of firm-level robot adoption.
Second, coarsened exact matching (Iacus, King and Porro 2012) was used to match robot-adopting organizations with non-robot adopting organizations on key observables, and the estimation of the main regressions was repeated on matched samples for comparison. For the NALMF sample, robot-adopting firms in the sample were matched to non-robot adopting firms by industry (measured by four-digit NAICS code), year, province, whether the firm is a multi-unit enterprise, total assets, firm age, average annual earnings of the firm’s employees and capital stock. Matching was done exactly by industry, year, province and multi-unit status, with coarsening allowed for the other variables. For the WES sample, matching was done exactly by industry, year and province, with coarsening allowed for total revenue, age of the organization, average annual employee earnings and capital stock.
5 Results
5.1 Main findings
Firms are adopting robots to increase productivity (see Appendix Table A1). However, that does not appear to come at the expense of total employment. Results of the baseline tests of the relationship between robot investments and total employment are presented in Table 1, columns 1 and 2, for both the full and matched samples created using coarsened exact matching. As columns 1 and 2 show, the coefficient for the measure of robot investment is positive and statistically significant, predicting an increase in total employment and supporting Hypothesis H1a. Column 3 presents the results from the instrumental variable estimation, which are directionally consistent with both columns 1 and 2 and are very similar in magnitude to the matched sample results in Column 2. For both the matched sample and instrumental variable estimations, a 1% increase in robot investment predicts a roughly 0.015% increase in total employment within the firm. Considering robot capital represents only 0.05% of the factor share, this is a substantial effect and suggests that there are complementary firm practices associated with robots. As an additional step, the same regression shown in Column 1 was estimated, but the robot investment measure was replaced with a series of time-indexed dummy variables for the years before and after robot adoption. The dummy variable coefficients were plotted graphically in Chart 1. Prior to robot adoption, there was no evidence of differences in total employment trends with non-robot adopting firms, but an increase in total employment occurred beginning in the first year of robot adoption. The results of the relationship between robot investments and non-managerial employment by different skill types are shown in Table 1, columns 4 to 9. As columns 4 and 5 show, there is consistent evidence of a negative and statistically significant relationship with middle-skilled employment, which supports Hypothesis H2. There is also evidence of a positive and statistically significant relationship for both low-skilled (columns 6 and 7) and high-skilled (columns 8 and 9) employment, which supports hypotheses 3 and 4.
Table 1-1
Total employment and non-managerial employment by skill type regressions — Model specifications
Table summary
This table displays the results of Total employment and non-managerial employment by skill type regressions — Model specifications Model 1, Model 2, Model 3, Model 4, Model 5, Model 6, Model 7, Model 8 and Model 9 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Regression type Fixed effects Fixed effects Two-stage least squares Fixed effects Fixed effects Fixed effects Fixed effects Fixed effects Fixed effects Dataset NALMF NALMF NALMF WES employer WES employer WES employer WES employer WES employer WES employer Sample Full Matched Full Full Matched Full Matched Full Matched Dependent variable ln(Total employees) ln(Total employees) ln(Total employees) ln(Total middle-skilled) ln(Total middle-skilled) ln(Total low-skilled production) ln(Total low-skilled production) ln(Total high-skilled) ln(Total high-skilled)
Data table for Chart 1 Data table for Chart 1
Table summary
This table displays the results of Data table for Chart 1. The information is grouped by Years before/after robot adoption (appearing as row headers), Change in employees, calculated using index units of measure (appearing as column headers). Years before/after robot adoption Change in employees index -5 1.08 -4 1.06 -3 1.07 -2 1.07 -1 1.06 0 1.17 1 1.22 2 1.21 3 1.23 4 1.21 5 1.21
The results for the tests on the relationship between robot investment and managerial and total non-managerial employment are shown in Table 2, again presenting both full and matched sample results. In columns 1 and 2, there is evidence of a negative and statistically significant relationship between robot adoption and managerial employment. Similar to the exercise done in Chart 1, the same regression shown in Column 1 was estimated, but the robot investment measure was replaced with a series of time-indexed dummy variables for the years before and after robot adoption, and the coefficients were plotted graphically in Chart 2. Prior to robot adoption, there was no evidence of differences in total managerial employment with non-robot adopting organizations, but a substantial decrease in managerial employment occurred beginning in the first year of robot adoption. Table 3 shows how robot investment may predict the hiring and departures of managerial and non-managerial employees. Robot adoption predicts a decrease in the hiring of new managers (columns 1 and 2), but an increase in the number of managerial departures (columns 3 and 4), suggesting that both contribute to the change in managerial headcount.
Table 2-1
Managerial and non-managerial employment, hiring, and departure regressions — Model specifications
Table summary
This table displays the results of Managerial and non-managerial employment Model 1, Model 2, Model 3 and Model 4 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Regression type Fixed effects Fixed effects Fixed effects Fixed effects Dataset WES employer WES employer WES employer WES employer Sample Full Matched Full Matched Dependent variable ln(Total managers) ln(Total managers) ln(Total
non-managerial employees) ln(Total
non-managerial employees)
Table 3-1
Managerial and non-managerial hiring, and departure regressions — Model specifications
Table summary
This table displays the results of Managerial and non-managerial hiring Model 1, Model 2, Model 3, Model 4, Model 5, Model 6, Model 7 and Model 8 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Regression type Fixed effects Fixed effects Fixed effects Fixed effects Fixed effects Fixed effects Fixed effects Fixed effects Dataset WES employer WES employer WES employer WES employer WES employer WES employer WES employer WES employer Sample Full Matched Full Matched Full Matched Full Matched Dependent variable ln(Total
managerial hires) ln(Total
managerial hires) ln(Total
managerial hires) ln(Total
managerial hires) ln(Total
non-managerial hires) ln(Total
non-managerial hires) ln(Total
non-managerial departures) ln(Total
non-managerial departures)
Data table for Chart 2 Data table for Chart 2
Table summary
This table displays the results of Data table for Chart 2 Non-robot and Robot adopters, calculated using index units of measure (appearing as column headers). Non-robot Robot adopters index 2001 1.00 1.00 2002 0.90 0.69 2003 1.00 0.53 2004 0.96 0.50 2005 0.95 0.50 2006 0.95 0.53
As additional confirmation, a test was conducted to determine whether total employment increases can be explained by an increase in total non-managerial employment. The results of this test are shown in Table 2, columns 3 and 4. If the total employment or managerial employment results are attributable to measurement error in either variable, it is unlikely that a corresponding change in non-managerial employment would be observed. The coefficient for robot investment is positive and statistically significant, consistent with total employment increases being driven by non-managerial employees. In Table 3, columns 5 to 8 examine whether these results can be explained by changes in hiring or turnover for non-managerial employees. The coefficient for robot investment is positive and significant across all specifications, suggesting that investments in robotics increase both non-managerial hiring (columns 5 and 6) and non-managerial departures (columns 7 and 8). While both hiring and turnover increase, the net effect of the two (Table 2, columns 3 and 4) ultimately predicts a net gain in total employment for non-managerial employees. Increases in hiring and departures for non-managerial employees also suggest a compositional change in the workforce, consistent with the findings in Table 1 that show a decline in middle-skilled workers and an increase in low-skilled and high-skilled workers.
Next, the relationship between robot investments and changes in the strategic priorities of organizations was examined. These results are displayed in Table 4. The pattern of employment changes attributable to robot adoption—especially the decrease in managerial employment—may be related to firms’ need to reduce labour costs. If true, these results may reflect a reverse causality where firms that focus on reducing the number of costly managers choose to adopt robots. As columns 1 and 2 show, the coefficient for robot investment is not statistically significant, providing no evidence that robot-purchasing firms are motivated by a desire to reduce labour costs. Columns 3 and 4 show a positive and significant coefficient for robot investment with respect to the strategic importance of improving product and service quality. Overall, the results suggest that robot investments are more likely to be motivated by a desire to improve the quality of production output, as opposed to a desire to improve efficiency through labour cost reductions. This suggests that the possibility of reverse causality where firms may choose to reduce managers and subsequently adopt robots is less likely. These results also corroborate evidence from the field—especially in manufacturing—according to which robots are often used to improve consistency and reduce production variance.
Table 4-1
Strategic priority regressions — Model specifications
Table summary
This table displays the results of Strategic priority regressions — Model specifications Model 1, Model 2, Model 3 and Model 4 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Regression type Fixed effects Fixed effects Fixed effects Fixed effects Dataset WES employer WES employer WES employer WES employer Sample Full Matched Full Matched Dependent variable
(strategic importance) Reducing labor costs Reducing labor costs Improving product/
service quality Improving product/
service quality
Table 4-2
Strategic priority regressions — Regression results
Table summary
This table displays the results of Strategic priority regressions — Regression results Model 1, Model 2, Model 3 and Model 4 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 ln(Total revenues) Coefficient -0.014 0.118 0.098 0.180 Standard error 0.130 0.322 0.133 0.380 Multi-unit enterprise Coefficient -0.197 0.192 -0.198 0.629 Note * Standard error 0.121 0.347 0.173 0.374 Unionized Coefficient -0.144 -0.743 Note *** -0.336 Note * 0.093 Standard error 0.230 0.209 0.199 0.333 Outsourcing Coefficient 0.050 0.488 0.094 0.960 Standard error 0.178 0.488 0.169 0.590 ln(Robot capital stock) Coefficient 0.027 -0.001 0.108 Note *** 0.103 Note *** Standard error 0.036 0.020 0.013 0.031 Year fixed effects Yes Yes Yes Yes Organization fixed effects Yes Yes Yes Yes Observations (number) 8,906 889 8,906 889 Adjusted R-squared 0.32 0.46 0.38 0.21
5.2 Changes in organizational practices and the nature of work
This section explores whether the allocation of decision-making authority to managers within the organization changes after robot adoption. If firms are simply downsizing managers to reduce slack, a change in decision-making authority for managers remaining within the firm is not necessarily expected. Downsizing may instead suggest that the remaining managers are doing more than before and, as a result, are granted increased decision-making authority. To explore this possibility, this study looks at how robot investments predict the allocation of decision-making authority over training activities and the choice of production technology. These results are shown in tables 5 and 6. These two decisions are particularly relevant, as they pertain to human capital management within the firm. Table 5 presents the results for the allocation of authority for training decisions, with the coefficient for robot investment being positive for non-managerial employees (columns 1 and 2) and negative for managerial employees (columns 3 and 4), with no significant relationship found for business owners and corporate headquarters (columns 5 and 6). The results provide evidence of a decentralization of responsibilities for training from managerial to non-managerial employees within the firm as a response to robot adoption. Table 6 shows results for the allocation of decision-making authority over the choice of production technology, with no significant relationship found for non-managerial employees (columns 1 and 2), a negative and significant relationship for managerial employees (columns 3 and 4), and a positive and significant relationship for business owners and corporate headquarters (columns 5 and 6). In contrast with training activities, these results suggest that the choice of production technology becomes centralized upwards from managerial employees to business owners and corporate headquarters. Although the allocation of decision-making authority for all managerial tasks cannot be measured, these results suggest that the type of work managers are doing changes with robot adoption. The downsizing of managers represents not only a reduction in headcount, but also a change in their decision-making authority and the nature of tasks they perform. These results also suggest that robot adoption is also associated with fundamental changes in organizational design.
Table 5-1
Task allocation regressions, training decisions — Model specifications
Table summary
This table displays the results of Task allocation regressions Model 1, Model 2 , Model 3 , Model 4 , Model 5 and Model 6 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Regression type Fixed effects Fixed effects Fixed effects Fixed effects Fixed effects Fixed effects Dataset WES employer WES employer WES employer WES employer WES employer WES employer Sample Full Matched Full Matched Full Matched Dependent variable (training decisions) Non-managerial employees Non-managerial employees Managers Managers Business owners or corporate headquarters Business owners or corporate headquarters
Table 5-2
Task allocation regressions, training decisions — Regression results
Table summary
This table displays the results of Task allocation regressions Model 1, Model 2 , Model 3 , Model 4 , Model 5 and Model 6 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 ln(Total revenues) Coefficient -0.003 -0.022 0.003 -0.017 0.027 0.307 Standard error 0.019 0.046 0.090 0.066 0.089 0.230 Multi-unit enterprise Coefficient 0.009 -0.094 -0.021 -0.234 0.110 0.755 Note * Standard error 0.013 0.095 0.077 0.640 0.104 0.450 Unionized Coefficient -0.041 0.014 -0.070 -0.027 -0.139 0.018 Standard error 0.139 0.015 0.212 0.025 0.173 0.101 Outsourcing Coefficient 0.011 0.121 -0.019 0.066 -0.058 -0.278 Standard error 0.028 0.074 0.072 0.300 0.081 0.195 ln(Robot capital stock) Coefficient 0.074 Note *** 0.077 Note *** -0.077 Note *** -0.080 Note *** 0.003 0.012 Standard error 0.011 0.012 0.011 0.012 0.003 0.009 Year fixed effects Yes Yes Yes Yes Yes Yes Organization fixed effects Yes Yes Yes Yes Yes Yes Observations (number) 6,173 632 6,173 632 6,173 632 Adjusted R-squared 0.29 0.84 0.33 0.72 0.39 0.75
Table 6-1
Task allocation regressions, choice of production technology — Model specifications
Table summary
This table displays the results of Task allocation regressions Model 1, Model 2, Model 3 , Model 4 , Model 5 and Model 6 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Regression type Fixed effects Fixed effects Fixed effects Fixed effects Fixed effects Fixed effects Dataset WES employer WES employer WES employer WES employer WES employer WES employer Sample Full Matched Full Matched Full Matched Dependent variable (choice of production technology) Non-managerial employees Non-managerial employees Managers Managers Business owners or corporate headquarters Business owners or corporate headquarters
Table 6-2
Task allocation regressions, choice of production technology — Regression results
Table summary
This table displays the results of Task allocation regressions Model 1, Model 2, Model 3, Model 4, Model 5 and Model 6 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 ln(Total revenues) Coefficient 0.004 0.006 0.056 -0.131 -0.049 0.365 Standard error 0.008 0.033 0.072 0.100 0.075 0.262 Multi-unit enterprise Coefficient -0.007 -0.010 0.038 -0.498 0.070 0.930 Note *** Standard error 0.012 0.018 0.066 0.427 0.096 0.344 Unionized Coefficient -0.000 0.009 0.231 0.868 Note *** -0.527 Note *** -0.878 Note *** Standard error 0.004 0.009 0.189 0.092 0.181 0.070 Outsourcing Coefficient -0.010 0.024 0.038 0.212 -0.003 -0.324 Note * Standard error 0.019 0.024 0.075 0.250 0.077 0.179 ln(Robot capital stock) Coefficient -0.000 0.002 -0.069 Note *** -0.077 Note *** 0.075 Note *** 0.082 Note *** Standard error 0.000 0.001 0.015 0.012 0.013 0.017 Year fixed effects Yes Yes Yes Yes Yes Yes Organization fixed effects Yes Yes Yes Yes Yes Yes Observations (number) 6,173 632 6,173 632 6,173 632 Adjusted R-squared 0.30 0.09 0.31 0.54 0.33 0.54
To further confirm these results at the organization level and consider how the nature of work may be changing with robot adoption at the individual employee level, a test was conducted to determine whether robot adoption at the organization level predicts changes in the span of control for managerial employees, the results of which are shown in Table 7, Column 1. The coefficient for robot investment was positive and statistically significant, suggesting that robot adoption predicts increases in the span of control for managers remaining within the organization. An increase in the span of control at the individual manager level is consistent with earlier organization-level findings of a reduction in managerial headcount and an increase in non-managerial employees.
Table 7-1
Span of control and work predictability regressions — Model specifications
Table summary
This table displays the results of Span of control and work predictability regressions — Model specifications Model 1 and Model 2 (appearing as column headers). Model 1 Model 2 Regression type Fixed effects Fixed effects Dataset WES employee WES employee Dependent variable Span of control Work unpredctability
Table 7-2
Span of control and work predictability regressions — Regression results
Table summary
This table displays the results of Span of control and work predictability regressions — Regression results Model 1 and Model 2 (appearing as column headers). Model 1 Model 2 ln(Total revenues) Coefficient 22.532 Note * -0.112 Standard error 12.112 0.317 Multi-unit enterprise Coefficient 32.915 0.255 Standard error 29.069 0.270 Unionized Coefficient -6.911 0.067 Standard error 4.560 0.231 Outsourcing Coefficient -4.066 0.325 Standard error 5.147 0.229 ln(Robot capital stock) Coefficient 0.342 Note ** 0.158 Note ** Standard error 0.132 0.066 Year fixed effects Yes Yes Employee fixed effects Yes Yes Observations (number) 11,719 10,969 Adjusted R-squared 0.15 0.59
As an additional test, the potential impact of robot investments on the routine nature of work for individual employees was examined. This study used a specific definition of routine—the degree to which workers can predict their schedule in advance, corresponding with the measure used. As shown in Table 7, Column 2, there is a positive relationship between robot investment and the unpredictability of work in advance. The results are consistent with the notion that, as robots automate a larger proportion of tasks within the organization and reduce variance in the production process, human workers are left to focus on work that is less predictable in nature.
5.3 Robots and performance measurement mechanism checks
Two separate tests using measures available in the WES employer survey were conducted to determine whether robot investments affect a firm’s ability to measure performance, as proposed in the theoretical arguments of this study.
The first test examined whether robot investments increase the likelihood of improvements in performance measurement when organizational change occurs in the workplace. The WES employer survey asked whether any organizational changes occurred during the year, and organizational change was defined as a “change in the way in which work is organized within your workplace or between your workplace and others.” If any organizational changes occurred, the survey subsequently asked respondents whether the impact of the organizational change that affected the most employees increased the “ability to measure performance” in the workplace. A dummy variable equal to 1 was created if the workplace reported having made an organizational change that increased the firm’s ability to measure performance. To address sample selection concerns, a first-stage probit regression was estimated to predict the occurrence of organizational change, using the strategic priority of “reorganizing the work process” within the firm as an exogenous predictor, and including the Inverse Mills Ratio from this regression as an additional control variable. As shown in Table 8, Column 1, the coefficient for robot investment is positive and significant, suggesting that robots contribute to improved performance measurement when organizational changes are implemented.
The second test determined whether robot investments were positively related to the strategic priority of improving performance measurement within the firm. For the measure of strategic priority, the section of the WES employer survey that asked respondents to “please rate the following factors with respect to their relative importance in your workplace general business strategy,” but now considering the factor of “improving measures of performance,” was used. As the results in Table 8, columns 2 and 3, show, the coefficient for robot investment is positive and significant, suggesting that robot adoption and the strategic importance of improving measures of performance are positively related.
Table 8-1
Performance measurement regressions, Workplace and Employee Survey employer sample — Model specifications
Table summary
This table displays the results of Performance measurement regressions Model 1, Model 2 and Model 3 (appearing as column headers). Model 1 Model 2 Model 3 Regression type Fixed effects Fixed effects Fixed effects Dataset WES employer WES employer WES employer Sample Full Full Matched Dependent variable Increase in ability to
measure performance Strategic priority of improving measures of performance Strategic priority of improving measures of performance
Table 8-2
Performance measurement regressions, Workplace and Employee Survey employer sample — Regression results
Table summary
This table displays the results of Performance measurement regressions Model 1, Model 2 and Model 3 (appearing as column headers). Model 1 Model 2 Model 3 ln(Total revenues) Coefficient 0.034 0.090 -0.171 Standard error 0.047 0.141 0.258 Multi-unit enterprise Coefficient 0.027 0.167 0.356 Standard error 0.088 0.192 0.251 Unionized Coefficient -0.028 0.039 -0.523 Note *** Standard error 0.062 0.186 0.120 Outsourcing Coefficient Note ... : not applicable -0.011 0.702 Standard error Note ... : not applicable 0.142 0.582 ln(Robot capital stock) Coefficient 0.022 Note ** 0.076 Note *** 0.119 Note *** Standard error 0.011 0.014 0.024 Inverse Mills ratio Coefficient -0.140 Note ** Note ... : not applicable Note ... : not applicable Standard error 0.068 Note ... : not applicable Note ... : not applicable Organization fixed effects Yes Yes Yes Year fixed effects Yes Yes Yes Observations (number) 4,947 8,906 889 Adjusted R-squared 0.42 0.29 0.59
5.4 Robustness checks
A series of additional robustness tests were conducted to test the results of this study. The positive relationship between robot investment and total employment was robust across different industries (appendix tables A2 to A4), suggesting that the results are not the result of industry-specific factors. Additional regressions (available upon request) controlled for IT investment as a possible omitted variable, investigated whether unobserved purchases from wholesalers and resellers within Canada (instead of direct import purchases) may affect the results, controlled for general improvements in firm performance as an alternative explanation for increases in total employment, controlled for import competition from China and the United States, and implemented an applied Heckman-style correction for the choice to adopt robots. The paper’s main findings were robust to all of these additional controls.
6 Discussion and conclusion
This study uses novel data that capture investments in robotics for a population of businesses in a developed economy to provide the first firm-level evidence of the effect of robot adoption on employment and management, as well as the associated changes in organizational practices. The results suggest that robots do not affect employment within the firm uniformly. They lead to net increases in the headcount of non-managerial employees, but also decreases in the headcount of managerial employees. This is consistent with the notion that, by taking on a subset of responsibilities and activities in the production process of the firm, robots affect the demand for workers engaged in other activities within the firm. Employees whose skills have greater complementarity to robot investments are more likely to experience net gains in employment, depending on the degree to which their skills are complementary. This study found skill polarization of the non-managerial workforce, with decreases in middle-skilled employment and increases in low-skilled and high-skilled employment. This is consistent with previous findings on automation (Autor and Salomons 2018; Autor, Levy and Murnane 2003). Surprisingly, there was evidence of displacement of specific higher-skilled cognitive jobs (e.g., managers) that were previously less vulnerable to skill-biased technological change from earlier waves of technology. This reduction may be the result of both a decrease in the need for certain types of supervisory work from robot adoption and an indirect effect of the changing composition of non-managerial employees. Consistent with a decline in managerial employment and an increase in total employment, this study found that the span of control for managers also increased after robot adoption. There is also evidence that managerial work fundamentally changed after robot adoption, as the decision-making authority of managers was reduced. However, there is no evidence that job losses were caused by firms desiring to cut labour costs. In fact, there is evidence that firms adopt robots primarily to improve product and service quality.
In addition to changes in employment, the results of this study show that organizational practices change with robot adoption, as the allocation of decision-making authority for certain tasks shifts to different layers of the hierarchy and away from managers. Human resource-related decisions with respect to training were decentralized from managers to non-managerial employees, while the choice of production technology was centralized from managers to business owners and corporate headquarters. This is different from the effects of earlier generations of IT that tended to decentralize decision-making authority (Acemoglu et al. 2007). However, with robot adoption rapidly increasing in prevalence and capability, the allocation of decision-making authority and other complementary work practices will likely continue to evolve. Firms that can best match their capabilities and work practices to productive opportunities can benefit substantially from robot investments and develop potential competitive advantages. This finding highlights the need to understand the different types of complements to robots as a new technology.
Overall, the findings from organization-level data suggest that the effect of robots on labour is more nuanced than earlier research predicted and requires a deeper examination beyond the industry or region level to understand how robots are used to complement and substitute labour and how organizational practices need to evolve with the changing nature of work. While the present analysis suggests that robot adoption is associated with the use of different types of labour, the associated implication for wages is also an important question. The extent to which wages may change depends on the types of jobs that are created and eliminated. Initial evidence suggests that, although labour cost reduction is not the primary reason for which firms adopt robots, the reduction in managerial and middle-skilled employment and increase in low-skilled and high-skilled employment ultimately predict an ambiguous result for average wages. However, complementing the finding of a decline in demand for middle-skilled employment, Dauth, Findeisen, Südekum and Woessner 2018 used industry-level robot investments to examine their effect on employee wages and found that robot adoption leads to substantial wage decreases for middle-skilled workers.
Changes in employee types and skills as a result of robot adoption would also lead firms to implement complementary work practices to accommodate this skill change, similar to earlier generations of skill-biased technological change (Bresnahan, Brynjolfsson and Hitt 2002; Murnane, Levy and Autor 1999). To understand these effects, the collection of microdata, especially at the firm level, is crucial. In addition, better data on robot investment in different contexts are critical to understand whether the observed effects on employment and work practices can be generalized to other economies (Buffington, Miranda and Seamans 2018; Frank et al. 2019). While this study provides detailed firm-level evidence on robotics and shows that work practices have already evolved in response to robotic technology, future research could continue to examine how this technology affects different firms, occupations, industries and geographic regions (Felten, Raj and Seamans 2019). With rapid advances in robotics capabilities, understanding their implications is critical, as investments in robots are likely to have profound effects on both employment and organizations.
7 Appendix: The productivity and employment consequences of robots: Firm-level evidence
S1 Productivity
An additional test was conducted to determine whether investments in robotics lead to increases in firm productivity. As columns 2 to 4 in the table below show, the coefficient for robot capital stock is positive and significant, indicating that robots do in fact increase firm productivity.
Table A.1-1
Productivity regressions — Model specifications
Table summary
This table displays the results of Productivity regressions — Model specifications Model 1, Model 2, Model 3 and Model 4 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Regression type Ordinary least squares Ordinary least squares Fixed effects Levinsohn-Petrin Dependent variable ln(Total revenues) ln(Total revenues) ln(Total revenues) ln(Total revenues)
S2 Total employment regression results by industry
This section presents the results of the total employment specification for the NALMF sample (also including ordinary least squares) by industries. Overall, the results were consistent with the original baseline regressions, although the substantially smaller sample size and lower prevalence of robot adoption reduced statistical power in some cases.
Table A.2-1
Total employment by industry — Model specifications
Table summary
This table displays the results of Total employment by industry — Model specifications Model 1, Model 2, Model 3, Model 4, Model 5, Model 6, Model 7 and Model 8 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Regression type Ordinary least squares Fixed effects Ordinary least squares Fixed effects Ordinary least squares Fixed effects Ordinary least squares Fixed effects Industry Automotive Automotive Petroleum and plastics Petroleum and plastics Minerals and metals Minerals and metals Machinery manufacturing Machinery manufacturing Dependent variable ln(Total employees) ln(Total employees) ln(Total employees) ln(Total employees) ln(Total employees) ln(Total employees) ln(Total employees) ln(Total employees)
Table A.3-1
Total employment by industry — Model specifications
Table summary
This table displays the results of Total employment by industry — Model specifications Model 1, Model 2 , Model 3, Model 4 , Model 5, Model 6, Model 7 and Model 8 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Regression type Ordinary least squares Fixed effects Ordinary least squares Fixed effects Ordinary least squares Fixed effects Ordinary least squares Fixed effects Industry Computer and electronic manufacturing Computer and electronic manufacturing Other manufacturing Other manufacturing Healthcare Healthcare Scientific research services Scientific research services Dependent variable ln(Total employees) ln(Total employees) ln(Total employees) ln(Total employees) ln(Total employees) ln(Total employees) ln(Total employees) ln(Total employees)
Table A.4-1
Total employment by industry — Model specifications
Table summary
This table displays the results of Total employment by industry — Model specifications Model 1 , Model 2 , Model 3 and Model 4 (appearing as column headers). Model 1 Model 2 Model 3 Model 4 Regression type Ordinary least squares Fixed effects Ordinary least squares Fixed effects Industry Administrative support, waste management services Administrative support, waste management services Other services Other services Dependent variable ln(Total employees) ln(Total employees) ln(Total employees) ln(Total employees)
References
Acemoglu, D., P. Aghion, C. Lelarge, J. Van Reenen, and F. Zilibotti. 2007. “Technology, information, and the decentralization of the firm.” The Quarterly Journal of Economics 122 (4): 1759–1799.
Acemoglu, D., and P. Restrepo. 2018. Artificial Intelligence, Automation and Work. NBER Working Paper Series, no. 24196. Cambridge, Massachusetts: National Bureau of Economic Research.
Acemoglu, D., and P. Restrepo. 2020. “Robots and jobs: Evidence from US labor markets.” Journal of Political Economy 128 (6): 2188–2244.
Agrawal, A., J. Gans, and A. Goldfarb. 2018. Prediction Machines: The Simple Economics of Artificial Intelligence. Boston: Harvard Business Review Press.
Aral, S., E. Brynjolfsson, and L. Wu. 2012. “Three-way complementarities: Performance pay, human resource analytics, and information technology.” Management Science 58 (5): 913–931.
Arntz, M., T. Gregory, and U. Zierahn. 2016. The Risk of Automation for Jobs in OECD Countries. OECD Social, Employment and Migration Working Papers, no. 189. Paris: OECD Publishing.
Autor, D.H., L.F. Katz, and M.S. Kearney. 2006. “The polarization of the US labor market.” American Economic Review 96 (2): 189–194.
Autor, D.H., F. Levy, and R.J. Murnane. 2003. “The skill content of recent technological change: An empirical exploration.” The Quarterly Journal of Economics 118 (4): 1279–1333.
Autor, D.H., and A. Salomons. 2017. Robocalypse Now: Does Productivity Growth Threaten Employment? Paper presented at the National Bureau of Economic Research’s Conference on Artificial Intelligence. Toronto, September 13-14, 2017.
Autor, D.H., and A. Salomons. 2018. Is Automation Labor-displacing? Productivity Growth, Employment, and the Labor Share. NBER Working Paper Series, no. 24871. Cambridge, Massachusetts: National Bureau of Economic Research.
Bell, G.D. 1967. “Determinants of span of control.” American Journal of Sociology 73 (1): 100–109.
Bepko, R.J., Jr., J.R. Moore, and J.R. Coleman. 2009. “Implementation of a pharmacy automation system (robotics) to ensure medication safety at Norwalk hospital.” Quality Management in Healthcare 18 (2): 103–114.
Bessen, J.E., M. Goos, A. Salomons, and W. Van den Berge. 2019. Automatic Reaction—What Happens to Workers at Firms that Automate? Boston University School of Law, Law and Economics Research Paper.
Bidwell, M. J. 2013. What happened to long-term employment? The role of worker power and environmental turbulence in explaining declines in worker tenure. Organization Science 24 (4): 1061–1082.
Bloom, N., L. Garicano, R. Sadun, and J. Van Reenen. 2014. “The distinct effects of information technology and communication technology on firm organization.” Management Science 60 (12): 2859–2885.
Bresnahan, T.F., E. Brynjolfsson, and L.M. Hitt. 2002. “Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence.” The Quarterly Journal of Economics 117 (1): 339–376.
Bresnahan, T.F., and M. Trajtenberg. 1995. “General purpose technologies ‘Engines of growth’?” Journal of Econometrics 65 (1): 83–108.
Brynjolfsson, E., and L.M. Hitt. 1996. “Paradox lost? Firm-level evidence on the returns to information systems spending.” Management Science 42 (4): 541–558.
Brynjolfsson, E., L.M. Hitt, and S. Yang. 2002. “Intangible assets: Computers and organizational capital.” Brookings Papers on Economic Activity 2002 (1): 137–181.
Brynjolfsson, E., and A. McAfee. 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton & Company.
Brynjolfsson, E., and T. Mitchell. 2017. “What can machine learning do? Workforce implications.” Science 358 (6370): 1530–1534.
Brynjolfsson, E., D. Rock, and C. Syverson. 2018. Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. NBER Working Paper Series, no. 24001. Cambridge, Massachusetts: National Bureau of Economic Research.
Buffington, C., J. Miranda, and R. Seamans. 2018. Development of Survey Questions on Robotics Expenditures and Use in US Manufacturing Establishments. Working Paper 18–44, Center for Economic Studies, U.S. Census Bureau.
Card, D., and J.E. DiNardo. 2002. “Skill-biased technological change and rising wage inequality: Some problems and puzzles.” Journal of Labor Economics 20 (4): 733–783.
Cockburn, I.M., R. Henderson, and S. Stern. 2018. The Impact of Artificial Intelligence on Innovation. NBER Working Paper Series, no. 24449. Cambridge, Massachusetts: National Bureau of Economic Research.
Dauth, W., Findeisen, S., Südekum, J., and Woessner, N. 2017. German robots-the impact of industrial robots on workers. IAB-Discussion Paper, no. 30/2017. Nürnberg, Germany: Institut für Arbeitsmarkt- und Berufsforschung (IAB).
David, H., and D. Dorn. 2013. “The growth of low-skill service jobs and the polarization of the US labor market.” American Economic Review 103 (5): 1553–1597.
Dickens, W.T., L.F. Katz, K. Lang, and L.H. Summers. 1989. “Employee crime and the monitoring puzzle.” Journal of Labor Economics 7 (3): 331–347.
Dickens, W.T., L.F. Katz, K. Lang, and L.H. Summers. 1990. “Why do firms monitor workers?” In Advances in the Theory and Measurement of Unemployment, ed. Y. Weiss and G. Fishelson, chapter 6, p. 159–171. London: Palgrave Macmillan.
Dinlersoz, E., and Z. Wolf. 2018. Automation, Labor Share, and Productivity: Plant-Level Evidence from US Manufacturing. Working Paper 18–39, Center for Economic Studies, U.S. Census Bureau.
Dixon, J. 2020. How to Build a Robots! Database. Analytical Studies: Methods and References, no. 028. Statistics Canada Catalogue no. 11-633-X. Ottawa: Statistics Canada.
Dustmann, C., J. Ludsteck, and U. Schönberg. 2009. “Revisiting the German wage structure.” The Quarterly Journal of Economics 124 (2): 843–881.
Eisenhardt, K.M. 1989. “Agency theory: An assessment and review.” Academy of Management Review 14 (1): 57–74.
Felten, E., M. Raj, and R. Seamans. 2019. The Occupational Impact of Artificial Intelligence: Labor, Skills, and Polarization. New York: NYU Stern School of Business.
Ford, M. 2015. Rise of the Robots: Technology and the Threat of a Jobless Future. New York: Basic Books.
Frank, M.R., D. Autor, J.E. Bessen, E. Brynjolfsson, M. Cebrian, D.J. Deming, M. Feldman, M. Groh, J. Lobo, and E. Moro. 2019. “Toward understanding the impact of artificial intelligence on labor.” Proceedings of the National Academy of Sciences 116 (14): 6531–6539.
Frey, C.B., and M.A. Osborne. 2017. “The future of employment: How susceptible are jobs to computerisation?” Technological Forecasting and Social Change 114: 254–280.
Gombolay, M., X.J. Yang, B. Hayes, N. Seo, Z. Liu, S. Wadhwania, T. Yu, N. Shah, T. Golen, and J. Shah. 2018. “Robotic assistance in the coordination of patient care.” The International Journal of Robotics Research 37 (10): 1300–1316.
Graetz, G., and G. Michaels. 2018. “Robots at work.” Review of Economics and Statistics 100 (5): 753–768.
Hales, C. 1986. “What do managers do? A critical review of the evidence.” Journal of Management Studies 23 (1): 88–115.
Hales, C. 1999. “Why do managers do what they do? Reconciling evidence and theory in accounts of managerial work.” British Journal of Management 10 (4): 335–350.
Hammer, M. 1990. “Reengineering work: Don’t automate, obliterate.” Harvard Business Review 68 (4): 104–112.
Hawkins, A.J. 2018. “Tesla relied on too many robots to build the Model 3, Elon Musk says.” THE VERGE,April 13, 2018. Available at: https://www.theverge.com/2018/4/13/17234296/tesla-model-3-robots-production-hell-elon-musk.
Helper, S., J.P. MacDuffie, and C. Sabel. 2000. “Pragmatic collaborations: Advancing knowledge while controlling opportunism.” Industrial and Corporate Change 9 (3): 443–488.
Helper, S., and R. Henderson. 2014. “Management practices, relational contracts, and the decline of General Motors.” Journal of Economic Perspectives 28 (1): 49–72.
Hong, B., L. Kueng, and M.J. Yang. 2019. “Complementarity of performance pay and task allocation.” Management Science 65 (11): 5152–5170.
Huselid, M.A., and B.E. Becker. 1997. “The impact of high performance work systems, implementation effectiveness, and alignment with strategy on shareholder wealth.” In Academy of Management Proceedings, ed. G. Atinc, vol. 1997, no. 1, p. 144–148. Briarcliff Manor, New York: Academy of Management.
Iacus, S.M., G. King, and G. Porro. 2012. “Causal inference without balance checking: Coarsened exact matching.” Political analysis 20 (1): 1–24.
Ichniowski, C., K. Shaw, and G. Prennushi. 1997. “The effects of human resource practices on manufacturing performance: A study of steel finishing lines.” American Economic Review 87 (3): 291–313.
Jensen, M.C., and W.H. Meckling. 1976. “Agency costs and the theory of the firm.” Journal of Financial Economics 3 (4): 305–360.
Kenny, M., and R. Florida. 1993. Beyond Mass Production: The Japanese System and its Transfer to the US. New York: Oxford University Press.
Koch, M., I. Manuylov, and M. Smolka. 2019. Robots and Firms. CESifo Working Paper, no. 7608. Munich, Germany: CESifo.
Kolbjørnsrud, V., R. Amico, and R.J. Thomas. 2016. “How artificial intelligence will redefine management.” Harvard Business Review 2: 1–6.
MacDuffie, J.P. 1997. “The road to ‘root cause’: Shop-floor problem-solving at three auto assembly plants.” Management Science 43 (4): 479–502.
Malone, T.W. 2003. “Is empowerment just a fad? Control, decision making, and IT .” In Inventing the Organizations of the 21st Century, ed. T.W. Malone, R. Laubacher and M.S. Scott Morton, chapter 3, p. 49–69. Cambridge, Massachusetts: The MIT Press.
Malone, T.W. 2004. The Future of Work: How the New Order of Business Will Shape Your Organization, Your Management Style and Your Life. AudioTech Business Book Summaries, Incorporated. Oak Brook, Illinois: AudioTech, Inc.
Mann, K., and L. Püttmann. 2017. “Benign Effects of Automation: New Evidence from Patent Texts.” Unpublished manuscript.
Manyika, J., M. Chui, Miremadi, M., Bughin, J., George, K., Willmott, P., and M. Dewhurst. 2017. A Future that Works: Automation, Employment, and Productivity. McKinsey Global Institute. No place: McKinsey and Company.
McAfee, A., and E. Brynjolfsson. 2017. Machine, Platform, Crowd: Harnessing our Digital Future. New York: W.W. Norton & Company.
Meyer, M.W. 1968. “Expertness and the span of control.” American Sociological Review 33 (6): 944–951.
Mintzberg, H. 1973. The Nature of Managerial Work. New York: Harper & Row.
Mintzberg, H. 1980. “Structure in 5’s: A synthesis of the research on organization design.” Management Science 26 (3): 322–341.
Mintzberg, H. 2013. Simply Managing: What Managers Do—And Can Do Better. San Francisco: Berrett-Koehler Publishers.
Murnane, R.J., F. Levy, and D. Autor. 1999. Technical Change, Computers and Skill Demands: Evidence from the Back Office Operations of a Large Bank. Manuscript.
Parker, M., and J. Slaughter. 1988. “Management by stress.” Technology Review 91 (7): 37–44.
Perrow, C. 1967. “A framework for the comparative analysis of organizations.” American Sociological Review 32 (2): 194–208.
Puranam, P., O. Alexy, and M. Reitzig. 2014. “What's “new” about new forms of organizing?” Academy of Management Review 39 (2): 162–180.
Raj, M., and R. Seamans. 2019. “Primer on artificial intelligence and robotics.” Journal of Organization Design 8 (1): 1–14.
Scott, E.D., K.C. O’Shaughnessy, and P. Cappelli. 1994. Management Jobs in the Insurance Industry: Organizational Deskilling and Rising Pay Inequity. Wharton Financial Institutions Center, Wharton School of the University of Pennsylvania.
Scully, M.A. 2000. “Manage your own employability: Meritocracy and the legitimation of inequality in internal labor markets and beyond.” Relational Wealth: The Advantages of Stability in a Changing Economy ed. C.R. Leana and D.M. Rousseau, chapter 11, p. 199–214. Oxford: Oxford University Press.
Simon, H.A. 1946. “The proverbs of administration.” Public administration review 6 (1): 53–67.
Syverson, C. 2004. “Product substitutability and productivity dispersion.” Review of Economics and Statistics 86 (2): 534–550.
Taylor, F.W. 1911. Shop Management. No place: Harber & Bothers.
Verl, A. 2019. Managing Mass Customisation with Software-defined Manufacturing. Stuttgart, Germany: International Federation of Robotics, University of Stuttgart.
Wu, L. 2013. “Social network effects on productivity and job security: Evidence from the adoption of a social networking tool.” Information Systems Research 24 (1): 30–51.
Zammuto, R.F., T.L. Griffith, A. Majchrzak, D.J. Dougherty, and S. Faraj. 2007. “Information technology and the changing fabric of organization.” Organization science 18 (5): 749–762.
| 2020-11-02T00:00:00 |
https://www150.statcan.gc.ca/n1/pub/11f0019m/11f0019m2020017-eng.htm
|
[
{
"date": "2020/11/02",
"position": 91,
"query": "robotics job displacement"
}
] |
|
Artificial Intelligence and Its Impact on Jobs - Newsroom
|
Artificial Intelligence and Its Impact on Jobs - Newsroom
|
https://news.stthomas.edu
|
[
"Emilie Dozer",
"Manjeet Rege",
"Phd",
"Dan Yarmoluk Ms In Data Science",
"More Manjeet Rege"
] |
As a McKinsey report forecasted 800 million global workers could be replaced by robots by 2030, they further stated that blue-collar jobs, such ...
|
Everywhere you turn today is some unbelievable technological advancement on a variety of fronts. In our everyday lives, we hear or experience things about autonomous vehicles, warehouse robots, chatbots, Alexa, Siri, Uber, automated email responses, robotic surgeries, Netflix recommendation systems, smart factories, smart buildings and search retargeting. Technology giants are becoming the most valuable companies on the planet, and we see our lives shifting from what we thought was kids and their smartphone addictions and gaming to encroaching on our daily lives across the board. They are typically revolving around a variety of enabling technology layers, namely, cloud computing, computational systems, networks and sensors, robotics, material sciences, digital manufacturing and artificial intelligence. However, at the center of it, is AI that permeates many of the other advances in some shape or form in creating intelligent systems on top of advances in core products or technologies.
“Computers, intelligent machines and robots seem like the workforce of the future. And as more and more jobs are replaced by technology, people will have less work to do and ultimately will be sustained by payments from the government,” predicts Elon Musk, the cofounder and CEO of Tesla. This is a scary proposition in some sense, in that what will we do if all the work is done by AI or robots? Isn’t life tough enough? Don’t we have enough economic disparity and can barely make ends meet today? To add insult to injury, many of the analyses seem to center on displacing the low wage workers. As if they didn’t have enough disadvantages already, their entire economic class will be wiped out is the feeling we get from the news cycle. This is evidenced by robotic warehouses and chatbots or automated customer service and we can really feel the changes all around us.
Innovation and technology are certainly changing; skills and jobs as we know them today will need to change. Our frame of reference is being disrupted like never before, the guideposts and rules are changing, and this causes discomfort, uncertainty and worry. How can we chart the course if we are uncertain that the traditional methods (hard work, educational degrees, etc.) do not necessarily guarantee a certain quality of life? News flash, it is changing rapidly and therefore uncertain. We need to all become comfortable with being uncomfortable, with adapting to change, continuing education and reskilling. Some reports predict that millennials will change their jobs 17 times, but that might be a low number when you really factor in the gig economy.
According to various reports, the warnings suggest that AI could lead to the loss of tens of millions of jobs. It begs the question, when or what is the time horizon of the adoption of AI and the job loss a reality? Many reports suggest of job displacement or the very nature of jobs shifting. Automation and technology have shifted work in pursuit of lowering costs, increasing efficiency and production. The automobile “displaced” work that was done via horse and buggy, electricity or fluorescent lighting displaced gas lamps and gas replaced coal in many instances. Jobs have been displaced in the past, but in today’s case the rate at which these exponential technologies are growing is moving faster than the rate of human adaptation. That speed at which we are experiencing technological and societal change is only the beginning as many futurists, such as Peter Diamandis, prophesize.
Bloomberg reports that “more than 120 million workers globally will need retraining in the next three years due to artificial intelligence’s impact on jobs, according to an IBM survey.” That report and interpretations of it seem to suggest that adoption of AI may result in massive job losses and requires massive retraining. This paints a doomsday scenario, creates uncertainty and worry. The interpretation is AI equals job loss; we would argue that the interpretation should be interpreted as AI and technology advancements will require job retraining and job reskilling. The reports also seem to suggest that our educational system is preparing for jobs of today, when we see that the jobs of the future will be quite different, with different resources and tools at our disposal. This further creates panic in that we see nothing but chaos and the inability to control our destiny for ourselves and our children.
The report was MIT-IBM Watson Lab research that shed light on the reorganization of tasks within occupations by analyzing 170 million online job postings in the U.S. between 2010-17. There is no question that AI and related technologies will affect all jobs; what the report did shed light on was the fact how the nature of work is changing, how tasks are changing and tried to link the implications for employment and wages. There were some key findings in the report, that tasks are shifting between people and machines (or AI), but the change has been small (Figure 1).
Automation or AI is disappearing from job requirements, shifting in the way work gets done; as technology reduces the cost of some tasks, the value of remaining tasks increases, particularly soft skills such as creativity, common sense, judgment and communication skills.
This type of analysis is what experts refer to as the “future of work” and how the work is shifting, job requirements are changing, and automation and AI are displacing certain sectors of the labor market. It also informs policymakers of where to focus attention and resources in order to best prepare for the future. Many of the takeaways and political talk seem to focus on “the vulnerable will be the most vulnerable” as a key takeaway, and that better-educated workers will fare out alright as AI/automation spreads. As a McKinsey report forecasted 800 million global workers could be replaced by robots by 2030, they further stated that blue-collar jobs, such as machine operating, warehouse workers and fast food are particularly susceptible to disruption.
But a new study published by the Brookings institution states that might not be the case. The report looked at thousands of AI patents and job descriptions and that educated, well-paid workers may be affected even more by the spread of AI. Most consider robotics and software as impacting the physical and routine work of traditionally blue-collar jobs. The report goes on to state that workers with a bachelor’s degree, for example, would be exposed to AI over five times more than those with only a high school degree. That is due to the fact AI is very strong at completing tasks that require planning, learning, reasoning, problem-solving and predicting – most of which are skills we think of as white-collar jobs.
This analysis of patent data and tasks and exposure to risk on a sampling of various occupations in Table 2.
AI’s impact on the workplace, the future of work, sectors of the economy and global domination are hard to assess. Most forecasts are rooted in well-established, well-understood technologies such as robotics and extrapolated across of a range of tasks, functions and jobs. The nature of AI being new and poorly understood, nonetheless unsuccessfully implemented across all industries, makes it even more difficult to understand. There is no shared agreement on the tasks, nor the expected impacts on the workforce or economy. The best scholars concede to the limitations of their economic or forecasts for the future. What we do know is that the nature of work will change, as it has through the centuries with innovation. We do know that disruption will occur in sectors of the economy, and we should brace for that change and try to harness that change for good. Perhaps AI can see patterns in deadly diseases, fight climate change and explore the universe. We should be as excited as nervous about change, and try to the best of our abilities to shape our society for that coming change.
Author bios:
Manjeet Rege is an associate professor of Graduate Programs in Software and Data Science and Director of Center for Applied Artificial Intelligence at the University of St. Thomas. Dr. Rege is an author, mentor, thought leader, and a frequent public speaker on big data, machine learning and artificial intelligence technologies. He is also the co-host of the "All Things Data" podcast that brings together leading data scientists, technologists, business model experts and futurists to discuss strategies to utilize, harness and deploy data science, data-driven strategies and enable digital transformation. Apart from being engaged in research, Dr. Rege regularly consults with various organizations to provide expert guidance for building big data and AI practice, and applying innovative data science approaches. He has published in various peer-reviewed reputed venues such as IEEE Transactions on Knowledge and Data Engineering, Data Mining & Knowledge Discovery Journal, IEEE International Conference on Data Mining, and the World Wide Web Conference. He is on the editorial review board of Journal of Computer Information Systems and regularly serves on the program committees of various international conferences.
Dan Yarmoluk is an adjunct faculty at Graduate Programs in Software at the University of St. Thomas. He has been involved in analytics, embedded design and components of mobile products for over a decade. He has focused on creating and driving IoT automation, condition monitoring and predictive maintenance programs with technology, analytics and business models that intersect to drive added value and digital transformation. Industries he has served include: oil and gas, refining, chemical, precision agriculture, food, pulp and paper, mining, transportation, filtration, field services and distribution. He publishes his thoughts frequently and co-hosts a popular podcast “All Things Data” with Dr. Manjeet Rege of the University of St. Thomas.
For more thought leadership on AI, read about the global battle for AI dominance in this Tommie Experts piece.
| 2020-11-19T00:00:00 |
2020/11/19
|
https://news.stthomas.edu/artificial-intelligence-and-its-impact-on-jobs/
|
[
{
"date": "2020/11/19",
"position": 47,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 42,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 45,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 43,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 50,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 42,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 33,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 47,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 45,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 64,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 33,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 49,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 31,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 44,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 42,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 43,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 34,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 98,
"query": "AI employment"
},
{
"date": "2020/11/19",
"position": 42,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 31,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 36,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 46,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 44,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 34,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 43,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 43,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 33,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 70,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 33,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 47,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 33,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 34,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 42,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 49,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 43,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 33,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 45,
"query": "AI impact jobs"
},
{
"date": "2020/11/19",
"position": 36,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 46,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 33,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 48,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 31,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 34,
"query": "automation job displacement"
},
{
"date": "2020/11/19",
"position": 46,
"query": "robotics job displacement"
},
{
"date": "2020/11/19",
"position": 42,
"query": "AI impact jobs"
}
] |
Benefits and Challenges of Technologies to Augment Patient Care
|
Artificial Intelligence in Health Care: Benefits and Challenges of Technologies to Augment Patient Care
|
https://www.gao.gov
|
[] |
Artificial Intelligence tools show promise for improving health care. They can help predict health trajectories, recommend treatments, and automate ...
|
What GAO Found
Artificial Intelligence (AI) tools have shown promise for augmenting patient care in the following two areas:
Clinical AI tools have shown promise in predicting health trajectories of patients, recommending treatments, guiding surgical care, monitoring patients, and supporting population health management (i.e., efforts to improve the health outcomes of a community). These tools are at varying stages of maturity and adoption, but many we describe, with the exception of population health management tools, have not achieved widespread use.
have shown promise in predicting health trajectories of patients, recommending treatments, guiding surgical care, monitoring patients, and supporting population health management (i.e., efforts to improve the health outcomes of a community). These tools are at varying stages of maturity and adoption, but many we describe, with the exception of population health management tools, have not achieved widespread use. Administrative AI tools have shown promise in reducing provider burden and increasing efficiency by recording digital notes, optimizing operational processes, and automating laborious tasks. These tools are also at varying stages of maturity and adoption, ranging from emerging to widespread.
GAO identified the following challenges surrounding AI tools, which may impede their widespread adoption:
Data access. Developers experience difficulties obtaining the high-quality data needed to create effective AI tools.
Developers experience difficulties obtaining the high-quality data needed to create effective AI tools. Bias. Limitations and bias in data used to develop AI tools can reduce their safety and effectiveness for different groups of patients, leading to treatment disparities.
Limitations and bias in data used to develop AI tools can reduce their safety and effectiveness for different groups of patients, leading to treatment disparities. Scaling and integration. AI tools can be challenging to scale up and integrate into new settings because of differences among institutions and patient populations.
AI tools can be challenging to scale up and integrate into new settings because of differences among institutions and patient populations. Lack of transparency. AI tools sometimes lack transparency—in part because of the inherent difficulty of determining how some of them work, but also because of more controllable factors, such as the paucity of evaluations in clinical settings.
AI tools sometimes lack transparency—in part because of the inherent difficulty of determining how some of them work, but also because of more controllable factors, such as the paucity of evaluations in clinical settings. Privacy. As more AI systems are developed, large quantities of data will be in the hands of more people and organizations, adding to privacy risks and concerns.
As more AI systems are developed, large quantities of data will be in the hands of more people and organizations, adding to privacy risks and concerns. Uncertainty over liability. The multiplicity of parties involved in developing, deploying, and using AI tools is one of several factors that have rendered liability associated with the use of AI tools uncertain. This may slow adoption and impede innovation.
GAO developed six policy options that could help address these challenges or enhance the benefits of AI tools. The first five policy options identify possible new actions by policymakers, which include Congress, elected officials, federal agencies, state and local governments, academic and research institutions, and industry. The last is the status quo, whereby policymakers would not intervene with current efforts. See below for details of the policy options and relevant opportunities and considerations.
Policy Options to Address Challenges or Enhance Benefits of AI to Augment Patient Care
Policy Option Opportunities Considerations Collaboration (report p. 32)
Policymakers could encourage interdisciplinary collaboration between developers and health care providers. Could result in AI tools that are easier to implement and use within a providers’ existing workflow.
Could help implement tools on a larger scale.
Approaches to encourage collaboration include agencies seeking input from innovators. For example, agencies have used a challenge format to encourage the public to develop innovative technologies. May result in the creation of tools that are specific to one hospital or provider.
Providers may not have time to both collaborate and treat patients. Data Access (report p. 33)
Policymakers could develop or expand high-quality data access mechanisms. A “data commons”–a cloud based-platform where users can store, share, access, and interact with data–could be one approach.
More high-quality data could facilitate the development and testing of AI tools.
Could help developers address bias concerns by ensuring data are representative, transparent and equitable. Cybersecurity and privacy risks could increase, and threats would likely require additional precautions.
Would likely require large amounts of resources to successfully coordinate across different domains and help address interoperability issues.
Organizations with proprietary data could be reluctant to participate. Best Practices (report p. 34)
Policymakers could encourage relevant stakeholders and experts to establish best practices (such as standards) for development, implementation, and use of AI technologies. Could help providers deploy AI tools by providing guidance on data, interoperability, bias, and implementation, among other things. Could help improve scalability of AI tools by ensuring data are formatted to be interoperable.
Could address concerns about bias by encouraging wider representation and transparency. Could require consensus from many public- and private-sector stakeholders, which can be time- and resource-intensive.
Some best practices may not be widely applicable because of differences across institutions and patient populations. Interdisciplinary Education (report p. 35)
Policymakers could create opportunities for more workers to develop interdisciplinary skills. Could help providers use tools effectively.
Could be implemented in a variety of ways, including through changing academic curriculums or through grants. Employers and university leaders may have to modify their existing curriculums, potentially increasing the length of medical training. Oversight Clarity (report p. 36)
Policymakers could collaborate with relevant stakeholders to clarify appropriate oversight mechanisms. Predictable oversight could help ensure that AI tools remain safe and effective after deployment and throughout their lifecycle.
A forum consisting of relevant stakeholders could help recommend additional mechanisms to ensure appropriate oversight of AI tools. Soliciting input and coordinating among stakeholders, such as hospitals, professional organizations, and agencies, may be challenging.
Excess regulation could slow the pace of innovation. Status quo (report p. 37) Policymakers could maintain the status quo (i.e., allow current efforts to proceed without intervention). Challenges may be resolved through current efforts.
Some hospitals and providers are already using AI to augment patient care and may not need policy-based solutions to continue expanding these efforts.
Existing efforts may prove more beneficial than new options. The challenges described in this report may remain unresolved or be exacerbated. For example, fewer AI tools may be implemented at scale and disparities in use of AI tools may increase.
Source: GAO.
Why GAO did this study
The U.S. health care system is under pressure from an aging population; rising disease prevalence, including from the current pandemic; and increasing costs. New technologies, such as AI, could augment patient care in health care facilities, including outpatient and inpatient care, emergency services, and preventative care. However, the use of AI-enabled tools in health care raises a variety of ethical, legal, economic, and social concerns.
GAO was asked to conduct a technology assessment on the use of AI technologies to improve patient care, with an emphasis on foresight and policy implications. This report discusses (1) current and emerging AI tools available for augmenting patient care and their potential benefits, (2) challenges surrounding the use of these tools, and (3) policy options to address challenges or enhance benefits of the use of these tools.
GAO assessed AI tools developed for or used in health care facilities; interviewed a range of stakeholder groups including government, health care, industry, academia, and a consumer group; convened a meeting of experts in collaboration with the National Academy of Medicine; and reviewed key reports and scientific literature. GAO is identifying policy options in this report.
For more information, contact Karen L. Howard at (202) 512-6888 or [email protected].
| 2020-11-30T00:00:00 |
https://www.gao.gov/products/gao-21-7sp
|
[
{
"date": "2020/11/30",
"position": 28,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 18,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 24,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 28,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 26,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 24,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 28,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 24,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 27,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 27,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 28,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 25,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 28,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 26,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 25,
"query": "artificial intelligence healthcare"
},
{
"date": "2020/11/30",
"position": 28,
"query": "artificial intelligence healthcare"
}
] |
|
32 Business Automation Statistics for 2021 | NetSuite
|
32 Business Automation Statistics for 2021
|
https://www.netsuite.com
|
[] |
The percentage of companies that have fully automated at least one function, however, has grown more modestly, from 29% in 2018 to 31% in 2020.
|
Business process automation (BPA), where technologies like virtual agents or cognitive engines built into software take over routine tasks, makes companies more efficient and agile. In the past few years, in fact, BPA projects have taken off, driven by leaders whose organizations are on the fast track to digitization—or who just want to free their people up for more creative, high-value pursuits.
BPA is not a specific technology or finite project. Rather, it’s an ongoing process of using technology to automate manual workflows, removing humans from the picture partially or completely. Most companies start with simpler tasks, like first-line customer service or T&E (travel and expense) routing, and progress from there once employees gain comfort.
Increasingly sophisticated software workflow engines are enabling companies to automate almost any type of horizontal business process, including demand planning, revenue forecasting, marketing, ERP, CRM, customer service and HR. Likewise, more advanced AI and machine learning, big data and robotic process automation (RPA) capabilities are enabling exciting industry-specific vertical BPA projects.
Let’s look at some of the more promising areas.
Automation Market
Key stat: 31% of businesses have fully automated at least one function. —McKinsey
A 2020 global survey of business leaders from a wide cross-section of industries conducted by McKinsey & Co. found that 66% were piloting solutions to automate at least one business process, up from 57% two years earlier.
The percentage of companies that have fully automated at least one function, however, has grown more modestly, from 29% in 2018 to 31% in 2020.
Among companies that have successfully completed BPA projects, McKinsey says common threads include involving employees in training the automation systems and erring toward over-communicating: “Respondents from companies with successful efforts are seven times more likely than others to say they formally involve the communications function while implementing automation efforts, and they are more than twice as likely to say the HR function is involved.”
That makes sense, because employees worried about automation making them redundant will at best have lower morale; at worst, they may attempt to subvert the effort.
Artificial intelligence (AI) and machine learning have provided further fuel for market advances in automation, enabling commercially viable products and services that can automate a growing number of routine business processes. For example, modern SaaS (software-as-a-service) solutions offer a simplified approach to automating manual processes and workflows throughout an organization and across businesses.
The tasks companies tend to automate first are routine processes that involve repetitive functions, such as routing customer queries and purchase orders, generating reports and automating routine steps in AP (accounts payable) processing. In fact, many companies start their BPA initiatives in finance, often with accounts payable automation.
AP teams first replaced recurring, manual, paper-based functions with digital records routed for approval electronically—managers authorized to approve requisitions in a workflow received email alerts or prompts to log in to a system and review forms. Now, advances in automation and machine learning make it even easier to fully automate the approval process based on predefined rules and policies.
Read more:
McKinsey & Co.: The imperatives for automation success(opens in new tab)
McKinsey & Co.: The automation imperative(opens in new tab)
NetSuite: 10 Accounts Payable Automation Best Practices
Business Process Management vs Business Process Automation
Key stat: 62% of organizations have up to 25% of their business processes modeled, but just 2% of the organizations surveyed have all of their processes modeled. —Signavio
Business process automation (BPA) and business process management (BPM) are related but not the same. BPM is considered a business practice that involves a formalized, organizational methodology based on an established path for efficient and effective management of all processes.
The Association for Information and Image Management (AIIM) says that successfully employing BPM(opens in new tab) requires organizing around outcomes and standardizing processes. Before automating processes, BPM also improves them—a crucial step to avoid simply moving flawed routines from manual to automated executions.
Business process management was a $3.38 billion market in 2019, and Mordor Intelligence projects a CAGR of 6.26%, with sales reaching $4.78 by 2025. Mordor’s 2020 forecast also points to the impact of COVID-19, which has exposed weaknesses in many companies’ supply chains and business processes.
Although BPM is a business practice, those implementing it use specialized BPM tools(opens in new tab) to model their processes and then optimize, automate and measure them. In fact, “automation” is the operative term when it comes to recurring tasks that require some form of decision-making.
BPA is complementary to, and has interdependencies with, other forms of automation that are also taking off:
Robotic process automation (RPA) uses bots to mimic routine cognitive human tasks. The RPA market, valued at $1.4 billion in 2019, is forecast to grow at a CAGR of 40.6% between 2020 and 2027, according to Grand View Research. Digital process automation (DPA) is a relatively new variant of BPM that is more lightweight and requires less coding. In 2019, DPA was a $7.8 billion market; it’s forecast by Mordor Research to grow at a CAGR of 13%, reaching $16.12 billion by 2025.
Read more:
Signavio: The State of Business Process Management 2020
Mordor Intelligence: Business Process Management Market - Growth, Trends, And Forecasts (2020 - 2025)(opens in new tab)
Grand View Research: Robotic Process Automation Market Size, Share & Trends Analysis Report(opens in new tab)
Demand
Key stat: The supply chain management (SCM) market is expected to grow from $15.85 billion in 2019 to $37.41 billion by 2027, a CAGR of 11.2%. —Allied Market Research
The key to effective supply chain management (SCM) is demand planning, the process of accurately predicting which goods customers will order, and at what volume. Overestimating demand results in excess inventory, while underestimating leads to lost sales and dissatisfied customers.
Factors that can influence demand forecasts include weather, economic climate, tariffs, currency fluctuations and a variety of other disruptions. Some can be factored in with solid product portfolio management and forecasting, but manual estimates are laborious.
Enter modern ERP systems, which include SCM automation capabilities that deliver real-time decision-making for demand planning. Advances in AI, machine learning and predictive analytics and the use of sensors also provide much better visibility.
For example, one large logistics company added a demand forecasting framework and was able to produce 35 million forecasts based on data from 2,000 locations. A case study(opens in new tab) conducted by consulting firm Elder Research found that forecasts during the four-week study delivered a median accuracy rate of 88%.
Read more:
Allied Market Research: Supply Chain Management Market Expected to Reach $37.41 Billion by 2027(opens in new tab)
Revenue Recognition
Key stat: The global market for accounting software is forecast to grow at a CAGR of 8.02% from 2018 to 2026, increasing from $11 billion to $20.4 billion. —Fortune Business Insights
Revenue recognition automation capabilities in accounting software are designed to offload manual tasks involved in gathering and calculating when revenue is recognized. In addition to simplifying the process, automating revenue recognition cycles reduces the risk of errors and fraud, ensures compliance and speeds decision-making by providing data in near real time.
That’s about to become more important. While the Financial Accounting Standards Board (FASB) delayed the deadline for ASC 606(opens in new tab) compliance for non-public businesses until companies’ fiscal years beginning Dec. 15, 2021, new requirements are on the way. The purpose of the change, according to the FASB, is to make it easier to compare revenue recognition practices across entities, industries, jurisdictions and capital markets while bringing more useful information to financial statements by requiring improved disclosures.
Automation will make compliance easier and more accurate. No wonder the global market for accounting software is forecast to grow at a CAGR of 8.02% from 2018 to 2026, increasing from $11 billion to $20.4 billion. Companies that don’t automate will soon be at a disadvantage.
Read more:
Fortune Business Insights: Accounting Software Market Size, Share and Industry Analysis(opens in new tab)
Productivity and Time Management
Key stat: In early May 2020, U.S. employee engagement advanced to a new high of 38%. —Gallup
Improving worker productivity is a top driver for technology investments, including automation. But results have been mixed.
Overall, U.S. productivity growth(opens in new tab) clocked in at a paltry 1.4% between 2007 and 2019, according to the Bureau of Labor Statistics. In the manufacturing sector, growth has increased only 0.5% since the financial crisis, falling sharply from 4.4%.
Clearly, there are fundamental issues holding back productivity. A few areas to consider:
Gallup says that high-performing employees have three things in common: talent, high engagement and 10-plus years of longevity with their employers. Among Millennials, 43% envision leaving their jobs within two years, while only 28% see themselves staying beyond five years, according to Deloitte. By 2030, 85 million jobs could be unfilled globally because there aren’t enough skilled people, writes consultancy Korn Ferry. That could result in $8.5 trillion in unrealized annual revenues. The productivity software market, which includes office and collaboration applications, was forecast to reach nearly $62 billion in 2020, with revenue predicted to increase at a CAGR of 6.8%, reaching $85 billion by 2025, says Statista.
Automation projects can lead to increased engagement by shifting to technology the sort of rote tasks that keep people from picking up more interesting work or devoting time to skills development exercises that may boost productivity.
McKinsey estimates that, in about 60% of occupations, at least one-third of workday activities could be automated. When considering productivity and time management, not to mention payroll, tax compliance and reporting and AP, ensure automation is part of the conversation. Your employees will thank you—a recent survey of more than 6,000 knowledge workers by ServiceNow shows that that BPA boosts not only productivity but satisfaction.
And the smarter this technology gets, the higher up the work stack it’s moving.
AI & Machine Learning
Key stat: 60% of retail respondents have implementation AI, up from 35% during the prior year, making it the industry with the sharpest increase. —McKinsey
Gallup: U.S. Employee Engagement Reverts Back to Pre-COVID-19 Levels (opens in new tab) Deloitte: 2018 Deloitte Millennial Survey (opens in new tab) Korn Ferry: The $8.5 Trillion Talent Shortage (opens in new tab) Statista: Productivity Software Market Outlook (opens in new tab) McKinsey & Co.: Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages (opens in new tab) ServiceNow: Productivity depends on people (opens in new tab)
Advances in AI and machine learning are key enablers of BPA. While people tend to use the terms interchangeably, that’s incorrect.(opens in new tab)
AI is the overarching science of creating intelligent software, bots and machines that can take on the decision-making and problem-solving functions performed by humans today.
Machine learning is one of many subsets of AI but the most critical because it employs algorithms and neural networks to gather massive amounts of data, including telemetry from sensors and other endpoints, to make decisions and/or execute tasks.
Other fast-maturing forms of ML- and AI-driven automation include natural language processing (NLP), robotic process automation,(opens in new tab) virtual agents (conversational interfaces), autonomous vehicles and human-like robots. Industries that have emerged as aggressive adopters of AI include financial services, IT and cybersecurity, insurance and pharma.
Still, use of true AI in BPA is relatively low, though it has accelerated considerably in recent years, with enterprise AI adoption up 25%, according to McKinsey’s 2019 Global AI survey.
Among its key findings:
63% of those that have implemented AI say that it contributed to increased revenues. 58% embedded at least one AI element into a process or product, up from 47% in 2018. 30% incorporated AI across business units, an increase from 21%.
As the COVID-19 pandemic of 2020 unfolded, many organizations accelerated their AI implementations. Three months after the outbreak, McKinsey conducted a separate survey. Among 800 executives, half were from the United States, with the remainder hailing from seven other countries.
Since the outbreak, McKinsey found that 88% of finance and insurance executives and 76% of those in IT have accelerated their implementations of automation and artificial intelligence. These industries were already leaders in the shift to automation and digitization before the pandemic. Hence, companies in these sectors were well-positioned to accelerate their implementations.
A Deloitte survey of 1,900 companies confirms some trends around use of AI to improve operations and decision-making while reducing the time spent on mundane tasks.
Respondents ranked the Top 9 benefits AI has delivered:
Enhance products and services: 43% Optimize internal business operations: 41% Make better decisions: 34% Automate tasks, enabling employees to become more creative: 31% Optimized external processes: 31% Create new products: 28% Pursue new markets: 27% Capture and apply knowledge that is hard to otherwise attain: 26% Apply automation to reduce headcount: 24%
Read more:
McKinsey & Co.: 2019 Global AI Survey
McKinsey & Co.: What 800 executives envision for the postpandemic workforce(opens in new tab)
Deloitte: Talent and workforce effects in the age of AI
Workflow & Automation
Key stat: Digitization and a focus on streamlining business processes is accelerating demand for modern workflow automation management systems, a market forecast to increase from $4.8 billion in 2018 to more than $26 billion in 2025. —Grand View Research
Companies have used software to automate business workflows for decades, but AI allows rules engines to replace manual approvals by triggering events automatically.
Modern workflow management solutions use machine learning to improve on how companies automate such processes as approving sales discounts, authorizing employee T&E expenses and intelligently responding to customer queries. Digitization and a focus on streamlining business processes is accelerating demand for modern workflow automation management systems, which Grand View expects to show a CAGR of 27.7% through 2025.
One popular project: Bringing automation to the supply chain.
In late 2019, a report forecast that the supply chain AI market was poised to grow at a CAGR of 39.4% through 2027. But months after the COVID-19 pandemic struck, Meticulous Research raised that forecast to an even more eye-opening 45.3%, with the market reaching $21.8 billion in less than seven years.
Read more:
Grand View Research: Workflow Management System Market Size, Share & Trends Analysis(opens in new tab)
Meticulous Research: Artificial Intelligence (AI) in Supply Chain Market(opens in new tab)
Big Data
Key stat: 64.8% of businesses planned to invest more than $50 million in big data and AI initiatives in 2020, up from 39.7% in 2018. —New Vantage Partners
The key to successful BPA is the ability to capture all the data relevant to the entirety of a business process. Given he complexity of some processes, that requires the ability to parse massive amounts of structured and unstructured information—big data.
Fortunately, advances in big data processing are giving companies confidence in automated decision-making.
Big data is also the underlying engine that enables AI, which drives advanced BPA initiatives. A recent executive survey from New Vantage Partners shows that:
65% of businesses planned to invest more than $50 million in big data and AI initiatives in 2020, up from 40% in 2018 While only 38% have created data-driven organizations, 27% have successfully created “data cultures” within their companies. 91% cited people and process challenges as the largest barriers to evolving into data-driven organizations.
We suspect that’s something CFOs can relate to.
Robotic Process Automation (RPA)
Key stat: 88% of corporate controllers expect to implement RPA in 2021, though many are hesitant to use it for financial reporting. —Gartner
RPA is the automation of repeatable human tasks with software-based robots, commonly known as “bots.”
Each bot, once programmed with machine learning and rules engines, performs a task that was once executed by a human. While most of us think of customer service chatbots here, a growing horizontal market for RPA is in automating financial reporting processes.
RPA could save finance teams 25,000 hours of avoidable rework from human errors, at a cost savings of $878,000, according to research firm Gartner. Still, a study found that only 29% of chief accounting officers (CAOs) surveyed are using RPA for financial reporting.
The analyst firm, which is forecasting that the worldwide RPA market will grow 19.5% from 2019 to 2020, to nearly $2 billion, also predicts that:
90% of large organizations throughout the world will have adopted RPA in some form by 2022. Organizations will triple the capacity of their existing RPA portfolios. Half of all new RPA clients will be purchased by business managers outside of IT. Prices for RPA software will decrease 10% to 15% by the end of 2020 and 5% to 10% in 2021 and 2022.
Worldwide RPA Software Revenue (Millions of U.S. Dollars) 2019 2020 2021 Revenue ($M) 1,411.1 1,579.5 1,888.1 Growth (%) 62.93 11.94 19.53
Source: Gartner (September 2020)
That suggests that this is a great time to explore the technology. CFOs may want partner with the heads of marketing and HR for pilot tests.
Read more:
Gartner: Robotic Process Automation Can Save Finance Departments 25,000 Hours of Avoidable Work Annually(opens in new tab)
Gartner: Worldwide Robotic Process Automation Software Revenue to Reach Nearly $2 Billion in 2021
HR Automation
Key stat: 25% of companies are using AI to screen resumes or job applications. —Littler
The global market for human resources management software is on the upswing. Also known as human capital management (HCM), modern cloud-based HRMSes use analytics to model everything from compensation and benefits to employee performance and allocation of labor. Investments in HR technology will soar between 2020 and 2022, according to a report by Gallagher, an insurance brokerage, risk management and consulting firm.
More than two-thirds, 69%, of HR execs surveyed said they will expand or replace their HR systems by 2022. According to the findings:
Just 15% have holistic HR technology strategies aligned with their corporate goals. Still, 35% have implemented new HR technology with success since 2018. 29% use more than 75% of the capabilities provided in their systems.
A survey of HR professionals and C-suite executives by Littler found that, while AI is in use to screen applications, companies are not getting full value. Most, 69%, say they are not using these systems in their recruiting or hiring processes, for example.
That’s a missed opportunity. Attracting and retaining top talent, developing employees to reach their potential and automating tasks to improve the work experience were the top HR technology concerns in PwC’s 2020 HR Technology Survey, and AI can help with all of these.
It appears that companies are listening: Among the 600 HR and IT executives PwC surveyed, 74% expect to increase HR technology spending. Likewise, 72% said their core HR applications will be cloud-based by the end of 2020.
Read more:
Gallagher: 2020 HR Technology Pulse Survey U.S. Report(opens in new tab)
Littler: Annual Employer Survey 2019(opens in new tab)
PwC: HR Technology Survey 2020
Marketing Automation
Key stat: At an expected CAGR of 19%, the market marketing automation software market is forecast to reach $16.87 billion by 2025. —Mordor Intelligence
Marketers are all about adding new customers and gaining more business from existing buyers, along with establishing and maintaining brand awareness.
How that happens depends on the company and its customers. Is spending most effective on traditional advertising though various media, or is direct outreach via mail, email, web and social media the way to go?
Automation and advances in omnichannel marketing technology have enabled personalized and interactive forms of engagement, such that companies don’t need to guess, or even choose. They can take an “all of the above” tack using marketing automation software that mechanizes repetitive tasks, helps marketers customize and automate entire campaigns and provides data and results analysis.
In its report on the marketing automation software market, Mordor Intelligence adds that adopters find value in gathering leads and presenting personalized offers.
Read more:
Mordor Intelligence: Marketing Automation Software Market - Growth, Trends, Forecasts (2020 - 2025)(opens in new tab)
Improve Expense
Management
Efficiency Free Product Tour (opens in new tab)
Customer Service Automation
Key stat: By 2022, 70% of customer interactions will use machine learning technology in virtual agents, up from 15% in 2018. —Gartner
Virtual agents, also called chatbots, have evolved from a novelty to a common feature in customer service platforms.
Virtual agents allow businesses to reduce their reliance on customer service representatives and still deliver expedited support for routine inquiries. But research firm Gartner says there are still gotchas and advises companies to screen chatbot vendors carefully, ask about plans for voice-enabled bots and budget for ongoing maintenance and improvements.
Read more:
Gartner: Chatbots Will Appeal to Modern Workers
Finally, don’t think that BPA is only, or even mainly, for large companies. Smaller businesses can leverage automation in a wide spectrum of functions, from employee scheduling and expense management to call centers and the supply chain.
| 2020-12-02T00:00:00 |
https://www.netsuite.com/portal/resource/articles/business-strategy/business-automation-statistics.shtml
|
[
{
"date": "2020/12/02",
"position": 67,
"query": "job automation statistics"
},
{
"date": "2020/12/02",
"position": 65,
"query": "job automation statistics"
}
] |
|
New research shows the robots are coming for jobs—but stealthily
|
New research shows the robots are coming for jobs—but stealthily
|
https://www.economist.com
|
[] |
The economic statistics have yet to signal the arrival of a robot-powered job apocalypse. Outside of slumps, firms remain keen to hire humans, ...
|
T HE YEAR is 2021, and honestly there ought to be more robots. It was a decade ago that two scholars of technology, Erik Brynjolfsson and Andrew McAfee, published “Race Against the Machine”, an influential book that marked the start of a fierce debate between optimists and pessimists about technological change. The authors argued that exponential progress in computing was on the verge of delivering explosive advances in machine capabilities. Headline-grabbing breakthroughs in artificial intelligence ( AI ) seemed to support the idea that the robots would soon upend every workplace. Given that, on the eve of the pandemic, jobs were as plentiful as ever, you might now conclude that the warnings were overdone. But a number of new economics papers caution against complacency. The robots are indeed coming, they reckon—just a bit more slowly and stealthily than you might have expected.
| 2021-01-16T00:00:00 |
2021/01/16
|
https://www.economist.com/finance-and-economics/2021/01/16/new-research-shows-the-robots-are-coming-for-jobs-but-stealthily
|
[
{
"date": "2021/01/16",
"position": 77,
"query": "robotics job displacement"
}
] |
How are robots affecting jobs and pay? - Economics Observatory
|
How are robots affecting jobs and pay?
|
https://economicsobservatory.com
|
[] |
Over the past 30 years, robots – specifically automation of manufacturing – have been taking tasks away from workers. This is known as 'task- ...
|
Decades of growing wage inequality have raised concerns about the impact of technology on the US labour market. New analysis of ‘task displacement’ distinguishes contrasting effects of automation on workers. Not all robots are created equal: some do more harm than good.
Over the past 40 years, earnings growth in the United States has been slow and unequal. Between 1980 and 2017, wages rose among male workers educated to degree level but fell among men without a degree by 10-20% in real terms (taking account of inflation). This is not a uniquely American problem: the pay gap between those who are more and less educated has grown in almost every industrialised country, albeit with the United States as an extreme case (Hoffmann et al, 2020).
Figure 1: US median weekly wages by education level
Source: US Bureau of Labor Statistics
New research is shedding light on the drivers behind this growing wage inequality. One possible explanation is that an increase in international trade has negatively affected the US labour market (Autor et al, 2014). Another factor may be the increase in the market power of US firms: mark-ups (the difference between the price at which they sell and the costs of production) have shot up – from 18% above marginal cost in 1980 to 67% in 2014 (De Loecker and Eeckhout, 2017).
There has also been a rise in global ‘superstar firms’. These firms are able to find ways to lower the amount they spend on labour in order to increase their market power further (Autor et al, 2017). New complementary analysis attributes this decline in wages to the rise of automation and increased adoption of robots in the production process since the 1980s (Acemoglu and Restrepo, 2018).
With these competing theories in mind, I attended a recent talk by Daron Acemoglu of MIT, which covered the effects of automation on inequality and what this can tell us about the future of work.
Are robots taking people's jobs?
Robots are central to understanding growing inequality in earnings. Between 1993 and 2007, one robot in the manufacturing industry has replaced 3.3 jobs in the United States. When restricted to commuting zones, this figure doubles to one robot replacing 6.6 jobs (Acemoglu and Restrepo, 2020).
Over the past 30 years, robots – specifically automation of manufacturing – have been taking tasks away from workers. This is known as ‘task-based displacement’. In one study, the authors find that more than 50% of the changes in US wage structure between 1980 and 2016 are due to workers being exposed to robot-driven changes in production processes (Acemoglu and Restrepo, 2017). Rather than technology increasing the productivity of labour, these innovations have taken tasks away from workers. Worse still, they have not created new jobs in the process.
The changing nature of jobs through automation has been a longstanding concern for the future of the labour market. Robots have had a substantial impact on the tasks carried out by manufacturing workers, but the services sector, including healthcare, transport and finance, has also been affected.
Globally, the OECD estimates that 14% of current jobs could disappear due to automation in the next 15 to 20 years, with another 32% of jobs very likely to experience radical change as individual tasks become automated (OECD, 2019). On average, participation in training for those in low-skilled jobs (which are most at risk of automation) is 40% lower than that for high-skilled workers. What’s more, workers whose jobs have not been automated are typically more educated and have seen wage increases.
Figure 2: Jobs at risk to automation (%)
Is automation the sole cause of task displacement?
Automation is a not a new phenomenon. From the weaving loom in the 19th century to the invention and continuing development of cars, automation has been part and parcel of life. Consequently, an alternative explanation for the recent rise in wage inequality has been the increase in ‘offshoring’ of production work and services. The United States alone lost over one million manufacturing jobs in the decade since China joined the World Trade Organization in 2001. Individuals who work in industries affected by greater competition from imports face lower earnings and are less likely to maintain a job with their initial employer or in the same industry (Autor et al, 2014).
But this offshoring of jobs can only explain some of the changes in wage structure. According to Daron Acemoglu, a change in the past 40 years has been the increase in bad or ‘so-so’ automation – technology that reduces employment and worsens the distribution of income. He argues that merely pushing wages up is not a solution. Rather, high-productivity or ‘good’ automation, when combined with the rapid creation of new tasks for workers, can be an effective engine for growth.
Consider, for example, the mechanisation of agriculture in the late 19th century, with the introduction of steam-powered machines followed by the first modern tractor. While employment in agriculture fell, overall labour demand in the United States rose because a range of new tasks were introduced in both manufacturing and services (Acemoglu and Restrepo, 2019). Daron Acemoglu would define this as ‘good automation’.
Then consider a more recent invention such as self-service checkouts, where the technology has not improved the quality of the service and has simply displaced tasks from retail workers onto consumers without increasing labour productivity. This is a prime example of excessive so-so automation: an invention just good enough to be adopted but not much more productive than the labour it has replaced.
In short, not all robots are created equal: some can do more harm than good.
Has Covid-19 accelerated technology adoption – and what does this mean for the future of work?
Covid-19 has forced many businesses to change their work practices, with over 40% of the UK workforce working remotely in May 2020, according to the Office for National Statistics (ONS, 2020). Recent survey data indicate that consumers and businesses leaped five years ahead in digital adoption within the first eight weeks of the pandemic (McKinsey, 2020). Another UK survey estimates that small and medium-sized enterprises (SMEs) alone created three years of innovation in the same number of months during and after the Spring 2020 lockdown (Be the Business, 2020).
But this accelerated technology adoption has been driven by the need to ensure business continuity during the pandemic and may not be good automation (improving productivity while creating new tasks for workers). Being able to schedule several meetings at once, rather than having a quick chat with your colleagues in the break room, is not exactly a new frontier for labour-augmenting technology.
The future of the labour market ultimately depends on the choices we make now. History shows that automation can improve outcomes for workers, but a growing body of evidence has shed light on why that has not been the case in the last 40 years. Policies can correct for biases towards automation resulting from innovation dynamics or market distortions.
Daron Acemoglu notes that one striking change in US economic policy over the same period has been the change in the tax structure. While labour has been taxed at an average rate of 25% over the past four decades, the average tax rate on software and equipment has fallen from 15% in the 1990s to 5% in the 2010s. By favouring capital investment, this tax regime may have encouraged excessive automation, creating a precarious situation where firms have incentives to choose robots over workers.
But automation that is neither productivity-improving nor able to generate new jobs is not inevitable. Looking forward, technologies such as artificial intelligence (AI) are capable of creating several new tasks for human workers. Yet the business models at the forefront of these new technologies are not yet making this outcome a priority. The decisions of policy-makers, businesses, consumers and citizens will determine the trajectory taken in the balance between automation and inequality.
Where can I find out more?
Author: Rahat Siddique
Photo by Steve Jurvetson from Wikimedia Commons
| 2021-02-08T00:00:00 |
https://economicsobservatory.com/how-are-robots-affecting-jobs-and-pay
|
[
{
"date": "2021/02/08",
"position": 92,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 88,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 90,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 93,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 89,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 86,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 88,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 91,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 91,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 95,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 90,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 81,
"query": "robotics job displacement"
}
] |
|
How are robots affecting jobs and pay? - Economics Observatory
|
How are robots affecting jobs and pay?
|
https://www.economicsobservatory.com
|
[] |
Over the past 30 years, robots – specifically automation of manufacturing – have been taking tasks away from workers. This is known as 'task- ...
|
Decades of growing wage inequality have raised concerns about the impact of technology on the US labour market. New analysis of ‘task displacement’ distinguishes contrasting effects of automation on workers. Not all robots are created equal: some do more harm than good.
Over the past 40 years, earnings growth in the United States has been slow and unequal. Between 1980 and 2017, wages rose among male workers educated to degree level but fell among men without a degree by 10-20% in real terms (taking account of inflation). This is not a uniquely American problem: the pay gap between those who are more and less educated has grown in almost every industrialised country, albeit with the United States as an extreme case (Hoffmann et al, 2020).
Figure 1: US median weekly wages by education level
Source: US Bureau of Labor Statistics
New research is shedding light on the drivers behind this growing wage inequality. One possible explanation is that an increase in international trade has negatively affected the US labour market (Autor et al, 2014). Another factor may be the increase in the market power of US firms: mark-ups (the difference between the price at which they sell and the costs of production) have shot up – from 18% above marginal cost in 1980 to 67% in 2014 (De Loecker and Eeckhout, 2017).
There has also been a rise in global ‘superstar firms’. These firms are able to find ways to lower the amount they spend on labour in order to increase their market power further (Autor et al, 2017). New complementary analysis attributes this decline in wages to the rise of automation and increased adoption of robots in the production process since the 1980s (Acemoglu and Restrepo, 2018).
With these competing theories in mind, I attended a recent talk by Daron Acemoglu of MIT, which covered the effects of automation on inequality and what this can tell us about the future of work.
Are robots taking people's jobs?
Robots are central to understanding growing inequality in earnings. Between 1993 and 2007, one robot in the manufacturing industry has replaced 3.3 jobs in the United States. When restricted to commuting zones, this figure doubles to one robot replacing 6.6 jobs (Acemoglu and Restrepo, 2020).
Over the past 30 years, robots – specifically automation of manufacturing – have been taking tasks away from workers. This is known as ‘task-based displacement’. In one study, the authors find that more than 50% of the changes in US wage structure between 1980 and 2016 are due to workers being exposed to robot-driven changes in production processes (Acemoglu and Restrepo, 2017). Rather than technology increasing the productivity of labour, these innovations have taken tasks away from workers. Worse still, they have not created new jobs in the process.
The changing nature of jobs through automation has been a longstanding concern for the future of the labour market. Robots have had a substantial impact on the tasks carried out by manufacturing workers, but the services sector, including healthcare, transport and finance, has also been affected.
Globally, the OECD estimates that 14% of current jobs could disappear due to automation in the next 15 to 20 years, with another 32% of jobs very likely to experience radical change as individual tasks become automated (OECD, 2019). On average, participation in training for those in low-skilled jobs (which are most at risk of automation) is 40% lower than that for high-skilled workers. What’s more, workers whose jobs have not been automated are typically more educated and have seen wage increases.
Figure 2: Jobs at risk to automation (%)
Is automation the sole cause of task displacement?
Automation is a not a new phenomenon. From the weaving loom in the 19th century to the invention and continuing development of cars, automation has been part and parcel of life. Consequently, an alternative explanation for the recent rise in wage inequality has been the increase in ‘offshoring’ of production work and services. The United States alone lost over one million manufacturing jobs in the decade since China joined the World Trade Organization in 2001. Individuals who work in industries affected by greater competition from imports face lower earnings and are less likely to maintain a job with their initial employer or in the same industry (Autor et al, 2014).
But this offshoring of jobs can only explain some of the changes in wage structure. According to Daron Acemoglu, a change in the past 40 years has been the increase in bad or ‘so-so’ automation – technology that reduces employment and worsens the distribution of income. He argues that merely pushing wages up is not a solution. Rather, high-productivity or ‘good’ automation, when combined with the rapid creation of new tasks for workers, can be an effective engine for growth.
Consider, for example, the mechanisation of agriculture in the late 19th century, with the introduction of steam-powered machines followed by the first modern tractor. While employment in agriculture fell, overall labour demand in the United States rose because a range of new tasks were introduced in both manufacturing and services (Acemoglu and Restrepo, 2019). Daron Acemoglu would define this as ‘good automation’.
Then consider a more recent invention such as self-service checkouts, where the technology has not improved the quality of the service and has simply displaced tasks from retail workers onto consumers without increasing labour productivity. This is a prime example of excessive so-so automation: an invention just good enough to be adopted but not much more productive than the labour it has replaced.
In short, not all robots are created equal: some can do more harm than good.
Has Covid-19 accelerated technology adoption – and what does this mean for the future of work?
Covid-19 has forced many businesses to change their work practices, with over 40% of the UK workforce working remotely in May 2020, according to the Office for National Statistics (ONS, 2020). Recent survey data indicate that consumers and businesses leaped five years ahead in digital adoption within the first eight weeks of the pandemic (McKinsey, 2020). Another UK survey estimates that small and medium-sized enterprises (SMEs) alone created three years of innovation in the same number of months during and after the Spring 2020 lockdown (Be the Business, 2020).
But this accelerated technology adoption has been driven by the need to ensure business continuity during the pandemic and may not be good automation (improving productivity while creating new tasks for workers). Being able to schedule several meetings at once, rather than having a quick chat with your colleagues in the break room, is not exactly a new frontier for labour-augmenting technology.
The future of the labour market ultimately depends on the choices we make now. History shows that automation can improve outcomes for workers, but a growing body of evidence has shed light on why that has not been the case in the last 40 years. Policies can correct for biases towards automation resulting from innovation dynamics or market distortions.
Daron Acemoglu notes that one striking change in US economic policy over the same period has been the change in the tax structure. While labour has been taxed at an average rate of 25% over the past four decades, the average tax rate on software and equipment has fallen from 15% in the 1990s to 5% in the 2010s. By favouring capital investment, this tax regime may have encouraged excessive automation, creating a precarious situation where firms have incentives to choose robots over workers.
But automation that is neither productivity-improving nor able to generate new jobs is not inevitable. Looking forward, technologies such as artificial intelligence (AI) are capable of creating several new tasks for human workers. Yet the business models at the forefront of these new technologies are not yet making this outcome a priority. The decisions of policy-makers, businesses, consumers and citizens will determine the trajectory taken in the balance between automation and inequality.
Where can I find out more?
Author: Rahat Siddique
Photo by Steve Jurvetson from Wikimedia Commons
| 2021-02-08T00:00:00 |
https://www.economicsobservatory.com/how-are-robots-affecting-jobs-and-pay
|
[
{
"date": "2021/02/08",
"position": 47,
"query": "robotics job displacement"
},
{
"date": "2021/02/08",
"position": 92,
"query": "robotics job displacement"
}
] |
|
The Future of Jobs in the Era of AI | BCG - Boston Consulting Group
|
The Future of Jobs in the Era of AI
|
https://www.bcg.com
|
[
"Rainer Strack",
"Miguel Carrasco",
"Philipp Kolo",
"Nicholas Nouri",
"Michael Priddis",
"Richard George"
] |
The increasing adoption of automation, artificial intelligence (AI), and other technologies suggests that the role of humans in the economy will shrink ...
|
The increasing adoption of automation, artificial intelligence (AI), and other technologies suggests that the role of humans in the economy will shrink drastically, wiping out millions of jobs in the process. COVID-19 accelerated this effect in 2020 and will likely boost digitization, and perhaps establish it permanently, in some areas.
However, the real picture is more nuanced: though these technologies will eliminate some jobs, they will create many others. Governments, companies, and individuals all need to understand these shifts when they plan for the future.
BCG recently collaborated with Faethm, a firm specializing in AI and analytics, to study the potential impact of various technologies on jobs in three countries: the US, Germany, and Australia. Using the underlying demographics in each country, we developed detailed scenarios that model the effects of new technologies and consider the impact of the pandemic on GDP growth. ( See Appendix A .)
One key finding is that the net number of jobs lost or gained is an artificially simple metric to gauge the impact of digitization. For example, eliminating 10 million jobs and creating 10 million new jobs would appear to have negligible impact. In fact, however, doing so would represent a huge economic disruption for the country—not to mention for the millions of people with their jobs at stake. Therefore, policymakers and countries that want to understand the implications of automation need to drill down and look at disaggregated effects. Understanding the future of jobs is a tall order, but the groundbreaking analysis we conducted helps governments, companies, and individuals take the critical first step to prepare for what is to come.
In This Report
Three Components of Workforce Imbalances
In general, computers perform well in tasks that humans find difficult or time-consuming to do, but they tend to work less effectively in tasks that humans find easy to do. Although new technologies will eliminate some occupations, in many areas they will improve the quality of work that humans do by allowing them to focus on more strategic, value-creating, and personally rewarding tasks.
To understand the potential impact of new technologies on future workforces, we looked at three components of imbalances in the US, Germany, and Australia:
Workforce Supply and Demand . We analyzed all elements that affect a nation’s full-time equivalent (FTE) workforce, including the number of college graduates and the rates of retirement, mortality, and migration. And we used standardized job taxonomies on a very granular level for both supply and demand. The taxonomies were based on 22 common job family groups, and close to 100 job families, found in countries all around the world. The three countries we studied for our analysis, however, showed slight variations in the numbers of job families—93 for the US, 86 for Germany, and 82 for Australia—because of differences in their national taxonomies. (See Exhibit 1.)
. We analyzed all elements that affect a nation’s full-time equivalent (FTE) workforce, including the number of college graduates and the rates of retirement, mortality, and migration. And we used standardized job taxonomies on a very granular level for both supply and demand. The taxonomies were based on 22 common job family groups, and close to 100 job families, found in countries all around the world. The three countries we studied for our analysis, however, showed slight variations in the numbers of job families—93 for the US, 86 for Germany, and 82 for Australia—because of differences in their national taxonomies. (See Exhibit 1.) Technology. To model the impact of technology, we used analytics provided by a Faethm platform to develop three sets of circumstances with different tech adoption rates. The technologies under consideration included programmed intelligence (predefined technologies, such as process automation and robotics), narrow AI (reactive technologies, such as tools that use machine learning to recognize and organize data), broad AI (proactive technologies that can sense external stimuli and make decisions), and reinforced AI (self-improving technologies, such as fully autonomous robots or those that can solve unstructured, complex problems). ( See Appendix B.) We considered the medium adoption rate to be the standard, but we also evaluated adoption rates that were 25% faster and 25% slower than the standard in our analysis.
To model the impact of technology, we used analytics provided by a Faethm platform to develop three sets of circumstances with different tech adoption rates. The technologies under consideration included programmed intelligence (predefined technologies, such as process automation and robotics), narrow AI (reactive technologies, such as tools that use machine learning to recognize and organize data), broad AI (proactive technologies that can sense external stimuli and make decisions), and reinforced AI (self-improving technologies, such as fully autonomous robots or those that can solve unstructured, complex problems). ( See Appendix B.) We considered the medium adoption rate to be the standard, but we also evaluated adoption rates that were 25% faster and 25% slower than the standard in our analysis. GDP Growth. Given the continuing and dynamic evolution of the pandemic, we used two major COVID-19 projections to simulate future GDP growth: one is a baseline, while the other is more severe and has a longer recovery time. We leveraged data from Oxford Economics for both projections from 2018 up to 2025 and then used the baseline projections to extrapolate growth to 2030.
1 / 1
Looking at all of these factors gave us an aggregate impact of automation and economic growth on national workforces by 2030. Two economic forecasts, and three possible technology adoption rates, led to a total of six possible scenarios:
Baseline COVID-19 projection: high, medium, or low rate of technology adoption
COVID-19 projection: high, medium, or low rate of technology adoption Severe COVID-19 projection: high, medium, or low rate of technology adoption
Throughout this report, unless mentioned otherwise, we will refer to the midrange scenario, which comprises a baseline GDP forecast in response to the pandemic and a medium rate of technology adoption.
We find that the US will likely experience a labor shortfall in its workforce of 0.9% to 4.4% by 2030. Germany will also experience a shortfall, of 0.5% to 4.1%. And although Australia will experience a labor shortfall of up to 3.7% in the baseline scenario, it will experience a labor surplus of up to 4.0% if the pandemic causes a more severe impact on GDP growth. (See Exhibit 2.) These consolidated gaps are the difference between the total supply and the total demand in the future workforce for each country. This net number, however, is only an initial indication, and policymakers and business leaders need to look at the disaggregated perspective to see the full picture. Our research also reveals that automation will reduce the number of both unskilled jobs and white-collar positions.
Exhibit 2 | The Future of Jobs in the Era of AI
The two additional sets of technology adoption circumstances that we considered would influence the labor curve accordingly. Faster adoption rates would lead to greater demand for people in specific occupations as well as greater surpluses in others that are more prone to automation. Slower adoption rates would lead to a less severe impact on the labor force. In total, the effect would be lower workforce demand.
A Closer Look at Three Markets
Taking the qualifications of the workforce into account in the form of job family groups generates a much more detailed picture.
United States. Talent shortfalls in key occupations, such as computer and mathematics, for the midrange scenario is set to soar from 571,000 in 2020 to 6.1 million by 2030. (See Exhibit 3.) The deficit in supply of architecture and engineering workers is also set to rise sharply, from 60,000 in 2020 to 1.3 million in 2030. So even though the country’s overall supply of labor is projected to rise, the US will face significant deficits in crucial fields. In fact, the sum of all job family groups with a shortfall is 17.6 million. Technology and automation will also drive people out of work in the US, particularly in office and administrative support, where the surplus of workers will rise from 1.4 million in 2020 to 3.0 million in 2030.
1 / 1
Germany. Germany is also projected to have a shortfall of talent in computer and mathematics by 2030: 1.1 million. (See Exhibit 4.) The next most severely affected job family groups are educational instruction and library occupations (346,000) as well as health care practitioners and technical occupations (254,000). Yet Germany’s overall shortfall of talent does not preclude workforce surpluses: production occupations, for example, are expected to rise from 764,000 in 2020 to 801,000 by 2030. This is a very good example of the shift from jobs with repetitive tasks in production lines to those in the programming and maintenance of production technology—and thus the need for significant reskilling (teaching employees entirely new skills needed for a different job or sector) and upskilling (giving employees upgraded skills to stay relevant in a current occupation).
1 / 1
Australia. Australia will experience difficulties in filling jobs in certain sectors, although the overall workforce supply looks less stretched. The greatest shortfall by far exists again in computer and mathematics, where the figure will rise to 333,000 by 2030. (See Exhibit 5.) The three job family groups with the next most significant shortfalls are management; health care practitioners and technical support; and business and financial operations.
1 / 1
However, technology will exacerbate Australia’s workforce surplus in certain sectors. For example, in production, the surplus will stay high, rising slightly—to 118,000—by 2030. And with technology taking over mundane, repetitive tasks, the surplus in office and administrative is expected to rise from 161,000 in 2020 to 180,000 by 2030. Nonetheless, the sum of all job family groups with a surplus is 0.6 million, while the sum of all job family groups with a shortfall is a cumulative 1.0 million jobs. Combining the two cumulative figures of shortfalls and surpluses gives the net workforce imbalances.
Growing Demand for Technological and Soft Skills
For all three countries, the development of labor supply does not fully match the changes in demand, except for certain occupations. At the same time, in many sectors, severe shortages of skilled workers will mean that growth in demand for talent will be unmet. This is particularly true for computer-related occupations and jobs in science, technology, engineering, and math, since technology is fueling the rise of automation across all industries. This is why the computer and mathematics job family group is likely to suffer by far the greatest worker deficits in all three countries.
Meanwhile, in job family groups that involve little or no automation but that do require compassionate human interaction tailored to specific groups—such as health care, social services, and certain teaching occupations—the demand for human skills will increase as well. Germany and the United States, given their overall human resource deficits, will face the greatest pressure for talent in these occupations. For example, Germany will suffer a shortfall of 346,000 people in the educational instruction and library sector by 2030. The deficit for health care practitioners and technical support will rise to 254,000. In the United States, the deficits for those two groups will rise to 1.1 million and to nearly 1.7 million, respectively, by 2030. Even Australia will suffer a significant shortfall, in health care practitioners and technical support: 168,000.
Sensitivity of Outcomes
Exhibit 6 provides an overview of all six potential scenarios and gives an indication of the possible situations that may occur across them. For example, Australia will face a shortfall in the baseline COVID-19 projection of approximately 800,000 workers, assuming a low rate of technology adoption. At the other end of the spectrum—the severe COVID-19 projection, with a high rate of technology adoption—Australia will face a labor surplus of about 800,000.
1 / 1
Compared with the United States and Germany, Australia is projected to experience a substantial growth in labor supply. In 2002, the national government started offering cash subsidies to parents of newborns in an effort to lift the country’s fertility rate. The increase resulted in a baby boom of people who will enter the job market over the next decade. At the same time, Australia has significantly cut immigration for the foreseeable future in response to the economic challenges of the pandemic—a short-term effect that will lead to increased labor supply when immigration resumes. Nevertheless, the projected skills mismatch is unlikely to be fully resolved. Therefore, we expect higher levels of unemployment in some areas and more acute skills shortages in others.
The shortfall is even more pronounced in Germany, where the analysis shows a talent shortfall in five of the six potential scenarios we identified. Only a severe impact by the pandemic on GDP, combined with high technology adoption, would generate a net surplus of approximately 1 million employees. Germany faces the dual challenge of a birth rate that has remained low, at an average of 1.6 children per woman, combined with aging baby boomers who will retire in the next decade. Exacerbating this is a demand for workers that we anticipate will either remain constant or increase.
Germany faces the dual challenge of a low birth rate combined with aging baby boomers who will retire in the next decade.
Similarly, five of the six potential scenarios in the US show a shortfall (up to 12.5 million people), and only the severe COVID-19 trajectory, combined with a high adoption rate of technology, indicates a surplus (up to 4.5 million).
Although the adoption of technology is progressing at roughly the same pace in all three countries, demographic profiles suggest that they will face different challenges during the digital transition. However, they all share the need for a labor force that has the right composition of skills to meet the needs of the digital age, which will demand upskilling and reskilling on a large scale. In the baseline projection, all three countries will face a net workforce gap. In addition, a certain level of structural unemployment (defined as the difference between demand and supply) will prevail. Therefore, the challenge is more significant than the aggregate numbers suggest.
Building an Adaptive Workforce
The stark predictions for labor deficits suggest that all three of the countries we studied should take deliberate action to build a workforce that is ready for the future. Governments and corporate leaders need to understand the specific demographic challenges they face, where the biggest impact of automation will be, and how they can help individuals remain employable by maintaining their skills. They then need to ensure that workers continue to learn over time as demand for different skills evolves. In short, countries must build an adaptive workforce.
Through a deeper dive into the analysis, we can identify the job families (which make up the job family groups discussed above) with the highest absolute surpluses and shortfalls in 2030 for the baseline projection. (See Exhibit 7.) These job families reflect the areas with the highest need for action from all stakeholders. The US, Germany, and Australia share some similarities here. For example, all three show that information and record clerks constitute one of the occupations with the greatest overall surplus (1.9 million for the three countries)—an increase generated by the ability of new technology solutions to manage this task. Similarly, all three countries will see a steep shortage of business operations specialists (who analyze business operations and identify customer needs) as a direct result of the data made more widely available by technology.
1 / 1
Of course, although humans may no longer be needed for some tasks, they will nevertheless be necessary to help develop automation. Decisions must be made on the rules governing the use of new tools and how to implement and maintain the software or robots that are taking over those tasks. Despite eliminating the need for human employees for many routine and administrative tasks, technology can also create new jobs as the demand for software developers, data analysts, cybersecurity testers, and other digital specialists rises across all sectors. There may be a need to redeploy, upskill, or reskill people—and perhaps even to redefine any given job itself. Although these markets share characteristics in terms of technology adoption, significant differences emerge.
In the United States, for example, for every six jobs that are being automated or augmented by new technologies, one additional job will be needed in order to develop, implement, and run those new technologies. In the aggregate, those newly created roles will encompass 63 occupations, mostly in the fields of data science and software development.
Increased job automation will also create significant opportunities. Primarily, it will enable workers to undertake higher-value tasks. For example, the removal of mundane, repetitive tasks in legal, accounting, administrative, and similar professions opens the possibility for employees to take on more strategic roles. This also illustrates how automation will affect not only blue-collar jobs but white-collar occupations as well.
The removal of mundane, repetitive tasks in legal, accounting, administrative, and similar professions opens the possibility for employees to take on more strategic roles.
Meanwhile, core human abilities—such as empathy, imagination, creativity, and emotional intelligence, which cannot be replicated by technology—will become more valuable. The supply of talent for occupations that require these abilities—such as health care workers, teachers, and counselors—is currently limited, causing the high shortfalls we see in these job families. At the same time, crises such as the COVID-19 pandemic underscore the importance of these occupations in ensuring societal well-being.
Recommendations for Governments
Countries need to take the following actions to get ahead of current and future work imbalances in their employment markets.
Plan for the future workforce. Governments should have a central workforce strategy and policy unit in place to understand the current trends in workforce supply and demand; identify the gaps that exist in certain jobs, sectors, and skills; and predict the measures that will be needed to close those gaps. Specific resources include advanced-analytics models to predict changes over time and sufficiently granular sources of data that can generate insights into various regions, sectors, and demographics. Furthermore, the findings should be translated into strategic directions that are then implemented in specific policies and programs across government departments, including education, welfare, labor, and economics.
Rethink education, upskilling, and reskilling. Guided by strategic workforce planning, governments should create adult upskilling and reskilling programs at scale. Success requires working closely with the private sector and academia to develop more creative solutions that match the shifting realities of the labor marketplace over time. Governments also need to refocus education systems to develop so-called metaskills, such as logical thinking, reasoning, curiosity, open-mindedness, collaboration, leadership, creativity, and systems thinking.
In addition, education systems must become more flexible, moving beyond degree programs that require several years to complete and, instead, facilitating intermittent periods of study. They should also help people to obtain microcredentials and certifications tailored to industry needs (ideally created in partnership with the private sector) and to upgrade their skills on a regular basis.
The right solution will require a much broader set of educational formats to convey these skills in a sound way. Current education funding models need to shift from large, one-time subsidies to smaller, incremental payments spread over a person’s lifetime. This might mean, for example, creating lifelong learning accounts, such as those in a program offered by the Singapore government, which provide funding for a person’s education over their lifetime and can be drawn upon whenever they need to upgrade existing skills or gain new ones. Traditional education systems will need to apprise prospective students of the fields of study and types of degrees that are most aligned with the needs of the workforce, so they can make informed decisions about which path to pursue. This is especially true for students who are transitioning to higher education. What’s more, doing so would cut down on any future reskilling needs because citizens will have acquired the skill profiles demanded by the labor market right from the start.
Build career and employment platforms. To ensure that the labor market is working as efficiently as possible, some governments are creating comprehensive data and digital employment platforms so that workers can navigate to jobs and training opportunities more easily and quickly. A world-class platform helps citizens assess their skills, identify potential employment pathways, and close capability gaps through upskilling and reskilling opportunities. Critically, these platforms need to be continually updated and reinvented to ensure that they remain relevant and useful. In addition, governments need to establish their own employer brands in order to influence immigration patterns and attract employees with relevant skills from other countries.
Update social safety nets. Given the risk that automation poses for many jobs, policymakers need to focus on providing upskilling and reskilling opportunities for workers who are in transition, employed part-time, or unable or unwilling to adapt to the digital economy. Welfare policies need to adapt so the system can assist people who regularly enter and exit the workforce. Supporting those who do not profit directly from the positive effects of future technologies is critical to fuel a societal support for this major shift toward a more flexible and adaptive workforce. Funding all this will require governments to embrace automation in their own administrations as boldly as possible.
Drive innovation and support small and midsize enterprises (SMEs). SMEs may lack the funds to continually enhance their automation and thus will need support in the form of subsidized loans or investment tax breaks as they work to develop a digitally enabled workforce. Regulations for the use of advanced technologies should not overburden these SMEs, in order to avoid inhibiting their innovation. Supporting SMEs will help build the capabilities needed to drive innovation throughout the economy as well.
Recommendations for Companies
To ensure that current and future work imbalances do not have an impact on their financial stability and ability to compete, companies need to take the following actions.
Perform strategic workforce planning. As at the country level, a company should regularly assess the current size, composition, and development of its workforce. It should also evaluate future demand on the basis of strategic direction and determine the gaps for certain jobs and specific skills. Furthermore, it should proactively design the measures needed to close those gaps. These strategic measures need to be closely connected to the company’s overall planning for the medium term and need to be budgeted accordingly to ensure swift implementation.
Upskill and reskill existing workforces. Given the rapid shifts in skill requirements and the number of entirely new tasks and roles that are emerging, the labor market will be unable to supply sufficient new talent to fill available positions. Companies therefore need to supplement external hiring with internal development initiatives and on-the-job training.
Create a lifelong learning culture. Corporate training used to consist of certifications or intermittent training programs, but the digital economy will demand a constant upgrading of skills. Companies therefore need to build constant learning into their business models. Content and skill upgrades should be delivered in a variety of formats so that they can be integrated into the daily routine of every employee, ensuring a nimble and agile workforce.
Rethink talent recruitment and retention strategies. The combination of demand for digital skills and demographic shifts will put extreme pressure on the labor supply pipeline, creating fierce competition for talent. Thus, companies may need to shift the recruitment focus from hiring for skill to hiring for will: as some of the skills needed in the future (such as coding computer languages) will most likely be self-taught or come without an explicit certification, HR professionals will need to view candidate criteria with a more open mind and embrace diverse curricula. Companies will also need to find new ways of retaining their talent and equipping them with the skills that will enable them to stay relevant within the changing context in which the enterprise operates.
Companies may also opt to create an employee pool to which people with new skills can be added without yet knowing which field of operations they’d be best suited for. Companies could choose to assess intangible skills with trial periods as well. In a postpandemic era, the higher prevalence of remote working will allow companies to access international and more fluid talent pools that are outside the companies’ main markets. For many organizations, this will be a completely new source of talent to explore and manage.
Recommendations for Individuals
In order to ensure that they are prepared for the jobs of the future, individuals will have to take greater responsibility for their own professional development, whether that means through upskilling or reskilling. They should take the following actions.
Make lifelong learning the new normal. Whether through programs offered by employers or through private channels, continuous learning and the acquisition of new skills must become central to an individual’s working life. Individuals should also invest not only in digital skills but also in metaskills, which will serve them well regardless of shifts in the market.
Continuous learning and the acquisition of new skills must become central to an individual’s working life.
Remain focused on upskilling and reskilling. More and more sources of information about jobs and skills will become available in the coming years. Many governments are establishing overviews of jobs and skills that are currently in demand and creating forecasts for the future. Individuals need to pay attention to these sources of information and update their skills accordingly, either by searching out high-quality providers of education or by charting their own course amid the vast amount of online-learning offers.
Become more flexible when developing a career path. Frequent career changes and lateral moves into similar job positions will become increasingly necessary. Therefore, workers should remain flexible throughout their careers, looking for positions where their existing skill sets can be applied successfully as well as updating their skill sets according to where their own interests match the market’s needs.
The Way Forward
As countries prepare to meet the demands of the digital age, they must understand the challenges that lie ahead. This means making use of more sophisticated analytical models to predict supply and demand in the labor market and integrating them into the foundation of their workforce strategies. It also means focusing on managing the transition to a future workforce so that the economic and social friction associated with the mismatch of supply and demand is minimized.
To reduce the mismatch in skills, governments should update the education system. They should create more flexible institutions that can anticipate the future needs of companies and refocus on metaskills.
Companies need to invest in corporate academies, training partnerships, and constant upskilling and reskilling of their existing workforces. They should also transform their HR functions and processes to cater to the shift in approach needed to hire and retain talent with the new skills in demand. Companies that make these investments and significant changes in their own processes stand to gain a substantial competitive advantage over those that stick with their current approach.
Countries that leverage education to create attractive locations for companies will gain a competitive edge over their static neighbors.
Perhaps more important, given the speed of the digital transformation, it is urgent to make such investments today. Countries that leverage education to create attractive locations for companies will gain a competitive edge over their static neighbors. Companies that hesitate will find themselves unable to access the talent they need and will fail to capitalize on the opportunities that technology brings. Surviving and thriving in the digital age means understanding current shifts, predicting future transformations, and responding rapidly to build an adaptive, future-ready workforce that can support a strong and equitable economy.
| 2021-03-11T00:00:00 |
2021/03/11
|
https://www.bcg.com/publications/2021/impact-of-new-technologies-on-jobs
|
[
{
"date": "2021/03/18",
"position": 48,
"query": "AI impact jobs"
},
{
"date": "2021/03/18",
"position": 47,
"query": "AI impact jobs"
},
{
"date": "2021/03/18",
"position": 66,
"query": "AI impact jobs"
},
{
"date": "2021/03/18",
"position": 44,
"query": "AI impact jobs"
},
{
"date": "2021/03/18",
"position": 45,
"query": "AI impact jobs"
},
{
"date": "2021/03/18",
"position": 46,
"query": "AI impact jobs"
},
{
"date": "2021/03/18",
"position": 46,
"query": "AI impact jobs"
},
{
"date": "2021/03/18",
"position": 46,
"query": "AI impact jobs"
}
] |
Why Robots Won't Steal Your Job - Harvard Business Review
|
Why Robots Won’t Steal Your Job
|
https://hbr.org
|
[
"Nahia Orduña",
"Is An Engineer Holding An Mba"
] |
85 million jobs may be displaced by the shift in labor between humans and machines by 2025, while 97 million new roles may
|
Science-fiction films and novels usually portray robots as one of two things: destroyers of the human race or friendly helpers. The common theme is that these stories happen in an alternate universe or a fantasy version of the future. Not here, and not now — until recently. The big difference is that the robots have come not to destroy our lives, but to disrupt our work.
| 2021-03-19T00:00:00 |
2021/03/19
|
https://hbr.org/2021/03/why-robots-wont-steal-your-job
|
[
{
"date": "2021/03/19",
"position": 13,
"query": "robotics job displacement"
},
{
"date": "2021/03/19",
"position": 13,
"query": "robotics job displacement"
},
{
"date": "2021/03/19",
"position": 12,
"query": "robotics job displacement"
},
{
"date": "2021/03/19",
"position": 12,
"query": "robotics job displacement"
},
{
"date": "2021/03/19",
"position": 15,
"query": "robotics job displacement"
},
{
"date": "2021/03/19",
"position": 13,
"query": "robotics job displacement"
},
{
"date": "2021/03/19",
"position": 14,
"query": "robotics job displacement"
},
{
"date": "2021/03/19",
"position": 13,
"query": "robotics job displacement"
},
{
"date": "2021/03/19",
"position": 13,
"query": "robotics job displacement"
},
{
"date": "2021/03/19",
"position": 13,
"query": "robotics job displacement"
},
{
"date": "2021/03/19",
"position": 14,
"query": "robotics job displacement"
},
{
"date": "2021/03/19",
"position": 15,
"query": "robotics job displacement"
}
] |
Artificial intelligence poses an inhuman risk to worker rights - Monitaur
|
Artificial intelligence poses an inhuman risk to worker rights
|
https://www.monitaur.ai
|
[] |
... labor unions. The Trades Union Congress (TUC), a large federation of trade unions in England and Wales, has published a manifesto ...
|
Article Summary of:
The circles of the broader social conversation about the risks of AI and ML keep expanding. While we've recently highlighted those in the research, NGO, and developer communities, a new entrant has entered the discussion: labor unions. The Trades Union Congress (TUC), a large federation of trade unions in England and Wales, has published a manifesto establishing core principles that mirror much of what we've seen from other public and private groups. Francis O'Grady of the TUC concludes, "Make no mistake. AI can be harnessed to transform working lives for the better. But without proper regulation, accountability and transparency, we risk it being used to set punishing targets, rob workers of human connection and deny them dignity at work."
The TUC's focus on the intersection of workers' rights and internal use of AI for hiring and human resources parallels accelerating interest in this area of late. Back in October, the American Bar Association expanded upon AI creating potential risks under existing employment laws for company counsel to monitor. SAFELab at Columbia University argues that social workers are important voices to ensure that the most vulnerable and affected communities are protected by AI regulation.
| 2021-03-25T00:00:00 |
https://www.monitaur.ai/articles/artificial-intelligence-poses-an-inhuman-risk-to-worker-rights
|
[
{
"date": "2021/03/25",
"position": 77,
"query": "artificial intelligence labor union"
}
] |
|
New manifesto defends workers' rights from artificial iIntelligence
|
New manifesto defends workers’ rights from artificial iIntelligence
|
https://www.unison.org.uk
|
[
"Demetrios Matheou"
] |
a legal duty on employers to consult trade unions on the use of “high risk” and intrusive forms of AI in the workplace;; a legal right for all ...
|
UNISON today signed up to a TUC manifesto designed to protect workers’ rights as technology increases in the workplace.
The manifesto, Dignity at work and the AI revolution, is based on a new report that says that employment law is failing to keep pace with the rapid expansion of artificial intelligence (AI) at work – which could lead to widespread discrimination and unfair treatment.
TUC president Frances O’Grady said this was “a crucial moment in the AI-driven technological workplace revolution.”
The report highlights how the use of AI has been accelerated by the coronavirus pandemic, with AI-powered technologies now making “high-risk, life changing” decisions about workers’ lives, such as selecting candidates for interview, day-to-day line management, performance ratings, shift allocation and deciding who is disciplined or made redundant.
One of the more shocking findings is that AI is being used to analyse facial expressions, tone of voice and accents in order to assess candidates’ suitability for roles.
The report says that unless urgent new legal protections are put in place, workers will become increasingly vulnerable and powerless to challenge “inhuman” forms of AI performance management.
To that end, the TUC has issued a joint call to tech companies, employers and government to support a new set of legal reforms for the ethical use of AI at work. These include:
a legal duty on employers to consult trade unions on the use of “high risk” and intrusive forms of AI in the workplace;
a legal right for all workers to have a human review of decisions made by AI systems, so they can challenge decisions that are unfair and discriminatory;
amendments to the UK general data protection regulation (UK GDPR) and Equality Act to guard against discriminatory algorithms;
a legal right to ‘ switch off ’ from work, so workers can create “communication free” time in their lives.
Introducing the manifesto, Ms O’Grady comments: “Artificial Intelligence (AI) is transforming the way we work and, alongside boosting productivity, offers an opportunity to improve working lives.
“But new technologies also pose risks: more inequality and discrimination, unsafe working conditions, and unhealthy blurring of the boundaries between home and work
“Our prediction is that, left unchecked, the use of AI to manage people will also lead to work becoming an increasingly lonely and isolating experience, where the joy of human connection is lost.”
UNISON president Josie Bird, who signed the manifesto pledge for the union, said: “The manifesto has come just at the right time. COVID and the increase in home working has seen a rise in intrusive AI use in workers’ lives.
“AI and digitalised technology are growing in all our jobs – and we need to plug the gaps in protections. We need these reforms in the law to ensure all workers are treated fairly and without discrimination.”
Ms Bird noted that, along with proposals for change, the manifesto “highlights the values we should all adopt to make sure that technology at work is for the benefit of everyone.”
Read the manifesto and pledge support
| 2021-03-25T00:00:00 |
2021/03/25
|
https://www.unison.org.uk/news/article/2021/03/new-manifesto-defends-workers-rights-artificial-intelligence/
|
[
{
"date": "2021/03/29",
"position": 92,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 94,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 94,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 91,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 91,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 92,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 93,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 90,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 93,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 90,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 91,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 94,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 92,
"query": "artificial intelligence labor union"
},
{
"date": "2021/03/29",
"position": 91,
"query": "artificial intelligence labor union"
}
] |
OpenAI CEO Sam Altman says AI could pay for UBI, experts disagree
|
Silicon Valley leaders think A.I. will one day fund free cash handouts. But experts aren’t convinced
|
https://www.cnbc.com
|
[
"Sam Shead"
] |
Artificial intelligence companies could become so powerful and so wealthy that they're able to provide a universal basic income to every man, ...
|
In this article META Follow your favorite stocks CREATE FREE ACCOUNT
Sam Altman, CEO of OpenAI. Patrick T. Fallon | Bloomberg | Bloomberg | Getty Images
Artificial intelligence companies could become so powerful and so wealthy that they're able to provide a universal basic income to every man, woman and child on Earth. That's how some in the AI community have interpreted a lengthy blog post from Sam Altman, the CEO of research lab OpenAI, that was published earlier this month. In as little as 10 years, AI could generate enough wealth to pay every adult in the U.S. $13,500 a year, Altman said in his 2,933 word piece called "Moore's Law for Everything." "My work at OpenAI reminds me every day about the magnitude of the socioeconomic change that is coming sooner than most people believe," said Altman, the former president of renowned start-up accelerator Y-Combinator earlier this month. "Software that can think and learn will do more and more of the work that people now do." But critics are concerned that Altman's views could cause more harm than good, and that he's misleading the public on where AI is headed. Glen Weyl, an economist and a principal researcher at Microsoft Research, wrote on Twitter: "This beautifully epitomizes the AI ideology that I believe is the most dangerous force in the world today." One industry source, who asked to remain anonymous due to the nature of the discussion, told CNBC that Altman "envisions a world wherein he and his AI-CEO peers become so immensely powerful that they run every non-AI company (employing people) out of business and every American worker to unemployment. So powerful that a percentage of OpenAI's (and its peers') income could bankroll UBI for every citizen of America." Altman will be able to "get away with it," the source said, because "politicians will be enticed by his immense tax revenue and by the popularity that paying their voter's salaries (UBI) will give them. But this is an illusion. Sam is no different from any other capitalist trying to persuade the government to allow an oligarchy." Beth Singler, an anthropologist at the University of Cambridge who studies AI and robots, told CNBC: "Overly relying on corporate taxes for human survival and flourishing has always seemed a mistake to me." She added: "Are we going to get Star Trek luxury space (pseudo) communism or Wall-E redundancy?"
Taxing capital
One of the main thrusts of the essay is a call to tax capital — companies and land — instead of labor. That's where the UBI money would come from. "We could do something called the American Equity Fund," wrote Altman. "The American Equity Fund would be capitalized by taxing companies above a certain valuation 2.5% of their market value each year, payable in shares transferred to the fund, and by taxing 2.5% of the value of all privately-held land, payable in dollars." He added: "All citizens over 18 would get an annual distribution, in dollars and company shares, into their accounts. People would be entrusted to use the money however they needed or wanted — for better education, healthcare, housing, starting a company, whatever." Altman said every citizen would get more money from the fund each year, providing the country keeps doing better. "Every citizen would therefore increasingly partake of the freedoms, powers, autonomies, and opportunities that come with economic self-determination," he said. "Poverty would be greatly reduced and many more people would have a shot at the life they want." Matt Clifford, the co-founder of start-up builder Entrepreneur First, wrote in his "Thoughts in Between" newsletter: "I don't think there is anything intellectually radical here ... these ideas have been around for a long time — but it's fascinating as a showcase of how mainstream these previously fringe ideas have become among tech elites." Meanwhile, Matt Prewitt, president of non-profit RadicalxChange, which describes itself as a global movement for next-generation political economies, told CNBC: "The piece sells a vision of the future that lets our future overlords off way too easy, and would likely create a sort of peasant class encompassing most of society." He added: "I can imagine even worse futures — but this the wrong direction in which to point our imaginations. By focusing instead on guaranteeing and enabling deeper, broader participation in political and economic life, I think we can do far better." Richard Miller, founder of tech consultancy firm Miller-Klein Associates, told CNBC that Altman's post feels "muddled," adding that "the model is unfettered capitalism." Michael Jordan, an academic at University of California Berkeley, told CNBC the blog post is too far from anything intellectually reasonable, either from a technology point of view, or an economic point of view, that he'd prefer not to comment. In Altman's defense, he wrote in his blog that the idea is designed to be little more than a "conversation starter." Altman did not immediately reply to a CNBC request for an interview. An OpenAI spokesperson encouraged people to read the essay for themselves. Not everyone disagreed with Altman. "I like the suggested wealth taxation strategies," wrote Deloitte worker Janine Moir on Twitter.
A.I.'s abilities
Founded in San Francisco in 2015 by a group of entrepreneurs including Elon Musk, OpenAI is widely regarded as one of the top AI labs in the world, along with Facebook AI Research, and DeepMind, which was acquired by Google in 2014. The research lab, backed by Microsoft with $1 billion in July 2019, is best known for creating an AI image generator, called Dall-E, and an AI text generator, known as GPT-3. It has also developed agents that can beat the best humans at games like Dota 2. But it's nowhere near creating the AI technology that Altman describes, experts told CNBC. Daron Acemoglu, an economist at MIT, told CNBC: "There is an incredible mistaken optimism of what AI is capable of doing."
| 2021-03-30T00:00:00 |
2021/03/30
|
https://www.cnbc.com/2021/03/30/openai-ceo-sam-altman-says-ai-could-pay-for-ubi-experts-disagree.html
|
[
{
"date": "2021/03/30",
"position": 78,
"query": "universal basic income AI"
},
{
"date": "2021/03/30",
"position": 82,
"query": "universal basic income AI"
},
{
"date": "2021/03/30",
"position": 82,
"query": "universal basic income AI"
},
{
"date": "2021/03/30",
"position": 83,
"query": "universal basic income AI"
},
{
"date": "2021/03/30",
"position": 82,
"query": "universal basic income AI"
},
{
"date": "2021/03/30",
"position": 83,
"query": "universal basic income AI"
},
{
"date": "2021/03/30",
"position": 81,
"query": "universal basic income AI"
},
{
"date": "2021/03/30",
"position": 82,
"query": "universal basic income AI"
},
{
"date": "2021/03/30",
"position": 83,
"query": "universal basic income AI"
},
{
"date": "2021/03/30",
"position": 83,
"query": "universal basic income AI"
},
{
"date": "2021/03/30",
"position": 87,
"query": "universal basic income AI"
}
] |
What do companies want to do with their employees about the AI ...
|
What do companies want to do with their employees about the AI skills gap? Replace or retrain?
|
https://community.deeplearning.ai
|
[] |
What do companies want to do with their employees about the AI skills gap? Replace or retrain? ... Although some of the biggest economies are ...
|
I was reading this very interesting report from Deloitte about “Talent and Workforce effects in the age of AI”. Out of the whole document, the following graph was very shocking for me
Although some of the biggest economies are missing (most notably India and Japan), it seems most employers prefer to replace the workforce by AI-skilled employees, as opposed to retrain.
What a motivation to finish (or re-do) the Deep Learning Specialization!
| 2021-04-10T00:00:00 |
2021/04/10
|
https://community.deeplearning.ai/t/what-do-companies-want-to-do-with-their-employees-about-the-ai-skills-gap-replace-or-retrain/221
|
[
{
"date": "2021/04/11",
"position": 68,
"query": "AI skills gap"
},
{
"date": "2021/04/11",
"position": 76,
"query": "AI skills gap"
}
] |
Driving Workforce Transformation with Machine Learning
|
Driving Workforce Transformation with Machine Learning
|
https://www.infoprolearning.com
|
[
"Infopro Learning"
] |
E-Learning enhanced by machine learning is an effective strategy for organizations looking to enable workforce transformation for new age ...
|
Solving Business Challenges with Human Capital Transformation Solutions
Human Capital, Human Capital Transformation, Transformational Strategy
3-Minute Read
The impact of Covid19 created significant changes in the way we work and communicate...
| 2021-04-19T00:00:00 |
https://www.infoprolearning.com/blog/machine-learning-enables-workforce-transformation/
|
[
{
"date": "2021/04/19",
"position": 56,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 55,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 39,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 57,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 58,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 57,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 55,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 38,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 41,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 55,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 39,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 64,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 62,
"query": "machine learning workforce"
},
{
"date": "2021/04/19",
"position": 62,
"query": "machine learning workforce"
}
] |
|
AI in the workplace: Why adoption remains low | UNLEASH
|
AI in the workplace: Why adoption remains low
|
https://www.unleash.ai
|
[
"Allie Nawrat",
"Chief Reporter",
"Allie Is An Award-Winning Business Journalist",
"Can Be Reached At Alexandra Unleash.Ai."
] |
However, of those that had implemented AI in their organizations, 74% said it made their employees happier, 82% said AI brought productivity, ...
|
Artificial Intelligence (AI) can be used in the workplace to automate boring, repetitive tasks that have become part of workers’ daily grind.
A survey of 700 individuals by Juniper Networks found that 95% thought their organization would benefit from embedding AI into their daily operations, products, and services. Also, 88% said they wanted to use AI as much as possible in their organization.
All of the survey respondents were involved in an organization’s implementation of AI and machine learning (ML) and were based in North America, Europe, and Asia Pacific.
However, there is a disconnect between the theory and the reality. With only 6% of the 163 C suite leaders surveyed reporting adoption of AI-powered solutions within their organization.
Also, only 22% of all 700 respondents said they used AI to automate or aid decision-making by their employees.
However, of those that had implemented AI in their organizations, 74% said it made their employees happier, 82% said AI brought productivity, and 71% noted the link between AI and the business’s operational efficiency.
So, given the positives that AI brings for companies and their staff, why is AI not being fully embraced in the workplace?
Explaining the workplace AI gap
The respondents told Juniper Networks that the reason for the disconnect was that many organizations were still figuring out the benefits of AI and ML, and that there were several challenges related to the practicalities of adopting these next-generation technologies.
The barriers these individuals reported included data and integration issues with their existing technology stacks – this is something that will require additional investment to be seamless.
40% of respondents noted that managing the convergence of AI with other technologies was a major challenge, while 58% noted that developing AI models and data sets that could be used across the company was a major issue.
Another issue is that the workforce is not yet fully on board on the advantages that AI tech brings to their daily lives.
While 73% of respondents said their organizations were struggling to prepare their workforce for AI integration, 41% also noted the challenge of training employees to work with AI systems, and 31% stated they were struggling to recruit workers already AI and ML trained.
The third, and final barrier, noted by Juniper was challenges in AI governance.
While 87% of C suite executives agreed that organizations needed to implement AI governance to mitigate risks, only 20% saw governance of AI as a key priority for their company. Just 6% said they had an AI lead overseeing the strategy and governance of AI adoption.
How to correct this AI gap
While companies reported they were mainly focused on bringing in AI-trained talent, Juniper advises that it is important that employers also make sure that the rest of their workforce understand AI and how it is being used in the organization.
To do this, companies need to invest in digital upskilling of their employees sooner rather than later.
Juniper Networks warns “the cost of inaction will be much worse”.
Juniper Networks also calls on companies to immediately work on policies and procedures for AI governance in order to mitigate risk. They recommend that companies “delegate responsibility and ensure it is cross-functional and covers the entire AI ecosystem and toolset”, “clarify the use of AI within your organization” and “have consistent standards and ethics across the enterprise”.
Sharon Mandell, Juniper Networks’ senior vice-president and chief information officer, concluded: “For AI, there is no doubt that there is light at the end of the challenge-filled tunnel and significant potential to generate even more meaningful and incredible outcomes than we’ve seen so far.
| 2021-04-28T00:00:00 |
2021/04/28
|
https://www.unleash.ai/artificial-intelligence/ai-in-the-workplace-why-adoption-remains-low/
|
[
{
"date": "2021/04/28",
"position": 73,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 68,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 71,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 83,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 86,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 86,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 69,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 68,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 72,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 67,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 69,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 68,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 72,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 87,
"query": "workplace AI adoption"
},
{
"date": "2021/04/28",
"position": 85,
"query": "workplace AI adoption"
}
] |
Will artificial intelligence take all our jobs or will it create more jobs ...
|
Will artificial intelligence take all our jobs or will it create more jobs for people?
|
https://forum.level1techs.com
|
[] |
AI and Machine learning are marketing phrases right now. Business are looking to replace mid to high salaries that are doing some low level in ...
|
Modern Artificial Intelligence has been taking jobs for over half a century. The main problem with appreciating the scope of job ‘losses’ is that “AI” keeps getting redefined to exclude the things that AI can do, and only include the things AI can’t yet do.
Once upon a time, the ability for a machine to pick up a fragile object without crushing it was deemed the domain of AI. Now machines flip burgers and rotate eggs in incubators, but that, somehow, is no longer considered AI. Countless other examples exist.
Funding isn’t as available for solved problems, so academics, researchers and doctoral students are always slanting their theses towards unsolved problems, tagging them as AI, and in-so-doing the definition of AI shifts slowly along with the titles of those theses.
Folks who do not appreciate that the current definition of AI is an ever-changing aspirational target will keep pushing dates further and further into the future. Like an oasis or mirage, it will therefore never be reached. “AI is over-hyped” is the sort of thing such people will say.
If, on the other hand, you simply consider the most basic definition of AI — something along the lines of “mechanised human thought” — then AI has existed for thousands, perhaps tens of thousands of years… and taking (or redefining) jobs every step of the way. An abacus is AI.
Historically, AI tended to redefine labour, instead of replace it. That notably changed during the Industrial Revolution. Since then outright replacement has grown as a fraction. With computing in the 20th century, replacement exploded. The limiting factor at the close of that century was that the vast majority of intelligence had to be explicitly programmed. That changed in the first two decades of this century.
Now we have “Machine Learning” as a field, and extraordinary advances have been and continue to be made. The thing that is different, this time in history, is that up until now we decided what the machines knew, and how they thought. We created them in our own image. Now the machines are learning for themselves. We increasingly do not know what they are thinking at any point in time. AI once clearly implied Artificial “Human” Intelligence. That is no longer the case. The AIs we are creating now are still artificial, undeniably more intelligent, but also decreasingly Human.
With all of that as a backdrop, if your period of interest is 2050-2100, then realise that unless you are on the cutting edge of machine learning, your current understanding of AI is almost completely outdated and irrelevant.
Society is increasingly (already almost blindly) trustful of AI. We believe the results calculators spew out. We turn right at the next intersection when the SatNav tells us to. We believe that the primary function of search engines is to help us find answers to questions. Unless that — for some unpredictable and doubtful reason — changes, we will increasingly submit ourselves to whatever AI decides for us.
When the AI was completely Human-like, the decision to trust what it did was a rational one. When the AI is no longer Human-like, does that decision remain rational?
“Will AI take jobs or create jobs?” was a good pre-2000 question. A better 2050-2100 question might be “What role, if any, does AI see for Humanity?”
The whole issue of jobs may be irrelevant if Humans have been deemed obsolete, and ‘goal-seeked’ to zero.
| 2021-05-11T00:00:00 |
2021/05/11
|
https://forum.level1techs.com/t/will-artificial-intelligence-take-all-our-jobs-or-will-it-create-more-jobs-for-people/172047
|
[
{
"date": "2021/05/11",
"position": 78,
"query": "AI impact jobs"
},
{
"date": "2021/05/11",
"position": 56,
"query": "artificial intelligence employment"
},
{
"date": "2021/05/11",
"position": 51,
"query": "artificial intelligence employment"
},
{
"date": "2021/05/11",
"position": 94,
"query": "artificial intelligence employment"
},
{
"date": "2021/05/11",
"position": 57,
"query": "artificial intelligence employment"
},
{
"date": "2021/05/11",
"position": 84,
"query": "future of work AI"
},
{
"date": "2021/05/11",
"position": 60,
"query": "artificial intelligence employment"
},
{
"date": "2021/05/11",
"position": 63,
"query": "AI employment"
}
] |
Survey Results - Impact Of AI/Machine Learning On Workforce ...
|
Survey Results - Impact Of AI/Machine Learning On Workforce Capability
|
https://elearningindustry.com
|
[
"Jeevan Joshi",
"Dr. Marina Theodotou",
"Sanjay Subbarao",
"Nick Bond"
] |
The survey highlights the effects of emerging technologies like AI/ML and what this will require of HR/L&D teams. We surveyed 65 Learning and Development and ...
|
The Impact Of AI/Machine Learning On Workforce Capability: Advanced HR/L&D Mindset And Skill Set
The Automation era which has been called the ‘fourth industrial revolution’ – is driving new ways of doing business often at the cost of traditional businesses. When it comes to emerging technologies, numerous companies including Facebook, Google, Microsoft, and many more are investing in Artificial Intelligence (AI) as it is said to be the future. With an increasing speculation of mass disruption of job roles, in the next 10 – 20 years, will there be anyone left for Learning and Development to train and develop? What does the future hold for HR/L&D professionals and how can we best adapt to the coming changes?
At LearningCafe and CapabilityCafe, we believe that the application of AI/ML will reshape the job market and will eventually create smarter jobs and roles that we can’t even imagine today. Reskilling the workforce and reforming learning and career models will play a critical role in facilitating this change.
Artificial Intelligence / Machine Learning: New Drivers Of Employment And Organizational Learning
We are on the brink of this new era, where humans and technology are so interconnected that we are unable to achieve outcomes without them. As AI continues to grab media headlines, we try to survey its practical use in business and workplace learning. Is the fear of Artificial Intelligence/Machine Learning (AI/ML) surpassing human capability justified? What are the uniquely human capabilities that only we can provide and what will this new world bring in terms of the creation of new roles for HR? How can L&D support the rapid and complex learning needs in this new automated environment?
Our recent survey on our webinar on Impact of Artificial Intelligence/Machine Learning on Workforce Capability highlights the effects of new and emerging technologies like AI/ML and what this will require of HR/L&D teams. Our survey also reflects some of the possible future implications of AI/ML assistance in our day to day work. We surveyed 65 Learning and Development and HR Professionals.
56% feel that AI/ML will substantially or moderately impact their job in the next 2 years.
The survey also found that 57% said they know very little about AI/ML and how it could change the way they do business.
55% feel AI/ML will partially replace HR/L&D, while 19% feel they could be completely over powered by it.
39% feel AI/ML will affect HR/L&D the most in tasks related to making recommendations about learning and job roles, whereas 27% feel AI/ML will supersede them in embedding learning in the workplace. While the stats naturally represent the impact of AI/ML on HR/L&D roles, it also predicts a decline in the number of professionals to be employed.
38% said experimenting with Al is the best way HR/L&D can tackle its influence, followed by 25% who felt teaming up with AI experts in the business is a better way forward.
Summary
Our survey shows that the advances in technologies like AI/ML have the potential to realign jobs and learning models which will have a direct impact on how HR/L&D functions.
The survey found that very few know what AI/ML can actually do and how it could disrupt or augment their jobs.
Our respondents think AI/ML assistance can be successfully integrated by collaborating with AI experts that know its practical application in the business. While others put a greater emphasis on experimenting with AI that encourages adaptability to newer technologies.
A majority of our respondents believe that AI/ML will partially or completely replace their roles, we think the future of HR/L&D will comprise of digitally smart professionals working hand in hand with AI/ML automated solutions.
Many respondents feel AI/ML will most likely control the analytics space by making recommendations or reporting on the recruitment and learning front. While few feel there could be a higher level of intelligence assistance on repetitive and time-consuming administrative tasks.
Shifting mindsets, re-skilling, learning and career development will play a critical role in facilitating this change. The question still remains: Will this be provided by the traditional internal Learning and Development team or some other model?
Originally published on September 30, 2017
| 2017-09-30T00:00:00 |
2017/09/30
|
https://elearningindustry.com/ai-machine-learning-on-workforce-capability-survey-results-impact
|
[
{
"date": "2021/05/12",
"position": 41,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 82,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 44,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 42,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 42,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 42,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 42,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 44,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 43,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 42,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 44,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 46,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 43,
"query": "machine learning workforce"
},
{
"date": "2021/05/12",
"position": 45,
"query": "machine learning workforce"
}
] |
The present and potential of AI in journalism - Knight Foundation
|
The present and potential of AI in journalism
|
https://knightfoundation.org
|
[
"John Keefe",
"Youyou Zhou",
"Jeremy B. Merrill",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img",
"Height Auto Max-Width"
] |
On May 13, 2021, Knight Foundation announced a new, $3 million initiative to help local news organizations harness the power of artificial intelligence.
|
On May 13, 2021, Knight Foundation announced a new, $3 million initiative to help local news organizations harness the power of artificial intelligence. Below, researchers outline an industry survey they conducted to help Knight understand the landscape.
Automation algorithms are all around us, detecting fraudulent use of our credit cards, determining what you see in your social media feed, and displaying shoe ads that follow you around online.
But how are news organizations using artificial intelligence, machine learning, and other algorithms for automation? We set out to survey the industry to help Knight Foundation understand the landscape and spot possibilities for future funding.
We collected 130 projects, focused primarily on projects done within the past three years. We drew from our own knowledge in data journalism and machine learning for journalists, as well as interviews, outreach into journalism-technology networks, examples described at conferences, and research done on the topic, including work done by JournalismAI project at the London School of Economics and by Jonathan Stray.
Augmenting reporting is a major focus
Almost half of the projects we surveyed used AI for “augmenting reporting capacity” These projects comb through large document dumps with machine learning, detect breaking news events in social media, and scrape Covid-19 data from government websites.
The second significant area AI is used in journalism is for “reducing variable costs.” That includes tools that automate the process of transcription, tagging of images and videos, and story generation. The category of projects that used AI for “optimizing revenue” — including dynamic paywalls, recommendation engines, and the digitization of a news organization’s archives — ranks third.
“Engagement” counts efforts to corral audience input, such as the algorithm KPCC / LAist used for sorting thousands of COVID-19 questions into manageable buckets. “Self-critique” includes work to foster gender and racial balance in an organization’s stories, and “news reporting” refers to situations where the result is the story, such as the real-time election needle at the New York Times.
It’s worth noting that some projects may cover a few different purposes; we chose the primary purpose based on what appeared to be the intent of the project. For example, a machine learning algorithm that combs through documents for keywords augments reporting capacity and at the same time reduces variable costs. We put that under “augmenting reporting capacity.” Understanding why newsrooms use AI can help both industry insiders and outside stakeholders identify the demands from the field.
Next, for the same AI projects surveyed, we looked into where they appeared in the news pipeline. Placing them in the whole picture of the news ecosystem helps reveal the spots for improvements.
From newsgathering to product development to subscriber acquisition and retention, newsrooms have used AI across the entire news production process. But as the following chart shows, when we talk about AI in newsrooms, we seem to lean heavily on the newsgathering part of the process and maybe do not pay as much attention to the product or the business side of the ecosystem.
Most AI projects happen at larger news organizations
Not surprisingly, national and global AI efforts far outpace those at the local level by simple project count. The larger organizations have more resources, in time, people, and money, to devote to innovation and experimentation. They also may have larger upsides for those investments.
AI projects require people with specific skills. But people we spoke to people at smaller news organization who have those capabilities — including newsroom and product engineers — who simply do not have the bandwidth. They must devote to their existing obligations.
Justin Myers, the data editor at The AP, said it was difficult to do AI projects in a big newsroom if the person with the skills was not specifically hired for that reason, not to mention for even higher hurdles for local newsrooms. “Finding someone with the time, skills and resources is hard for a newsroom. Finding a project where the level of effort pays off is hard.”
But the need for AI for local newsrooms is equally, if not more urgent.
The work AI can do is “work that reporters could do without machines, but it would take much longer. I see a big benefit of AI as the reallocation of resources, especially for smaller newsrooms,” said John Conway, vice president of WRAL Digital at Capitol Broadcasting Group.
A third of the projects at large newsrooms could be repeated for smaller ones
Software designed to automatically tag pictures may not be necessary for news organizations who don’t process thousands of images per day, but we estimated that 44 of the 130 projects surveyed could be adapted for smaller-scale use.
Here is a list of highly repeatable use cases of AI at the local level:
Templated sports, schools, real estate, and other stories.
Reporting tools including transcription services, entity extraction from documents, claim/fact identification, social media event detection.
Engagement helpers, such as the KPCC / LAist Covid-19 question sorting system.
Dynamic paywalls and subscriber prediction algorithms.
Recommendation engines.
Photo searching and tagging systems.
Homepage curation systems.
Self-critique systems, monitoring gender and racial bias in stories.
Many of the use cases fall into the realm of tools or systems that could operate almost invisibly inside a news organization’s subscriber or a content management system.
| 2021-05-12T00:00:00 |
https://knightfoundation.org/articles/the-present-and-potential-of-ai-in-journalism/
|
[
{
"date": "2021/05/12",
"position": 50,
"query": "AI journalism"
},
{
"date": "2021/05/12",
"position": 50,
"query": "AI journalism"
}
] |
|
The Role of Artificial Intelligence in the Future of Work - CDC Blogs
|
The Role of Artificial Intelligence in the Future of Work
|
https://blogs.cdc.gov
|
[
"Jay Vietas",
"Phd",
"Cih"
] |
Although research gaps exist regarding the use and impact of AI on the workforce, AI offers both the promise to improve the safety and health of ...
|
As discussed in a previous NIOSH Science Blog, artificial intelligence (AI) is in the process of transforming almost all aspects of society. Whether using an application to determine the best route to drive, receiving recommendations from Netflix on what to watch, or using face detection to logon to a personal smartphone, the use of AI is already very much part of modern living.
Specific to the workplace, work, and workforce, AI is fueling improvements in productivity and will likely be a significant influencer on the future of work. Whether using natural language processing to extract valuable information from volumes of reports [1], using models to predict supply needs [2], or using computer vision to recognize outputs or products [3-4], these tools are quickly becoming essential ingredients to developing a competitive edge in business today.
Although research gaps exist regarding the use and impact of AI on the workforce, AI offers both the promise to improve the safety and health of workers, and the possibility of placing workers at risk in both traditional and non-traditional ways. Occupational safety and health (OSH) professionals and practitioners, typically focused on specific physical, chemical, and biological hazards in the workplace, should be aware of the implications AI might have for the workforce.
In worker safety and health, AI offers the ability to take advantage of advances in sensors within the work environment [5-6]. The large data sets generated by these sensors can be used to improve exposure estimates and potentially predict adverse events in the workplace. Computers can be trained to learn patterns in images or video, enabling a form of AI described as computer vision. Computer vision has been shown to be useful in monitoring safety compliance [7-8], tracking workers in a particular area [9], and examining safety conditions on a particular job site [10]. Computer vision can also be layered over physical reality. Referred to as augmented reality, it can provide information to workers and OSH professionals, which can improve training and assist in reducing the impact of hazards in the workplace [11]. Computers can also be trained to process and analyze human language, also called natural language processing; such a tool has provided valuable information regarding fatality data in the mining industry [12], and could offer additional opportunities through the review of safety reports for OSH and allied professionals in the field.
In addition to the benefits of AI, there are also concerns regarding the use of this technology in the workplace. This is especially true if the data used are incomplete, inappropriate, or insecure; the methods are not easily explained or understood; or if the systems operate without the oversight of a human agent [13-14]. Integration of this technology, using systems which are predictable and reliable, has been shown to improve performance and acceptance [15]. The inverse also appears to be true, demonstrated by failures associated with the Maneuvering Characteristic Augmentation System (MCAS), an AI system designed to activate and assist the pilot under particular circumstances, which resulted in the two crashes of Boeing 737 Max airplanes [16-17].
To illustrate further, a preliminary report from the National Transportation Safety Board stated that a lack of information necessary to identify non-normal conditions, limited assumptions regarding pilot response, and transparent understanding of the operation of the MCAS contributed to both of these incidents [18]. Safety Board recommendations, which may be applicable for most AI systems, include the development of tools and methods to validate assumptions about pilot or operator recognition and response, consideration of the design and training to minimize the potential for safety impact, and improvement in the clarity of failure indicators to enhance timeliness and effectiveness of response of the pilot.
Simply put, there is a need to consider human interaction and response when implementing technological solutions. OSH professionals and practitioners should consider how the worker will interact with the tool; the decisions which may be made which encourage human action (or inaction); position(s) the worker must maintain; and the impact on schedules, number of hours worked, or even the potential to work alone. These possibilities should be evaluated to determine how they impact chemical, physical, and biological hazard exposures in the workplace, how they impact the mental health and well-being of the workforce, and if they generate new or unanticipated potential hazards.
In an attempt to promote human dignity, while minimizing potential risk, a variety of organizations developed recommendations for the responsible and ethical use of AI in society [19-21]. Such recommendations can help OSH professionals and practitioners engage with data scientists and computer programmers to develop AI systems applicable to the workforce, which are effective, explainable, accountable, secure, and fair.
Effective: Ensure AI is the right tool to address the problem/concern. Technology should be used to improve productivity or working conditions and should not be used haphazardly. While the improper use of a particular AI system may not directly cause harm, it may ultimately impact trust in other AI-based systems.
Ensure AI is the right tool to address the problem/concern. Technology should be used to improve productivity or working conditions and should not be used haphazardly. While the improper use of a particular AI system may not directly cause harm, it may ultimately impact trust in other AI-based systems. Explainable: Logic of, and decisions produced by AI should be communicated to stakeholders in a concise and useful manner. This is essential for mitigating risk and assessing impact of unintended, and potentially harmful, consequences.
Logic of, and decisions produced by AI should be communicated to stakeholders in a concise and useful manner. This is essential for mitigating risk and assessing impact of unintended, and potentially harmful, consequences. Accountable: Organizations and individuals should be accountable for the outcomes of the AI systems they develop and implement. For data scientists and computer programmers, accountability encourages an attached understanding of the systems created and the potential impact on others. Furthermore, if unexpected or safety incidents occur, the appropriate group or individuals can learn and improve from the incident.
Organizations and individuals should be accountable for the outcomes of the AI systems they develop and implement. For data scientists and computer programmers, accountability encourages an attached understanding of the systems created and the potential impact on others. Furthermore, if unexpected or safety incidents occur, the appropriate group or individuals can learn and improve from the incident. Secure: AI systems should be safe from outside interference. While cybersecurity is typically familiar for programmers, the potential consequence if the system is hacked or the data become corrupt should be an important safety consideration. Access to the data and the code used for the system should be known and based upon appropriate risk-benefit analysis. OSH professionals and practitioners should consider or lead these analyses.
AI systems should be safe from outside interference. While cybersecurity is typically familiar for programmers, the potential consequence if the system is hacked or the data become corrupt should be an important safety consideration. Access to the data and the code used for the system should be known and based upon appropriate risk-benefit analysis. OSH professionals and practitioners should consider or lead these analyses. Fair: AI systems should be aware of and appropriately address potential discrimination and bias. Systems, which are trained using one segment of the population may be biased and produce results which are different for another portion of the population. Evaluating and testing systems which guard against this premise can ensure safer outcomes and improve worker acceptance.
The possible uses of AI within the workplace are numerous and are expected to be a primary driver in defining the future of work. While the benefits are expected to be tremendous, the potential risks to worker health will continue to evolve along with the advances in technology. NIOSH will continue to research how AI may be able to assist the OSH community, advancing understanding of the origins of how AI may cause adverse health outcomes, and also improving the practical application of worker safety and health risk management in an increasingly AI-inhabited world.
Would you like to learn more about the impact of AI on tomorrow’s workforce? Join us on Thursday, June 17, 2021 from 1pm-2pm EDT for: The Role of Artificial Intelligence in the Future of Work. This free webinar, presented by the NIOSH Future of Work Initiative, Emerging Technologies Branch, and Artificial Intelligence Interest Group will feature Dr. Jay Vietas from NIOSH and Dr. Houshang Darabi from the University of Illinois-Chicago.
The Role of Artificial Intelligence in the Future of Work webinar is now available online.
Have you used AI in your workplace? We would like to hear about your experiences in the comment section below.
To learn more about the NIOSH Future of Work Initiative, please visit the NIOSH Future of Work Initiative website.
Jay Vietas, PhD, CIH, CSP, is Branch Chief of the Emerging Technologies Branch in the NIOSH Division of Science Integration.
References
| 2021-05-24T00:00:00 |
2021/05/24
|
https://blogs.cdc.gov/niosh-science-blog/2021/05/24/ai-future-of-work/
|
[
{
"date": "2021/05/24",
"position": 61,
"query": "artificial intelligence workers"
},
{
"date": "2021/05/24",
"position": 74,
"query": "future of work AI"
},
{
"date": "2021/05/24",
"position": 53,
"query": "artificial intelligence workers"
},
{
"date": "2021/05/24",
"position": 62,
"query": "artificial intelligence workers"
},
{
"date": "2021/05/24",
"position": 50,
"query": "future of work AI"
},
{
"date": "2021/05/24",
"position": 52,
"query": "artificial intelligence workers"
},
{
"date": "2021/05/24",
"position": 55,
"query": "artificial intelligence workers"
},
{
"date": "2021/05/24",
"position": 52,
"query": "future of work AI"
},
{
"date": "2021/05/24",
"position": 62,
"query": "artificial intelligence workers"
},
{
"date": "2021/05/24",
"position": 48,
"query": "future of work AI"
},
{
"date": "2021/05/24",
"position": 50,
"query": "artificial intelligence workers"
}
] |
4 successful examples of reskilling and upskilling programs
|
4 successful examples of reskilling and upskilling programs
|
https://eightfold.ai
|
[
"Eightfold Ai",
".Pp-Multiple-Authors-Boxes-Wrapper.Pp-Multiple-Authors-Layout-Inline.Multiple-Authors-Target-Shortcode .Pp-Author-Boxes-Avatar Img",
"Width",
"Important",
"Height",
"Border-Style",
"None",
"Border-Radius",
".Pp-Multiple-Authors-Boxes-Wrapper.Pp-Multiple-Authors-Layout-Inline.Multiple-Authors-Target-Shortcode .Pp-Author-Boxes-Meta A",
"Background-Color"
] |
... automation changes the nature of manufacturing jobs. Upskilling ... AI-specific reskilling initiatives. This puts both employees and ...
|
Upskilling is the process of teaching employees new skills or improving existing ones to adapt to changing job requirements, facilitate role advancement, or meet organizational goals. This is achieved through continuous learning and development, enabling employees to keep pace with evolving technology, industry trends, and workplace demands.
Reskilling involves training employees to take on new roles or responsibilities by teaching them a different set of skills than those they currently possess. This process typically occurs when an employee’s current role becomes obsolete or redundant due to changes in technology, business needs, or market trends. Reskilling allows employees to successfully transition to new positions within the organization.
The key difference between upskilling and reskilling lies in their focus and purpose. Upskilling aims to build new skills relevant to an employee’s existing role, while reskilling enables an employee to change career paths entirely.
For example, upskilling might involve a marketing professional learning advanced data analytics to remain competitive in digital marketing trends. In contrast, reskilling may involve a factory worker being retrained for roles in logistics or software support as automation changes the nature of manufacturing jobs.
Upskilling aims to build new skills relevant to an employee’s existing role, while reskilling enables an employee to change career paths entirely.
One of the biggest conversations happening in human resources right now is about how to build a workforce that is agile enough to adapt to the ever-changing world of work.
It’s a conversation that began before the COVID-19 pandemic due to increased automation in workplaces and the advent of remote work. The global health crisis and its impacts on labor supply and demand, however, have made it a primary focus for organizations trying to pave a path into an uncertain future.
“The future of work has arrived — sooner than many of us anticipated — and workers and businesses are being forced to adapt,” says Maria Flynn, president and CEO at Jobs for the Future. Many organizations have responded to this opportunity by building talent-management strategies that incorporate reskilling and upskilling employees, which Flynn asserts will help them succeed.
“The companies that will emerge from the COVID-19 crisis in strong positions are those that make investments in ‘future-proofing’ their workforce and provide their employees with upskilling opportunities to ensure they are resilient as the economy evolves,” Flynn says.
But with so much else going on in the world of work and the ongoing economic uncertainty many businesses are experiencing during the pandemic, is such an approach to talent management realistic? Is now the right time to implement strategies of worker upskilling and reskilling?
The short answer is yes. “Human investment is now the best investment we can make,” writes Tram Anh Nguyen, cofounder of the education platform, Centre for Finance, Technology, and Entrepreneurship. And employees are anxious for the opportunity to improve their skills for future opportunities at work.
Employees are ready for reskilling and upskilling. Organizations need to catch up.
Recognizing that their current skill sets may not take them into the future, workers have become anxious to close their skills gaps to maintain relevance in the workforce. “Millions of employees want to learn on the job,” write Kelly Palmer of Degreed and Aaron Hurst, founder and CEO at Imperative.
Companies are behind the curve when it comes to investing in learning and development programs for employees.
According to Deloitte’s 2020 Global Human Capital Trends Report, only 17 percent of respondent organizations have significantly invested in AI-specific reskilling initiatives. This puts both employees and companies at a disadvantage when it comes to maintaining relevance and succeeding in the future. In today’s economic climate, it is a business imperative to train current employees to close skills gaps in order to meet the future needs of the organization.
As Carol Patton at Human Resource Executive writes: “Reskilling or upskilling employees is no longer a trend but a survival strategy that fuels or sustains a company’s growth.”
Companies are holding back for many reasons. According to Siddhartha Gupta, CEO of Mercer Mettl, the issues that keep businesses from investing in reskilling and upskilling programs include:
identifying relevant skills gaps.
finding the time for employee training.
budgeting enough money for learning and development programs.
With the right plan in place, a company of any size can make reskilling and upskilling opportunities possible. Here are some lessons that can be learned from organizations that are paving the way in employee upskilling and reskilling initiatives.
Insights from companies with reskilling and upskilling programs
As technology progresses, the future of work will continuously evolve. Some companies have implemented reskilling and upskilling programs, and their efforts provide lessons for others as they create their own programs.
AT&T’s future-ready initiative focuses on personalized skills development paths
Upon examination, AT&T found that only about half of its employees had the STEM skills the company would require of its workers in the future. The company realized it had to quickly solve this problem.
Bill Blase, senior executive vice president of human resources at AT&T, says the company had two choices. “We could go out and try to hire all these software and engineering people and probably pay through the nose to get them, but even that wouldn’t have been adequate. Or we could try to reskill our existing workforce so they could be competent in the technology and the skills required to run the business going forward.” It chose the latter path.
It’s a billion dollar, multi-year investment that includes elements almost any company can develop. The initiative focuses on collaborations with online education platforms to offer employees online learning opportunities. It includes personalized learning experiences in a career portal that helps employees plan their future and identify skills they need to learn.
Building online training programs and career portals are two upskilling and reskilling tactics that even small companies can employ to ensure their workforces are prepared for the future.
PricewaterhouseCoopers’ new world, new skills initiative focuses on access and collaboration
In 2019, PwC announced a $3 billion investment in job training for all employees. While there are a number of different elements to the initiative, the Digital Fitness app and the Digital Lab are two pieces that stand out.
The Digital Fitness app allows PwC employees to assess their digital knowledge and create customized learning plans. Through the app, they receive learning assets to “help our people think differently and unlock their innovative creativity at scale,” Joe Atkinson vice chair, chief products and technology officer at PwC, writes.
The Digital Lab allows employees to collaborate and share innovative solutions. “Digital Lab is a democratized platform, which uses social and gamification features to incentivize building and sharing of assets with wide applicability,” explains Sarah McEneaney, digital talent leader at PwC U.S. Through the platform, employees not only learn from one another but also apply their new skills.
This collaborative approach can be used by any size company to give employees access to more resources and help them build the skills they need to succeed.
Accenture’s connected learning platform gives employees control of learning
Accenture CEO Julie Sweet says the company has invested nearly $1 billion in 2021 on millions of hours of training to reskill its workforce. Central to the initiative is the company’s Connected Learning Platform which is a blend of classroom and digital learning opportunities with content from internal and external subject matter experts.
“Our people learn best by connecting, collaborating, and practicing for the scenarios they will encounter in their work with our clients,” says Ellyn Shook, chief leadership and human resources officer for Accenture. “From basic skills to industry-specific content, learning is available to all our people anywhere, anytime – and, in many cases, no selection or approval is involved. Simply tap the app and start learning.”
In this way, employees control their own learning and career development. Such platforms don’t have to be built to offer thousands of classes in hundreds of roles. Workers in any position at any size company would benefit from autonomous learning opportunities.
Amazon’s upskilling 2025 initiative includes apprenticeships
Amazon launched its Upskilling 2025 initiative as part of its commitment to prepare workers for a more digitized workplace in the future. “We think it’s important to invest in our employees to help them gain new skills and create more professional options for themselves,” says Beth Galetti, senior VP of People Experience and Technology at Amazon.
One of the many opportunities offered through the initiative is the Mechatronics and Robotics Apprenticeship Program. In the two-phase program, employees attend classes and receive on-the-job training in preparation for work as mechatronics and robotics technicians. When done, they will then be poised to earn more money and secure better career opportunities.
While external apprenticeships may not be feasible financially for all companies, internal mentorship or apprenticeship programs can be cost-efficient ways to upskill employees. By learning on the job, employees learn the practical applications of new skills.
Images by: Michal Bednarek/©123RF.com, jamesteohart/©123RF.com, Sergey Nivens/©123RF.com
| 2021-06-01T00:00:00 |
2021/06/01
|
https://eightfold.ai/blog/reskilling-and-upskilling/
|
[
{
"date": "2021/06/01",
"position": 87,
"query": "reskilling AI automation"
}
] |
Machine Learning Can Teach Your Workforce
|
Machine Learning Can Teach Your Workforce
|
https://www.aimltechbrief.com
|
[
"Scott Koegler",
"Aiml Tech Brief",
"Deborah Huyett",
"Robert Agar",
"Craig Gehrig"
] |
Artificial intelligence and machine learning are making their way into the business in a big way. Every time you speak to the executives of ...
|
Artificial intelligence and machine learning are making their way into the business in a big way. Every time you speak to the executives of big or small companies, you will not end the conversation without hearing their plans for investment in machine learning (ML) or artificial intelligence (AI). One of the areas that have benefited immensely from ML/AI is human resource management. Although this sector has experienced significant changes in the past few years due to the evolution of technologies, arguably, none of them has transformed HR like AI and ML. Here are some ways that artificial intelligence and machine learning are helping in human resources:
Reduces bias in appraisals
The biggest challenge of any human resource manager during performance appraisals is to appraise employees without bias. This is often difficult because humans are naturally biased. With the help of AI/ML algorithms, analysis can be done beyond the usual spreadsheets through the execution of employee assessments via continuous appraisals. The AI/ML algorithms can be used to estimate the employees’ career paths and prepare them for the advancement of their careers.
Estimating employee morale
The human resource industry is leveraging AI and ML in the identification of employee patterns over time. Technologies such as facial recognition are capable of measuring employee emotions on a particular scale and even differentiating gender. The reports and data gathered can be used to develop solutions by deriving insights and acting on them. The insights can lead to the development of strategies to boost employee morale and enhance their potential in places of work.
Skill management
Machine learning is showing high potential in enhancing the management and development of individual skills. While this area is still in its initial stages, AI-based platforms can be calibrated to guide the development and management of individual employees without the intervention of human coaches. This not only saves time but also provides the opportunity for more people to be managed to grow their careers and remain engaged all the time. An example of the use of ML in skill management is Workday, a company that builds personalized training recommendations for employees based on the needs of the organization and the specifics of an employee. With machine-based feedback, individuals can learn a lot and grow into their jobs.
Streamline hiring processes
The hiring process is often difficult and employers end up getting candidates who are not up to their set standards. However, with AI/ML, every stage of the hiring process is enhanced by availing data given by personalized research tools. These tools and data allow organizations to find the best talent in the industry with the specific traits that meet employer’s desires. An applicant tracking software can help HR recruiters by analyzing numerous resumes and reducing the ambiguities encountered during recruitment. The AI-based software can analyze several resumes based on specified keywords, location, skills, and the applicant experience.
Payroll processing
Payroll processing is one of the complex tasks that HR managers have to face regularly in their operations. With the help of ML and AI, payroll processing and employee expenses management can be taken care of with the help of HR bots. With these bots, you may not need to spend too much time filling many forms that are necessary for the documentation of business expenses. Rather, you can just notify the bot, which will process everything and get your bills approved by your manager. This simplifies work for the HR personnel and enhances accuracy.
Artificial intelligence and machine learning technologies are proving crucial for operating businesses. For the human resource aspect of the organization, it allows organizations and HR teams, in particular, to easily manage various day-to-day processes, overcome hurdles and assure operational efficiency.
| 2021-06-21T00:00:00 |
https://www.aimltechbrief.com/index.php/machinelearning/item/7156-machine-learning-can-teach-your-workforce?rCH=-2
|
[
{
"date": "2021/06/21",
"position": 85,
"query": "machine learning workforce"
}
] |
|
Jobs Lost to Automation Statistics: Is Your Job on the List?
|
Jobs Lost to Automation Statistics: Is Your Job on the List?
|
https://whattobecome.com
|
[
"Dunja Funduk"
] |
In the last 15 years, 2% of people in the US have lost their jobs due to automation. This figure might look small, but in real numbers, it is ...
|
New machines in the 21st century can create a problem with unemployment, as the people that lost their jobs need to find another vocation. Jobs lost to automation statistics show that some jobs disappear entirely, while others are just being reshaped under new conditions.
Researchers disagree on how automation will affect long-term employment. Some predict that the unemployment rate will rise, while others think the new technologies will bring new business opportunities and create jobs that didn’t exist before.
Automation and artificial intelligence are making progress on a daily level. New machines and computer programs are making jobs faster and easier, and we will see the massive impact of automation on employment in the years to come. Millions of people have already lost jobs in the USA.
So, what are predictions?
Read on.
Crucial Automation and Job Loss Statistics – Editor’s Choice One-fourth of jobs in the US are in danger of automation
The number of robots doing human jobs increases by 14% each year
Workers at junior positions face the biggest threat of automation
By 2030, artificial intelligence is expected to add $15 trillion to the world economy
55% of the jobs that don’t require a bachelor’s degree are in jeopardy of automation
One-third of jobs could no longer exist in the next 25 years
55% of workers think automation will boost their productivity
Storage, manufacturing, and transportation are facing the biggest chance to be fully automated
1. Over 25% of Americans feel threatened by automation.
Most Americans say they feel threatened by new technologies. Research shows that 35% of all jobs could become obsolete by 2030. Speaking in numbers, 57 million employees might face job loss.
2. Almost 40% of people fear losing their jobs because of mechanization.
Automation and job loss statistics show that people are more and more concerned about losing their jobs to automation or robots. The number of worried workers has increased by 4% since 2014.
People think that every business is at risk, not just the jobs that don’t require education. However, the majority of them also think robotics and automation could never replace a human being.
3. Every year, 14% more robots start performing some human jobs.
When talking about jobs lost due to technology, we can’t ignore the increased utilization of robots in almost every industry. New discoveries speed up this course, and the predictions are that the number of robots will go up even faster.
4. More than half of employees think the primary goal of automation is productivity growth.
When it comes to automation and jobs, almost 60% of workers think that automation will help them be more productive. Also, analysis shows that business owners want automation to improve their employees’ productivity, reduce risks, and avoid mistakes. Only 11% of employers want to use new inventions to replace human employees.
5. 70% of employees would work on themselves to upgrade their professional skills.
Data on jobs at risk of automation reveal that people consider improving their skills just to retain their jobs. 70% of people are willing to do whatever it takes to stay as competitive as robots.
6. 55% of workers will need upskilling or reskilling to keep their jobs.
Technological improvements don’t only affect automation and job loss, but also people. More than half of workers will have to gain new skills, and almost 35% will have to take courses. Many will have to significantly improve their skills to retain jobs.
7. Three industries are facing the chance to be fully automated – storage, transportation, and manufacturing.
No one can precisely tell how many jobs will be lost to automation, but research from 2017 suggests three waves of automation will influence various industries. Some industries are at greater risk than others. For example, one of the waves will be the period when improved computer programs outperform people in data analysis.
The augmentation and the independence rush will hit industries such as storage and transportation, and the same thing will happen to manufacturers. The construction industry is one of the jobs at risk of automation, as well.
8. AI will most likely add $15 trillion to the world economy by 2030.
The GDP growth is predicted to be vast. It will increase by 25% in China and 14% in North America. A big part of the profits will come from product improvements, and automation will increase the affordability and attractiveness of the product, as AI replacing jobs statistics show.
9. 55% of vocations that don’t require a college degree could easily be automated.
Administration jobs and manufacturing will proceed to be at the greatest risk of mechanization in the next few years. All the positions that don’t require a college degree are at high risk, as well.
Robots will do one-fourth of these jobs. Also, almost 80% of food preparation jobs could be automated. Jobs lost to automation statistics say these jobs are in sharp contrast to careers such as psychology or education.
10. Rural parts of Europe and America are at a high risk of job loss. Alabama and Arkansas show the potential risk could be higher than 65%.
Big cities on the east and west coast of the US are booming because the IT sector is located there. Automation and artificial intelligence will most likely bring new job opportunities for the people in these areas.
At the same time, states in the heart of the US will face the risk of job loss due to automation because people in rural areas won’t be able to find a new vocation as easily. On top of that, they mostly work in industries like transportation and agriculture.
11. By 2022, jobs done by humans could drop by more than 10%.
Nowadays, people do 70% of all tasks, while robots do the rest. If these unfavorable unemployment trends continue, predictions are that humans will be doing less than 60% of the work in the future.
Automation replacing jobs will be evident, especially in information technology. What’s more, it will take one share of communicating and coordinating.
12. 17% of the jobs held by women and 24% of jobs held by men are in danger of automation.
This gap is so great because more men work in production, manufacturing, or transportation. Men dominate industries such as building and installation, and they are at the highest automation risk level. Most women work in professions that are at low risk of automation.
13. Workers aged 18 to 25 are at a 50% automation risk.
Junior positions are the easiest to be replaced. Older workers (up to 54 years of age) face a 40% automation risk. Younger workers perform highly repetitive tasks. They often work in the food service and preparation industry, which is highly rated by the jobs automation risk calculator.
Young workers make up 10% of America’s workforce, but they hold 30% of food prep and service jobs, and robots can easily replace them.
14. One-third of current jobs didn’t exist 30 years ago.
Automation doesn’t always entail job loss or low productivity of human workers. Sometimes, it leads to automation job displacement. Workers also tend to think that automation will bring them new job opportunities and higher salaries.
Employees are willing to learn new programs and acquire new skills if that means they will find a new job easily. On the other hand, economists think modern automation is different and that its main goal is to displace rather than improve humans’ work. That means there will be more jobs lost to automation by 2030.
15. Robots could take over 20 million jobs in the next decade.
Each robot can do what 1.6 human workers can. That means that millions of employees could be replaced in the next ten years. Regions that are marked as low-income areas will experience a greater impact of automation than high-income regions.
16. Purchases of medical robots increase by 50% every year.
By 2050, the need for investment in healthcare will increase. Robots that work in medical services are now worth $2 billion.
17. Automation can boost global GDP by 1.5% every year.
Although jobs lost to automation statistics show that many workers could lose their jobs, it can positively influence the global GDP. However, employees who lose their job would need to be employed again to sustain long-term economic growth.
18. Seven million people in America lost their jobs in the last 15 years due to automation.
When automation started influencing production, citizens gained access to cheaper products, while large workforce sectors were left jobless. Once they can find new jobs, they face a reduction in wages of up to 30%, automation and job loss statistics show.
19. Two-thirds of workers think automation will bring them better jobs.
The International Federation of Robotics found that, even though the number of jobs lost by automation keeps rising, people are still very optimistic about the benefits it could bring. The survey found out that workers that are at the greatest risk hope to get better employment.
Jobs Lost to Automation Statistics — Conclusion
Automation is one of the most powerful forces that have been changing and reshaping the modern world. Every similar change has brought great disturbances to how the market works and the sort of businesses that people do.
Artificial intelligence and robotics will eliminate some jobs and create a better work environment for everyone. People will be educated in fields such as computing and design. Robots will take over manual labor, and people will do jobs that require more critical thinking and planning.
Frequently Asked Questions (FAQ)
| 2021-06-22T00:00:00 |
2021/06/22
|
https://whattobecome.com/blog/jobs-lost-to-automation-statistics/
|
[
{
"date": "2021/06/22",
"position": 49,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 48,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 50,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 48,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 49,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 51,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 47,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 51,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 49,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 49,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 48,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 48,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 49,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 52,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 49,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 53,
"query": "job automation statistics"
},
{
"date": "2021/06/22",
"position": 51,
"query": "job automation statistics"
}
] |
Automation Helped Kill up to 70% of the US's Middle-Class Jobs
|
The heart of the internet
|
https://www.reddit.com
|
[] |
In other words, 70% of the US's middle-class workers grinding through easily-automated tasks have been freed to create greater value for society ...
|
A subreddit devoted to the field of Future(s) Studies and evidence-based speculation about the development of humanity, technology, and civilization. -------- You can also find us in the fediverse at - https://futurology.today
Members Online
| 2021-06-24T00:00:00 |
https://www.reddit.com/r/Futurology/comments/o729wb/automation_helped_kill_up_to_70_of_the_uss/
|
[
{
"date": "2021/06/24",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2021/06/24",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2021/06/24",
"position": 17,
"query": "automation job displacement"
}
] |
|
Automaker factory robotics: What it means for jobs and electric ...
|
Automaker factory robotics: What it means for jobs and electric vehicle production -
|
https://greenautomarket.com
|
[
"Prof Mateu Turró",
"Chuck Parker",
"Joel Pointon"
] |
Over the next decade, the US may lose more than 1.5 million jobs to automation. The number of robots currently in the global workforce, 2.25 ...
|
The use of robotics in vehicle manufacturing will continue to grow at a fast enough pace to speed up production — and to remove quite a lot of jobs. Of course, job loss is nothing new in auto manufacturing where downsizing plants and moving some of them overseas has been taking place since the 1980s.
For the auto industry, it all started with General Motors testing out prototype spot welding robots in 1961. By the 1980s, billions of dollars were being spent by automakers worldwide to automate fundamental tasks in their assembly plants. Automation system deployment did decline in the 1990s, but innovative technology did help it to rebound in the next decade.
Today, it’s a common part of factories, and it’s starting to become another revenue source for automakers through providing robotic services to other companies. These companies are selling the advantages of protecting workers from injuries and making factories more efficient and streamlined by bringing in the best of robotics. There’s also the point about making the job less repetitive and boring for workers, which could also help improve retention.
At TC Sessions: Mobility 2021 earlier this month, three auto executives spoke to the issues. Max Bajracharya of Toyota Research Institute, Mario Santillo of Ford, and Ernestine Fu of Hyundai described how their companies are now viewing the technology. It’s not about the auto industry, as much it its for these companies to make names for themselves — and clientele — in the robotics sector.
“I think all automakers are recognizing that there won’t be the automotive business in the future as it is today,” Bajracharya said. “ A lot of automakers, Toyota included, are looking for what’s next. Automakers are very well positioned to leverage what they already know about robotics and manufacturing to take on the robotics market.”
Yet on the factory job front, there still are expectations for machines replacing humans. A 2019 report from Oxford Economics estimates that about 8.5 percent of the global manufacturing workforce stands to be replaced by robots, with about 14 million manufacturing jobs lost in China alone out of the 20 million projected to be displaced by 2030. Over the next decade, the US may lose more than 1.5 million jobs to automation. The number of robots currently in the global workforce, 2.25 million, has multiplied threefold over the past 20 years, doubling since 2010. Of course, these statistics go far beyond automakers with manufacturing including computers, consumer electronics, clothing, parts and components, packaged food, and other segments.
Four companies dominate the general industrial robotics market: Fanuc, Yaskawa, Kuka, and ABB. Automakers sometimes work with more than one of them, and other partners in automation.
There’s a correlation being made by automakers between robotics and EVs — through building and converting more factories into electric vehicle production and robotics playing an integral role. The connection seems to be more about electric autonomous vehicles and mobility. Robotic manufacturing might be included in the campaign they’re describing, at least for a few automakers.
Here’s a look at where all that’s going, starting with the big question: Will robotics take a leap forward, transforming vehicle manufacturing plants and upending the workforce?
BMW: The German automaker is betting on selling autonomous mobile robotics (AMRS) to the logistics sector. That will be through its Industry-Driven Engineering for Autonomous Logistics unit, which abbreviates to IDEAL and has the formal name of IDEALworks (IW). BMW Group started this unit in late 2020. The company has been partnering with Nvidia to develop mobile robots for internal use in their factories, primarily around automated material handling at the last mile, for a number of years. IW builds on this internal development and expands the scope to include autonomous robots in the logistics sector, and that could expand to couriers, 3PLs (third-party logistics), retailer stores, and online retailers.
The robot deployed is referred to as the small transport robot (STR) and is equipped with a Lips 3D camera and Sick sensors for safety. All robots rely on the Nvidia AGX hardware and makes significant use of NVIDIA’s SDK. BMW hopes to relieve employees from mundane and repetitive tasks so they can do better in their core competencies.
A year ago, the company confirmed it will cut about 6,000 jobs in Germany in an effort to cut costs as the automotive sector continues to struggle to recover from the Covid-19 outbreak. The German automaker and its works council agreed the workforce reduction will be achieved via a mixture of redundancies, early retirements, and not renewing temporary contracts, along with not filling new vacancies. It’s the first time since the financial crisis of 2008 that the company has had to cut staff. The company also tied the cuts to expanding focus on electric mobility and autonomous driving while boosting corporate efficiency.
BYD: On March 2, Beijing Horizon Robotics Technology R&D Co., Ltd. (Horizon Robotics), an AI chip supplier, held a strategic cooperation signing ceremony with BYD Co., Ltd., at BYD’s Shenzhen headquarters.
Horizon Robotics, a five-year-old company specializing in AI chips for robots and autonomous vehicles, sees huge potential in automotive partnerships. Horizon’s OEM and Tier 1 auto partners, according to the firm, include Audi, Bosch, Continental, SAIC Motor and BYD. Based on its own deep chip and intelligent technology accumulation, BYD says it will be cooperating with Horizon’s leading artificial intelligence chips and algorithms. It gives BYD a leverage point for adding AI, robotics, and automated vehicles, into its catalogue tied to electric vehicles and advanced batteries.
FCA: Fiat Chrysler Automobile’s robot unit Comau was spun off before the merger with PSA, for the benefit of all shareholders of the combined company. The Jan 19, 2021, $52 billion merger between FCA and PSA Group created Stellantis, now the fourth-largest automaker in the world. Comau is an Italian industrial automation company specializing in processes and automated systems that improve corporate manufacturing production through four core offerings: Controls; Teach Pendant with its ergonomic human robot interface; Auxiliary Equipment enabling equipment for increased functionality; and Software, offering digital tools to enhance processes.
Comau considers itself to be a leading company in the industrial automation field on the global playing field. The full portfolio includes: joining, assembly and machining solutions for traditional and electric vehicles, robotized manufacturing systems, a complete family of robots (including collaborative and wearable robotics) with extensive range and payload configurations, autonomous logistics, and asset optimization services with real-time monitoring and control capabilities. Tesla just because one of its clients this year.
Ford: In April, the company announced that at its transmission plant Livonia, Mich., where robots help assemble torque converters now includes a system that uses AI to learn from previous attempts how to make the production process more efficient. Inside a large safety cage, robot arms wheel around grasping circular pieces of metal, each about the diameter of a dinner plate, from a conveyor and slot them together.
Ford uses technology from a startup called Symbio Robotics that looks at the past few hundred attempts to determine which approaches and motions appeared to work best. A computer sitting just outside the cage shows Symbio’s technology sensing and controlling the arms. The enhanced automation allows this part of the assembly line to run 15 percent faster, a significant improvement in automotive manufacturing where thin profit margins depend heavily on manufacturing efficiencies, Ford said.
General Motors: General Motors announced late last year that Factory ZERO, Detroit-Hamtramck Assembly Center, the company’s all-electric vehicle assembly plant, is the first automotive plant in the US to install dedicated 5G fixed mobile network technology. Verizon’s 5G Ultra Wideband service is operating now at Factory ZERO, with its exponential increases in both bandwidth and speed supporting the ongoing transformation of the plant as it prepares to begin producing EVs in 2021.
It offers considerably faster download speeds and greater bandwidth than 4G networks. Factory ZERO is being completely retooled with a $2.2 billion investment, the largest ever for a GM manufacturing facility. Once fully operational, the plant will create more than 2,200 good-paying U.S. manufacturing jobs, the company said.
General Motors embraced smart manufacturing in 2018 through its Zero Down Time robot program in partnership with Japan’s Fanuc. Dan Grieshaber, GM’s director of global manufacturing integration, recently told Automotive News that the program includes 13,000 robots across GM’s 54 global manufacturing plants. The robots upload their data to Fanuc where the results are measured against GM’s performance expectations.
GM is using the system to troubleshoot maintenance issues and other quirks before they become serious. Another goal: helping prevent fatigue for workers that use repetitive motion. The sensors, actuators and tendons — comparable to the nerves, muscles and tendons in a human hand — increase dexterity for the worker. GM also uses collaborative robots or “cobots” that can operate around the human workforce without a safety cage.
Hyundai: In December, Hyundai Motor Group and SoftBank Group Corp. agreed on a transaction that placed Hyundai at an 80 percent controlling interest in Boston Dynamics in a deal that values the mobile robot firm at $1.1 billion. The deal came as Hyundai Motor Group envisions the transformation of human life by combining world-leading robotics technologies with its mobility expertise.
The two owners hope it will establish a leading presence in the field of robotics, and it will mark another major step for Hyundai toward its strategic transformation into a Smart Mobility Solution Provider. The Korean company said that it has invested substantially in development of future technologies, including autonomous driving technology, connectivity, eco-friendly vehicles, smart factories, advanced materials, artificial intelligence (AI), and robots.
Boston Dynamics produces mobile robots with advanced mobility, dexterity and intelligence, enabling automation in difficult, dangerous, or unstructured environments. The company launched sales of its first commercial robot, Spot, in June of 2020 and has since sold hundreds of robots in a variety of industries, such as power utilities, construction, manufacturing, oil and gas, and mining.
Nissan: As Nissan prepares to build a new generation of electrified, intelligent and connected cars, the company is making a series of investments to upgrade its production technologies and facilities, but the company is emphasizing the benefits that will come to employees more than cutting costs. Nissan’s mission is improving efficiency in terms of preventing mistakes, maintaining quality, ensuring that workers are freed from monotonous tasks, and reducing strain and fatigue from work.
One way to make improvements will be choosing when to automate. Certain assembly line processes are best suited for robots, particularly if they’re simple and repetitive yet relatively strenuous for humans. Another area of focus will be on industrial robots that work on things like welding and assembly are ordinarily kept in cages for safety reasons, due to their size, strength and speed of movement. Cobots (collaborative robots) seem to the answer here, for manufacturing processes where people and machines need to work closely together. Cobots offer robotic arms with limited strength and speed of movement. In addition to being extremely nimble, they can be easily reprogrammed to learn new tasks, Nissan said.
Tesla: Tesla and Stellantis-owned Comau are setting up a new series of automation equipment for manufacturing at Tesla’s Fremont Factory in Northern California. According to permits submitted by Tesla to the City of Fremont, Tesla will begin to anchor and install Comau’s products that entail highly automated and effective manufacturing techniques that are designed for electric vehicles.
Before Tesla started building its Model 3 compact sedan in 2017, CEO Elon Musk laid out a vision for its Fremont, Calif., assembly plant to become the factory of the future. But Musk had to learn similar lessons that what General Motors tried in the 1980s. GM saw its efforts backfire, as robots sprayed paint on each other and welding machines damaged vehicle bodies. Tesla’s efforts met a similar fate, as Model 3 production got off to a much slower start than the company had predicted. The delays were severe, and Musk later admitted he was wrong for trying to lean so heavily on automation. But the challenges persist, according to current and former Tesla employees. Mechanical problems are continuing at the Fremont plant, but this time are not cutting off production targets.
Toyota: Toyota has been developing industrial robots since the 1970s and has been bringing them into their manufacturing systems to improve quality and reduce costs. Robots are primarily used in their welding, painting, and assembly processes. In recent years, everything has been shifted over to Toyota Research Institute (TRI). Most recently, TRI has been refining its technology and service to be applied to the home. As societies age, there will be huge demand for increased caregiving, systems that enable us to live independently longer, and assistance for an increasingly aging workforce, the company said. Robots and automation can play a key role in freeing up people to spend more time with family, assisting people with tasks they enjoy, or helping them perform work for their jobs.
It will be drastically different than the machines Toyota has set up to make its factories more efficient. Here’s where machine learning and artificial intelligence (AI) methodology come to play. To address the diversity a robot faces in a home environment, TRI teaches the robot to perform arbitrary tasks with a variety of objects, rather than program the robot to perform specific predefined tasks with specific objects. In this way, the robot learns to link what it sees with the actions it is taught. When the robot sees a specific object or scenario again, even if the scene has changed slightly, it knows what actions it can take with respect to what it sees.
Volkswagen: Speaking of the aging global population, Volkswagen plans to use robots to cope with a shortage of new workers caused by retiring baby boomers. According to the company, the move to a more automated production line would ensure car manufacturing remains competitive in high-cost Germany. Similarly to other manufacturing outlets, VW predicts many of its workers will retire between now and 2030. Plus, a lack of skilled employees joining the business is forcing the company to look for alternative solutions.
The German automaker is also moving forward on automation at its electric vehicle plants. VW Passenger Cars and VW Commercial Vehicles divisions have ordered more than 2,200 new robots for the planned production of EVs at the German plants and the US plant in Chattanooga, Tenn. The company ordered more than 1,400 robots from the Japanese manufacturer Fanuc for their production facilities in Chattanooga, and from Emden in Germany. Volkswagen Commercial Vehicles is purchasing 800 or more robots from the Swiss manufacturer ABB for the carmaker’s Hanover, Germany, plant. The robots will be primarily used in body construction and battery assembly.
Volkswagen had been doing a lot of business with Kuka, bringing in thousands of robots to its plants all over the world. There’s been speculation that since Kuka was taken over by the Chinese technology group Midea in spring 2016, Volkswagen has been trying to become more independent of the robot specialist based in Germany. But the company has rekindled its relationship, including giving part of the production duties over to Kuka for its ID Buzz electric vehicle.
And in other news……….
Supercharger network opening up: Tesla will be breaking one of its golden rules: don’t let anybody beyond Tesla owners use its charging network. The company has told Norwegian officials that it plans to open the Supercharger network to other automakers by September 2022. A decade after deploying the first Supercharger, Tesla now has over 25,000 Superchargers at over 2,700 stations around the world. But opening it up has been in the works. Last year, CEO Elon Musk said that Superchargers are now being used “low-key” by other automakers.
A German official recently announced that they have been in talks with Tesla to open up the network to other automakers. In Norway, Tesla wants incentives from the government to open up its Supercharger network. Government officials confirmed that
Tesla told them that it plans to open the Supercharger network to other automakers by Sept. 2022, and they will approve the incentives as long as Tesla goest through with the initiative.
It would be viable for Tesla to open its chargers throughout Europe, where its Supercharger network uses the CCS connector, which is standard in the region. However in North America, Tesla would have to offer an adapter since it uses a proprietary plug on its vehicles and charging stations in that market.
Ford and Argo report on AV improvements: Ford just released more details on its self-driving vehicle development, the first time since its 2018 safety report to the US Dept. of Transportation. In addition to working with Argo AI to advance the development of a robust Automated Driving System to guide Ford vehicles on roads, the automaker continued to research and develop an improved customer experience, fleet management capabilities, behind-the-scenes transportation-as-a-service software, and more.
In addition to Miami, Ford plans to launch its self-driving service in Washington, D.C., and Austin, Texas. In all three of these cities, Ford established robust testing and business operations, including terminals and command centers to manage these fleet of vehicles as they transport people or deliver goods. Ford’s newest self-driving test vehicles are built on the Escape Hybrid platform, taking advantage of increased electrification capabilities and featuring the latest in sensing and computing technology. The Escape will be utilized to initially launch the service with.
Alongside testing in Miami, Austin and Washington, D.C., Argo AI continues to test the Automated Driving System in Detroit, Pittsburgh, and Palo Alto, Calif. These projects have helped the company integrate self-driving test vehicles directly into its business pilots, offering real-world insights into what is required to run an efficient self-driving business.
Ford hopes to be a part of city transportation systems and provide a service that helps make people’s lives better. An example is the collaboration in Miami that created a Ford-designed smart infrastructure. Ford worked closely at the city, county, and state level to begin researching complex intersections. The data will help Ford and transportation officials better understand how autonomous vehicles can better navigate through busy or tricky intersections.
Argo continues to make significant advances toward enabling commercialization — including the recently announced Argo Lidar sensor with sensing range capability of 400 meters. This new technology enables Ford and Argo to test vehicles on highways and help connect vehicles to warehouses and suburban areas, expanding potential service areas for ride-hailing and goods deliveries.
| 2021-06-28T00:00:00 |
2021/06/28
|
https://greenautomarket.com/automaker-factory-robotics-what-it-means-for-jobs-and-electric-vehicle-production/
|
[
{
"date": "2021/06/28",
"position": 94,
"query": "robotics job displacement"
},
{
"date": "2021/06/28",
"position": 90,
"query": "robotics job displacement"
},
{
"date": "2021/06/28",
"position": 92,
"query": "robotics job displacement"
},
{
"date": "2021/06/28",
"position": 95,
"query": "robotics job displacement"
},
{
"date": "2021/06/28",
"position": 91,
"query": "robotics job displacement"
},
{
"date": "2021/06/28",
"position": 88,
"query": "robotics job displacement"
},
{
"date": "2021/06/28",
"position": 89,
"query": "robotics job displacement"
},
{
"date": "2021/06/28",
"position": 63,
"query": "robotics job displacement"
},
{
"date": "2021/06/28",
"position": 93,
"query": "robotics job displacement"
},
{
"date": "2021/06/28",
"position": 93,
"query": "robotics job displacement"
},
{
"date": "2021/06/28",
"position": 97,
"query": "robotics job displacement"
},
{
"date": "2021/06/28",
"position": 91,
"query": "robotics job displacement"
}
] |
WHO issues first global report on Artificial Intelligence (AI) in health ...
|
WHO issues first global report on Artificial Intelligence (AI) in health and six guiding principles for its design and use
|
https://www.who.int
|
[] |
Artificial Intelligence (AI) holds great promise for improving the delivery of healthcare and medicine worldwide, but only if ethics and ...
|
Artificial Intelligence (AI) holds great promise for improving the delivery of healthcare and medicine worldwide, but only if ethics and human rights are put at the heart of its design, deployment, and use, according to new WHO guidance published today.
The report, Ethics and governance of artificial intelligence for health, is the result of 2 years of consultations held by a panel of international experts appointed by WHO.
“Like all new technology, artificial intelligence holds enormous potential for improving the health of millions of people around the world, but like all technology it can also be misused and cause harm,” said Dr Tedros Adhanom Ghebreyesus, WHO Director-General. “This important new report provides a valuable guide for countries on how to maximize the benefits of AI, while minimizing its risks and avoiding its pitfalls.”
Artificial intelligence can be, and in some wealthy countries is already being used to improve the speed and accuracy of diagnosis and screening for diseases; to assist with clinical care; strengthen health research and drug development, and support diverse public health interventions, such as disease surveillance, outbreak response, and health systems management.
AI could also empower patients to take greater control of their own health care and better understand their evolving needs. It could also enable resource-poor countries and rural communities, where patients often have restricted access to health-care workers or medical professionals, to bridge gaps in access to health services.
However, WHO’s new report cautions against overestimating the benefits of AI for health, especially when this occurs at the expense of core investments and strategies required to achieve universal health coverage.
It also points out that opportunities are linked to challenges and risks, including unethical collection and use of health data; biases encoded in algorithms, and risks of AI to patient safety, cybersecurity, and the environment.
For example, while private and public sector investment in the development and deployment of AI is critical, the unregulated use of AI could subordinate the rights and interests of patients and communities to the powerful commercial interests of technology companies or the interests of governments in surveillance and social control.
The report also emphasizes that systems trained primarily on data collected from individuals in high-income countries may not perform well for individuals in low- and middle-income settings.
AI systems should therefore be carefully designed to reflect the diversity of socio-economic and health-care settings. They should be accompanied by training in digital skills, community engagement and awareness-raising, especially for millions of healthcare workers who will require digital literacy or retraining if their roles and functions are automated, and who must contend with machines that could challenge the decision-making and autonomy of providers and patients.
Ultimately, guided by existing laws and human rights obligations, and new laws and policies that enshrine ethical principles, governments, providers, and designers must work together to address ethics and human rights concerns at every stage of an AI technology’s design, development, and deployment.
Six principles to ensure AI works for the public interest in all countries
To limit the risks and maximize the opportunities intrinsic to the use of AI for health, WHO provides the following principles as the basis for AI regulation and governance:
Protecting human autonomy: In the context of health care, this means that humans should remain in control of health-care systems and medical decisions; privacy and confidentiality should be protected, and patients must give valid informed consent through appropriate legal frameworks for data protection.
Promoting human well-being and safety and the public interest. The designers of AI technologies should satisfy regulatory requirements for safety, accuracy and efficacy for well-defined use cases or indications. Measures of quality control in practice and quality improvement in the use of AI must be available.
Ensuring transparency, explainability and intelligibility. Transparency requires that sufficient information be published or documented before the design or deployment of an AI technology. Such information must be easily accessible and facilitate meaningful public consultation and debate on how the technology is designed and how it should or should not be used.
Fostering responsibility and accountability. Although AI technologies perform specific tasks, it is the responsibility of stakeholders to ensure that they are used under appropriate conditions and by appropriately trained people. Effective mechanisms should be available for questioning and for redress for individuals and groups that are adversely affected by decisions based on algorithms.
Ensuring inclusiveness and equity. Inclusiveness requires that AI for health be designed to encourage the widest possible equitable use and access, irrespective of age, sex, gender, income, race, ethnicity, sexual orientation, ability or other characteristics protected under human rights codes.
Promoting AI that is responsive and sustainable. Designers, developers and users should continuously and transparently assess AI applications during actual use to determine whether AI responds adequately and appropriately to expectations and requirements. AI systems should also be designed to minimize their environmental consequences and increase energy efficiency. Governments and companies should address anticipated disruptions in the workplace, including training for health-care workers to adapt to the use of AI systems, and potential job losses due to use of automated systems.
These principles will guide future WHO work to support efforts to ensure that the full potential of AI for healthcare and public health will be used for the benefits of all.
| 2021-06-28T00:00:00 |
https://www.who.int/news/item/28-06-2021-who-issues-first-global-report-on-ai-in-health-and-six-guiding-principles-for-its-design-and-use
|
[
{
"date": "2021/06/28",
"position": 75,
"query": "AI healthcare"
},
{
"date": "2021/06/28",
"position": 77,
"query": "AI healthcare"
},
{
"date": "2021/06/28",
"position": 69,
"query": "AI healthcare"
},
{
"date": "2021/06/28",
"position": 69,
"query": "AI healthcare"
},
{
"date": "2021/06/28",
"position": 60,
"query": "AI healthcare"
},
{
"date": "2021/06/28",
"position": 79,
"query": "AI healthcare"
},
{
"date": "2021/06/28",
"position": 67,
"query": "AI healthcare"
},
{
"date": "2021/06/28",
"position": 65,
"query": "AI healthcare"
},
{
"date": "2021/06/28",
"position": 61,
"query": "AI healthcare"
},
{
"date": "2021/06/28",
"position": 65,
"query": "AI healthcare"
}
] |
|
Ethics and governance of artificial intelligence for health
|
Ethics and governance of artificial intelligence for health
|
https://www.who.int
|
[] |
... healthcare workers who will rely on these technologies and the communities and individuals whose health will be affected by its use. Read ...
|
Overview
The WHO guidance on Ethics & Governance of Artificial Intelligence for Health is the product of eighteen months of deliberation amongst leading experts in ethics, digital technology, law, human rights, as well as experts from Ministries of Health. While new technologies that use artificial intelligence hold great promise to improve diagnosis, treatment, health research and drug development and to support governments carrying out public health functions, including surveillance and outbreak response, such technologies, according to the report, must put ethics and human rights at the heart of its design, deployment, and use.
The report identifies the ethical challenges and risks with the use of artificial intelligence of health, six consensus principles to ensure AI works to the public benefit of all countries. It also contains a set of recommendations that can ensure the governance of artificial intelligence for health maximizes the promise of the technology and holds all stakeholders – in the public and private sector – accountable and responsive to the healthcare workers who will rely on these technologies and the communities and individuals whose health will be affected by its use.
| 2021-06-28T00:00:00 |
https://www.who.int/publications/i/item/9789240029200
|
[
{
"date": "2021/06/28",
"position": 57,
"query": "artificial intelligence healthcare"
},
{
"date": "2021/06/28",
"position": 54,
"query": "artificial intelligence healthcare"
},
{
"date": "2021/06/28",
"position": 56,
"query": "artificial intelligence healthcare"
},
{
"date": "2021/06/28",
"position": 56,
"query": "artificial intelligence healthcare"
},
{
"date": "2021/06/28",
"position": 56,
"query": "artificial intelligence healthcare"
}
] |
|
The impact of artificial intelligence and digital style on industry and ...
|
The impact of artificial intelligence and digital style on industry and energy post-COVID-19 pandemic
|
https://link.springer.com
|
[
"Sharifi",
"Abbas.Sharifi Mee.Uut.Ac.Ir",
"Department Of Mechanical Engineering",
"Urmia University Of Technology",
"Uut",
"Urmia",
"Ahmadi",
"Department Of Industrial Engineering",
"Ala",
"Management"
] |
Some third parties are outside of the European Economic Area, with varying standards of data protection. ... disruption in the supply chain ...
|
Technology in education, disease prevention, and treatment
The digital technologies and technologies under the title of the Fourth Industrial Revolution have assisted in providing distance education, remote monitoring system, and sending information from distant places to health bases (Javaid et al. 2020). The video surveillance based on artificial intelligence and machine vision effectively reduced the work of doctors and hospital managers in these critical situations. Digital technologies offer methods for the proper isolation of infected patients to reduce the high risk of mortality, accelerate drug production, treatment, and care, which may also contribute to epidemics similar to COVID-19 in the future (Javaid et al. 2020). With the outbreak of COVID-19 and the increase in mortality, scientists worldwide are looking for new technologies to screen infected patients at different stages, find the best clinical trials, control the spread of the virus, etc. Recent studies have revealed that machine learning and artificial intelligence are more promising, faster, and more reliable technologies than humans in healthcare. In this regard, it should be noted that not all technologies are intended to remove humans from the cycle of interactions in the medical industry but to provide accurate decisions to physicians (Lalmuanawma et al. 2020). Although it is uncertain how carbon emissions would affect the planet after the COVID-19 outbreak, the energy intensity effect and the economic structure effect have opposing effects on carbon intensity drop, respectively, accelerating and delaying it. The energy mix effect, on the other hand, has a minimal impact on carbon intensity reduction. The outcomes of the deconstruction of industrial carbon intensity demonstrate that technological and structural factors varied greatly between industries.
A short brief explanation of rehabilitation and the effect of medicine over the COVID-19
Our healthcare system relies heavily on rehabilitation. Even so, during a pandemic response, when the healthcare system’s attention switches to the wise management of a surge of severely ill people, it is highly vulnerable. During the rapid growth of the COVID-19 pandemic, the problems of altering the roles of treatment providers and systems reveal insights that may help systems manage their reactions as they deal with these concerns. The impacts of the pandemic will last well beyond the immediate emergency response. Preparing for future pandemics is a critical long-term strategy for maximizing our ability to react to future problems in our healthcare system’s rehabilitation services. Infection prevention and control methods and supportive care, such as supplemental oxygen and mechanical ventilatory support, are being used in the treatment of COVID-19. The US Food and Drug Administration (FDA) has authorized remdesivir (Veklury) to treat COVID-19 in some circumstances.
The advantages of Industry 4.0 technologies over the COVID-19 crisis
Javaid et al. (2020) describe the effects of the Fourth Industrial Revolution on coronavirus prevention. Artificial intelligence and new technologies have also been effective in preventing coronavirus disease. This critical movement is including the use of virtual reality by physicians and medical staff to reduce the risk of coronavirus disease, which is applied to create a completely stress-free environment for training medical staff (Haleem and Javaid 2019; Ren et al. 2020).
Artificial intelligence for COVID-19 crisis management initially helped people with thermal imaging equipment scan public spaces, which could be used for social distancing. The infrared cameras were used to monitor crowds at airports, public places, and train stations across China. These cameras are also applied to detect the faces of people with a high fever. They can scan 200 people per minute and see people with body temperatures higher than 37.3° under the pretext that they may suffer from the disease (Naudé 2020). Imagine a time when there was a crisis similar to COVID-19, and there was no possibility of using these technologies. In such cases, due to a lack of sufficient information about this disease, people would maintain social relations with each other and use traditional findings for their treatment.
On the other hand, doctors would treat patients with simple laboratory facilities and devices. Also, in the corridors of medical centers and hospitals, people would be move with high density. Hence, the result would be nothing but the spread of the virus and the increase in infections and deaths due to COVID-19. However, nowadays, with digital technologies and modern technologies, advanced image processing cameras can be used in medical centers’ corridors to distinguish people suspected of being infected. Industry 4.0 technologies can be described as professional assistants for all community sections (see Table 1). The most important applications of this technology on COVID-19 are as follows:
Prevention and deceleration of the spread of disease
Providing treatment strategies and various methods in the diagnosis of COVID-19
Equipping and improving the supply chain of healthcare industries
Integrated control and monitoring of medical centers and public places
Significant reduction in virus infection and death due to COVID-19
Table 1 Industry 4.0 technologies during the COVID-19 outbreak (Haleem and Javaid 2019; Ren et al. 2020; Naudé 2020) Full size table
Mobile applications and helping to solve the COVID-19 crisis
One of the categories of Industry 4.0 is mobile technology, which is the article’s title due to its high importance. Regardless of the type of operating system and features, mobile technologies are of great importance in people’s daily lives (Javaid et al. 2020). In the post-COVID-19 era, this technology is efficient and valuable. Since addressing mobile health service needs is the only acceptable way to connect to the rest of the world, facilities, sensors, and chips can also control the issue. Smartphones, without a doubt, are based on these operating systems. In today’s medical industry, various countries make use of innovative technologies.
For example, Germany has implemented a tracking program based on smartwatches, which uses pulse and temperature. It transmits the resulting data to areas of health bases for further analysis (Heidel et. al. 2020). This feature has been applied in a limited and experimental way to evaluate people with coronavirus disease. People are split into information assets by location in this method. The smartwatch user may develop coronavirus disease due to changes in the reference program, such as pulse and temperature.
In the same way, that area is under the control of the health staff. With this system, individuals’ incidence and the speed of disease transmission can be significantly assessed (Whitelaw et al. 2020). Every country takes the importance of mobile technologies seriously based on the number of casualties in this crisis by reviewing the release dates and launches of applications contributing to the COVID-19 problem (Lalmuanawma et al. 2020). It can be seen that most of the countries that produce these applications started operating in “April and May.” In the meantime, only three countries, Austria, Singapore, and Israel, contributed to the COVID-19 crisis in their region with this technology in March and about a month and a half earlier than other countries.
As shown in Table 2, different countries have taken a step forward in software technologies to control diseases such as COVID-19. Each of them tries to manage and evaluate the condition with a part of artificial intelligence divisions. As mentioned in the previous part related to Germany, the application that was run in that country was also implemented on a smartwatch and used in other technologies such as Bluetooth and other sensors in a smartphone. For example, a program in Ghana is applied to study the population in each region. City-wide video surveillance cameras monitor each area of the city’s population and display it online on the mobile platform. It allows people to check the area’s population before leaving home or work for essentials (Whitelaw et al. 2020, Wang et al. 2021a, 2021b). Artificial intelligence and big data can be highly effective in education, especially in preventing COVID-19 disease. Big data come with three attributes: speed, volume, and variety; each of which can be interpreted as follows:
Speed in terms of information processing
Volume in terms of the high amount of data in each process
Diversity in terms of the number of different sources and channels that can produce big data
Table 2 Some call tracking applications in different countries with the necessary permissions (Whitelaw et al. 2020, Lalmuanawma et al. 2020, Heidel et. al, 2020) Full size table
There are different types of big data that are divided into three categories based on their source type:
Imaging-based data (e.g., data mining, extracting high-dimensional information from images)
Data based on wearable sensors
Digital and computational data by smartphones and the Internet
Any style of this data can be effective in treating COVID-19 (see Fig. 2). These issues can be necessarily provided by various valuable software in the previous section on corona outbreaks, or the data type can be changed (Bragazzi et al. 2020). ICT is a “helper” that helps disseminate information about the outbreak to large segments of the worldwide population in ways that would be impossible without it. Educators and administrators must be best ready to use ICTs in education throughout the COVID-19 crisis and beyond. This crisis is the perfect opportunity for government-led projects in schools to try out new ways to reach out to kids, learn from other nations, and incorporate practical techniques into regular education. Also, to generate practical student learning experiences, digital technologies must be integrated into sound instructional programs. Governments must undertake the required preparations to better map teaching and learning demands in future educational crises. This necessitates collecting comprehensive survey data on ICT use in schools as a necessary first step in adequately guiding policymaking. The media, particularly social media, can also inform pupils about the virus and basic hygiene. An animated music video advocating handwashing and other preventive steps to guard against the virus has gone popular in Vietnam.
Fig. 2 Big data and its defined features Full size image
Technology in production and industry
As the business relies more on technology, new production techniques such as 3D printers and industrial automation have increased in factories since the advent of the COVID-19. Moreover, import incentives have been declined. During this period, smartphones became increasingly widespread and accelerated digital globalization (Schilirò 2020). The global economy has been affected strongly by the outbreak of the corona. For instance, industries such as tourism, aviation, industry, and manufacturing have suffered in the 3 months of the disease since the supply chain management of sectors such as automobiles and electronics faced problems with the closure of international flights. The lack of supply of spare parts worldwide is among them. The coronavirus had the most significant impact on the manufacturing industry in China, the USA, and Germany (Cai and Luo 2020). COVID-19 pandemic and the effect on industrial parts around the world is an example in India. The lockdown has caused labor shortages around the country, which has increased transportation options’ resource limitations. Businesses that rely on a “just in time” or “restricted inventory” approach are likely to be disrupted by delayed supplies. Other enterprises and other services have also been shut down, exacerbating the situation. Due to the complete suspension of public transportation, personnel responsible for handling and delivering essential commodities and supplies must now walk to work. The same is particularly exhausting for people living on the fringes of the informal economy, resulting in more low productivity. As indicated in Table 3, if the virus is present in communities for a long time, the expert’s knowledge becomes more about this virus. This virus is accompanied by alterations every moment and changes its symptoms. It can be said that the importance of artificial intelligence and digital style on the life of the industry is the only industry the effects of which are visible in the short term, and there is no need for a long time to discover the truths.
Table 3 Applications of digitization if COVID-19 continues to be prevalent Full size table
Applications of digitalization on food and agricultural industries
In the last 20 years, scientists and physicians have shown more interest in artificial intelligence technologies. First of all, many have attempted to provide a precise definition of artificial intelligence. According to some authors, artificial intelligence is a machine’s ability to decode and understand input in an intelligent system. Some believe that artificial intelligence is a new way to manage information in a business model (Lüdeke-Freund 2010). Among the industries, the food industry is also of great importance. In situations similar to this, the need for proper nutrition for a growing population increases. This need interacts directly with systems in the field of agricultural and food sustainability. That is why numerous companies create activities to use artificial intelligence technologies to solve multiple problems and save valuable energy resources. The condition for accepting these technologies in process management is to possess sustainable business models (SBM), which can positively affect the whole system without harming the environment and society (Lüdeke-Freund 2010; Stevens and Shearmur 2020). According to Fig. 3, deep learning, computer vision, physical and software robots, and processing are the major technologies among artificial intelligence technologies that improve industries’ quality and performance. Machine vision and deep learning is a method to analyze data, which builds analytical models for a system and is based on the fact that the system can understand the information from the data, identify the models alone, and make the final decision with minimal human intervention. For instance, in the agricultural sector, programs have been developed with artificial intelligence technology, which can detect plant pests with photography (Di Vaio et al. 2020).
Fig. 3 The main parts of artificial intelligence Full size image
Physical and software robots are advanced machines that can solve small and large problems. For example, most people know robotic agriculture, in which they speed up tasks such as weeding or packing fruits. Robots are valuable assets allowing farmers to use their processing information about their profession to obtain various data such as temperature, humidity, fertilizer, soil condition, and water consumption and receive and control them through the robot by optimizing them in the next step (Di Vaio et al. 2020).
Artificial intelligence has other applications in the food industry as follows:
Sorting food products
Improving product quality
Ensuring compliance with health standards
Preparing and producing drinks
A successful example is the Kankan company; they continuously work to create intelligent solutions to improve health. The system, which can also be used in restaurants, uses face recognition and object recognition cameras to monitor workers to see if workers wear cooking hats and masks following food hygiene laws. Moreover, this software extracts and sends all the images to the management for review (Di Vaio et al. 2020). Digitalization is hugely influential in the industry, especially in the agricultural sector, accounting for half of the world’s water consumption (see Table 4). Nowadays, drones are used in farms and agricultural sectors for the reasons as following:
The possibility of a complete assessment of the ripening status of a fruit
Irrigation
Timely use for herbicides and pesticides
Table 4 A review of some work done in the food and agricultural industries with digital style and artificial intelligence Full size table
These factors have reduced environmental hazards for humans and agricultural products (Di Vaio et al. 2020).
| 2021-09-14T00:00:00 |
2021/09/14
|
https://link.springer.com/article/10.1007/s11356-021-15292-5
|
[
{
"date": "2021/07/16",
"position": 93,
"query": "AI economic disruption"
},
{
"date": "2021/07/16",
"position": 92,
"query": "AI economic disruption"
}
] |
Featured in VentureBeat: Can AI predict labor market trends?
|
Featured in VentureBeat: Can AI predict labor market trends?
|
https://bitsandatoms.co
|
[
"Nik Dawson"
] |
On the 16th of July 2021, I was featured in a VentureBeat article by Kyle Wiggers, titled: AI Weekly: Can AI predict labor market trends?
|
On the 16th of July 2021, I was featured in a VentureBeat article by Kyle Wiggers, titled: AI Weekly: Can AI predict labor market trends?. The article discusses the growing area of labour market analytics. It covers the common methods and datasets that we use in the industry to make job market predictions. The article also discusses the various shortcomings of these approaches, where I’m quoted:
“The challenge with predicting anomalies is simply that they’re hard to predict! An anomaly is something that deviates from the norm. So, when you train machine learning models on historic data, the future predictions are a product of that past information,” Dawson said. “This is [especially] problematic when ‘black swan’ events occur, like COVID-19 … Supply-side data are important for understanding what’s actually going on with workers, but they’re lagging indicators — it takes time for the data to reflect the crises that have occurred.”
There is also a discussion of the risks of bias and discrimination in predicting trends from labour market data:
“As Dawson notes, the risks are high when it comes to bias in labor market predictions. In HR settings, prejudicial algorithms have informed hiring, career development, and recruitment decisions. There are ways to help address the imbalances — for example, by excluding sensitive information like race, gender, and sexual orientation from training datasets. But even this isn’t a silver bullet, as these characteristics can be inferred from a combination of other features.”
The article concludes with future opportunities for labour market analytics, where I’m quoted discussing my optimism about Reinforcement Learning applications:
Dawson said he’s optimistic about what reinforcement learning might add to the mix of labor market predictions. Not only does it better reflect how job mobility actually occurs, but it also lessens the risks of bias and discrimination in job predictions because it’s less reliant on aggregated historic training data, he asserts: “[Reinforcement learning is a] goal-oriented approach, where an agent (say, an individual looking for a job) navigates their environment (e.g. job market) and performs actions to achieve their goal (e.g. takes a course to upskill for a target career),” Dawson said. “As the agent interacts with their environment, they learn and adjust their actions to better achieve their goal; they also respond to an environment that dynamically adjusts (e.g. a labor market crisis). This approach balances ‘exploitation’ of an individual’s current state (e.g. recommending jobs strongly aligned with their skills and previous occupations) with ‘exploration’ of new paths that are different to an individual’s state (e.g. recommending jobs that are new career paths).”
Thanks, Kyle for the interview! Check out the full article here.
| 2021-07-19T00:00:00 |
2021/07/19
|
https://bitsandatoms.co/featured-in-venturebeat-can-ai-predict-labor-market-trends/?utm_source=rss&utm_medium=rss&utm_campaign=featured-in-venturebeat-can-ai-predict-labor-market-trends
|
[
{
"date": "2021/07/19",
"position": 87,
"query": "AI labor market trends"
},
{
"date": "2021/07/19",
"position": 82,
"query": "AI labor market trends"
},
{
"date": "2021/07/19",
"position": 93,
"query": "AI labor market trends"
},
{
"date": "2021/07/19",
"position": 88,
"query": "AI labor market trends"
},
{
"date": "2021/07/19",
"position": 89,
"query": "AI labor market trends"
},
{
"date": "2021/07/19",
"position": 92,
"query": "AI labor market trends"
},
{
"date": "2021/07/19",
"position": 88,
"query": "AI labor market trends"
},
{
"date": "2021/07/19",
"position": 92,
"query": "AI labor market trends"
},
{
"date": "2021/07/19",
"position": 89,
"query": "AI labor market trends"
},
{
"date": "2021/07/19",
"position": 85,
"query": "AI labor market trends"
},
{
"date": "2021/07/19",
"position": 88,
"query": "AI labor market trends"
},
{
"date": "2021/07/19",
"position": 84,
"query": "AI labor market trends"
}
] |
Artificial intelligence in healthcare: transforming the practice of ...
|
Artificial intelligence in healthcare: transforming the practice of medicine
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Junaid Bajwa",
"Amicrosoft Research",
"Cambridge",
"Usman Munir",
"Bmicrosoft Research",
"Aditya Nori",
"Cmicrosoft Research",
"Bryan Williams",
"Duniversity College London",
"London"
] |
AI is a powerful and disruptive area of computer science, with the potential to fundamentally transform the practice of medicine and the delivery of healthcare.
|
ABSTRACT Artificial intelligence (AI) is a powerful and disruptive area of computer science, with the potential to fundamentally transform the practice of medicine and the delivery of healthcare. In this review article, we outline recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective, reliable and safe AI systems, and discuss the possible future direction of AI augmented healthcare systems. KEYWORDS: AI, digital health
Introduction Healthcare systems around the world face significant challenges in achieving the ‘quadruple aim’ for healthcare: improve population health, improve the patient's experience of care, enhance caregiver experience and reduce the rising cost of care.1–3 Ageing populations, growing burden of chronic diseases and rising costs of healthcare globally are challenging governments, payers, regulators and providers to innovate and transform models of healthcare delivery. Moreover, against a backdrop now catalysed by the global pandemic, healthcare systems find themselves challenged to ‘perform’ (deliver effective, high-quality care) and ‘transform’ care at scale by leveraging real-world data driven insights directly into patient care. The pandemic has also highlighted the shortages in healthcare workforce and inequities in the access to care, previously articulated by The King's Fund and the World Health Organization (Box 1).4,5 Box 1. Workforce challenges in the next decade By 2030, the gap between supply of and demand for staff employed by NHS trusts could increase to almost 250,000 full-time equivalent posts.4 Based on the current trends and needs of the global population by 2030, the world will have 18 million fewer healthcare professionals (especially marked differences in the developing world), including 5 million fewer doctors than society will require.5 Open in a new tab The application of technology and artificial intelligence (AI) in healthcare has the potential to address some of these supply-and-demand challenges. The increasing availability of multi-modal data (genomics, economic, demographic, clinical and phenotypic) coupled with technology innovations in mobile, internet of things (IoT), computing power and data security herald a moment of convergence between healthcare and technology to fundamentally transform models of healthcare delivery through AI-augmented healthcare systems. In particular, cloud computing is enabling the transition of effective and safe AI systems into mainstream healthcare delivery. Cloud computing is providing the computing capacity for the analysis of considerably large amounts of data, at higher speeds and lower costs compared with historic ‘on premises’ infrastructure of healthcare organisations. Indeed, we observe that many technology providers are increasingly seeking to partner with healthcare organisations to drive AI-driven medical innovation enabled by cloud computing and technology-related transformation (Box 2).6–8 Box 2. Quotes from technology leaders Satya Nadella, chief executive officer, Microsoft: ‘AI is perhaps the most transformational technology of our time, and healthcare is perhaps AI's most pressing application.’6 Tim Cook, chief executive officer, Apple: ‘[Healthcare] is a business opportunity ... if you look at it, medical health activity is the largest or second-largest component of the economy.’7 Google Health: ‘We think that AI is poised to transform medicine, delivering new, assistive technologies that will empower doctors to better serve their patients. Machine learning has dozens of possible application areas, but healthcare stands out as a remarkable opportunity to benefit people.’8 Open in a new tab Here, we summarise recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective AI systems and discuss the possible future direction of AI augmented healthcare systems.
What is artificial intelligence? Simply put, AI refers to the science and engineering of making intelligent machines, through algorithms or a set of rules, which the machine follows to mimic human cognitive functions, such as learning and problem solving.9 AI systems have the potential to anticipate problems or deal with issues as they come up and, as such, operate in an intentional, intelligent and adaptive manner.10 AI's strength is in its ability to learn and recognise patterns and relationships from large multidimensional and multimodal datasets; for example, AI systems could translate a patient's entire medical record into a single number that represents a likely diagnosis.11,12 Moreover, AI systems are dynamic and autonomous, learning and adapting as more data become available.13 AI is not one ubiquitous, universal technology, rather, it represents several subfields (such as machine learning and deep learning) that, individually or in combination, add intelligence to applications. Machine learning (ML) refers to the study of algorithms that allow computer programs to automatically improve through experience.14 ML itself may be categorised as ‘supervised’, ‘unsupervised’ and ‘reinforcement learning’ (RL), and there is ongoing research in various sub-fields including ‘semi-supervised’, ‘self-supervised’ and ‘multi-instance’ ML. Supervised learning leverages labelled data (annotated information); for example, using labelled X-ray images of known tumours to detect tumours in new images. 15
‘Unsupervised learning’ attempts to extract information from data without labels; for example, categorising groups of patients with similar symptoms to identify a common cause. 16
In RL, computational agents learn by trial and error, or by expert demonstration. The algorithm learns by developing a strategy to maximise rewards. Of note, major breakthroughs in AI in recent years have been based on RL.
Deep learning (DL) is a class of algorithms that learns by using a large, many-layered collection of connected processes and exposing these processors to a vast set of examples. DL has emerged as the predominant method in AI today driving improvements in areas such as image and speech recognition.17,18
How to build effective and trusted AI-augmented healthcare systems? Despite more than a decade of significant focus, the use and adoption of AI in clinical practice remains limited, with many AI products for healthcare still at the design and develop stage.19–22 While there are different ways to build AI systems for healthcare, far too often there are attempts to force square pegs into round holes ie find healthcare problems to apply AI solutions to without due consideration to local context (such as clinical workflows, user needs, trust, safety and ethical implications). We hold the view that AI amplifies and augments, rather than replaces, human intelligence. Hence, when building AI systems in healthcare, it is key to not replace the important elements of the human interaction in medicine but to focus it, and improve the efficiency and effectiveness of that interaction. Moreover, AI innovations in healthcare will come through an in-depth, human-centred understanding of the complexity of patient journeys and care pathways. In Fig 1, we describe a problem-driven, human-centred approach, adapted from frameworks by Wiens et al, Care and Sendak to building effective and reliable AI-augmented healthcare systems.23–25 Fig 1. Open in a new tab Multi-step, iterative approach to build effective and reliable AI-augmented systems in healthcare. Design and develop The first stage is to design and develop AI solutions for the right problems using a human-centred AI and experimentation approach and engaging appropriate stakeholders, especially the healthcare users themselves. Stakeholder engagement and co-creation Build a multidisciplinary team including computer and social scientists, operational and research leadership, and clinical stakeholders (physician, caregivers and patients) and subject experts (eg for biomedical scientists) that would include authorisers, motivators, financiers, conveners, connectors, implementers and champions.26 A multi-stakeholder team brings the technical, strategic, operational expertise to define problems, goals, success metrics and intermediate milestones. Human-centred AI A human-centred AI approach combines an ethnographic understanding of health systems, with AI. Through user-designed research, first understand the key problems (we suggest using a qualitative study design to understand ‘what is the problem’, ‘why is it a problem’, ‘to whom does it matter’, ‘why has it not been addressed before’ and ‘why is it not getting attention’) including the needs, constraints and workflows in healthcare organisations, and the facilitators and barriers to the integration of AI within the clinical context. After defining key problems, the next step is to identify which problems are appropriate for AI to solve, whether there is availability of applicable datasets to build and later evaluate AI. By contextualising algorithms in an existing workflow, AI systems would operate within existing norms and practices to ensure adoption, providing appropriate solutions to existing problems for the end user. Experimentation The focus should be on piloting of new stepwise experiments to build AI tools, using tight feedback loops from stakeholders to facilitate rapid experiential learning and incremental changes.27 The experiments would allow the trying out of new ideas simultaneously, exploring to see which one works, learn what works and what doesn't, and why.28 Experimentation and feedback will help to elucidate the purpose and intended uses for the AI system: the likely end users and the potential harm and ethical implications of AI system to them (for instance, data privacy, security, equity and safety). Evaluate and validate Next, we must iteratively evaluate and validate the predictions made by the AI tool to test how well it is functioning. This is critical, and evaluation is based on three dimensions: statistical validity, clinical utility and economic utility. Statistical validity is understanding the performance of AI on metrics of accuracy, reliability, robustness, stability and calibration. High model performance on retrospective, in silico settings is not sufficient to demonstrate clinical utility or impact.
To determine clinical utility, evaluate the algorithm in a real-time environment on a hold-out and temporal validation set (eg longitudinal and external geographic datasets) to demonstrate clinical effectiveness and generalisability. 25
Economic utility quantifies the net benefit relative to the cost from the investment in the AI system. Scale and diffuse Many AI systems are initially designed to solve a problem at one healthcare system based on the patient population specific to that location and context. Scale up of AI systems requires special attention to deployment modalities, model updates, the regulatory system, variation between systems and reimbursement environment. Monitor and maintain Even after an AI system has been deployed clinically, it must be continually monitored and maintained to monitor for risks and adverse events using effective post-market surveillance. Healthcare organisations, regulatory bodies and AI developers should cooperate to collate and analyse the relevant datasets for AI performance, clinical and safety-related risks, and adverse events.29
What are the current and future use cases of AI in healthcare? AI can enable healthcare systems to achieve their ‘quadruple aim’ by democratising and standardising a future of connected and AI augmented care, precision diagnostics, precision therapeutics and, ultimately, precision medicine (Table 1).30 Research in the application of AI healthcare continues to accelerate rapidly, with potential use cases being demonstrated across the healthcare sector (both physical and mental health) including drug discovery, virtual clinical consultation, disease diagnosis, prognosis, medication management and health monitoring. Table 1. Widescale adoption and application of artificial intelligence in healthcare Timeline Connected/augmented care Precision diagnostics Precision therapeutics Precision Medicine Summary Short term: 0–5 years Internet of things in healthcare
Virtual assistants
Augmented telehealth
Personalised mental health support Precision imaging (eg diabetic retinopathy and radiotherapy planning) CRISPR (increasing use) Digital and AI enabled research hospitals30 AI automates time consuming, high-volume repetitive tasks, especially within precision imaging Medium-term: 5–10 years Ambient intelligence in healthcare Large-scale adoption and scale-up of precision imaging Synthetic biology
Immunomics Customisation of healthcare
Robotic assisted therapies AI uses multi-modal datasets to drive precision therapeutics Long term: >10 years Autonomous virtual health assistants, delivering predictive and anticipatory care
Networked and connected care organisations (single digital infrastructure) Holographic and hybrid imaging
Holomics (integrated genomic/radiomic/proteomic/clinical/immunohistochemical data) Genomics medicine
AI driven drug discovery New curative treatments
AI empowered healthcare professionals (eg digital twins) AI enables healthcare systems to achieve a state of precision medicine through AI-augmented healthcare and connected care Open in a new tab We describe a non-exhaustive suite of AI applications in healthcare in the near term, medium term and longer term, for the potential capabilities of AI to augment, automate and transform medicine. AI today (and in the near future) Currently, AI systems are not reasoning engines ie cannot reason the same way as human physicians, who can draw upon ‘common sense’ or ‘clinical intuition and experience’.12 Instead, AI resembles a signal translator, translating patterns from datasets. AI systems today are beginning to be adopted by healthcare organisations to automate time consuming, high volume repetitive tasks. Moreover, there is considerable progress in demonstrating the use of AI in precision diagnostics (eg diabetic retinopathy and radiotherapy planning). AI in the medium term (the next 5–10 years) In the medium term, we propose that there will be significant progress in the development of powerful algorithms that are efficient (eg require less data to train), able to use unlabelled data, and can combine disparate structured and unstructured data including imaging, electronic health data, multi-omic, behavioural and pharmacological data. In addition, healthcare organisations and medical practices will evolve from being adopters of AI platforms, to becoming co-innovators with technology partners in the development of novel AI systems for precision therapeutics. AI in the long term (>10 years) In the long term, AI systems will become more intelligent, enabling AI healthcare systems achieve a state of precision medicine through AI-augmented healthcare and connected care. Healthcare will shift from the traditional one-size-fits-all form of medicine to a preventative, personalised, data-driven disease management model that achieves improved patient outcomes (improved patient and clinical experiences of care) in a more cost-effective delivery system. Connected/augmented care AI could significantly reduce inefficiency in healthcare, improve patient flow and experience, and enhance caregiver experience and patient safety through the care pathway; for example, AI could be applied to the remote monitoring of patients (eg intelligent telehealth through wearables/sensors) to identify and provide timely care of patients at risk of deterioration. In the long term, we expect that healthcare clinics, hospitals, social care services, patients and caregivers to be all connected to a single, interoperable digital infrastructure using passive sensors in combination with ambient intelligence.31 Following are two AI applications in connected care. Virtual assistants and AI chatbots AI chatbots (such as those used in Babylon (www.babylonhealth.com) and Ada (https://ada.com)) are being used by patients to identify symptoms and recommend further actions in community and primary care settings. AI chatbots can be integrated with wearable devices such as smartwatches to provide insights to both patients and caregivers in improving their behaviour, sleep and general wellness. Ambient and intelligent care We also note the emergence of ambient sensing without the need for any peripherals. Emerald (www.emeraldinno.com): a wireless, touchless sensor and machine learning platform for remote monitoring of sleep, breathing and behaviour, founded by Massachusetts Institute of Technology faculty and researchers.
Google nest: claiming to monitor sleep (including sleep disturbances like cough) using motion and sound sensors. 32
A recently published article exploring the ability to use smart speakers to contactlessly monitor heart rhythms. 33
Automation and ambient clinical intelligence: AI systems leveraging natural language processing (NLP) technology have the potential to automate administrative tasks such as documenting patient visits in electronic health records, optimising clinical workflow and enabling clinicians to focus more time on caring for patients (eg Nuance Dragon Ambient eXperience (www.nuance.com/healthcare/ambient-clinical-intelligence.html)).
Precision diagnostics Diagnostic imaging The automated classification of medical images is the leading AI application today. A recent review of AI/ML-based medical devices approved in the USA and Europe from 2015–2020 found that more than half (129 (58%) devices in the USA and 126 (53%) devices in Europe) were approved or CE marked for radiological use.34 Studies have demonstrated AI's ability to meet or exceed the performance of human experts in image-based diagnoses from several medical specialties including pneumonia in radiology (a convolutional neural network trained with labelled frontal chest X-ray images outperformed radiologists in detecting pneumonia), dermatology (a convolutional neural network was trained with clinical images and was found to classify skin lesions accurately), pathology (one study trained AI algorithms with whole-slide pathology images to detect lymph node metastases of breast cancer and compared the results with those of pathologists) and cardiology (a deep learning algorithm diagnosed heart attack with a performance comparable with that of cardiologists).35–38 We recognise that there are some exemplars in this area in the NHS (eg University of Leeds Virtual Pathology Project and the National Pathology Imaging Co-operative) and expect widescale adoption and scaleup of AI-based diagnostic imaging in the medium term.39 We provide two use cases of such technologies. Diabetic retinopathy screening Key to reducing preventable, diabetes-related vision loss worldwide is screening individuals for detection and the prompt treatment of diabetic retinopathy. However, screening is costly given the substantial number of diabetes patients and limited manpower for eye care worldwide.40 Research studies on automated AI algorithms for diabetic retinopathy in the USA, Singapore, Thailand and India have demonstrated robust diagnostic performance and cost effectiveness.41–44 Moreover, Centers for Medicare & Medicaid Services approved Medicare reimbursement for the use of Food and Drug Administration approved AI algorithm ‘IDx-DR’, which demonstrated 87% sensitivity and 90% specificity for detecting more-than-mild diabetic retinopathy.45 Improving the precision and reducing waiting timings for radiotherapy planning An important AI application is to assist clinicians for image preparation and planning tasks for radiotherapy cancer treatment. Currently, segmentation of the images is time consuming and laborious task, performed manually by an oncologist using specially designed software to draw contours around the regions of interest. The AI-based InnerEye open-source technology can cut this preparation time for head and neck, and prostate cancer by up to 90%, meaning that waiting times for starting potentially life-saving radiotherapy treatment can be dramatically reduced (Fig 2).46,47 Fig 2. Open in a new tab Potential applications for the InnerEye deep learning toolkit include quantitative radiology for monitoring tumour progression, planning for surgery and radiotherapy planning.47
Precision therapeutics To make progress towards precision therapeutics, we need to considerably improve our understanding of disease. Researchers globally are exploring the cellular and molecular basis of disease, collecting a range of multimodal datasets that can lead to digital and biological biomarkers for diagnosis, severity and progression. Two important future AI applications include immunomics / synthetic biology and drug discovery. Immunomics and synthetic biology Through the application of AI tools on multimodal datasets in the future, we may be able to better understand the cellular basis of disease and the clustering of diseases and patient populations to provide more targeted preventive strategies, for example, using immunomics to diagnose and better predict care and treatment options. This will be revolutionary for multiple standards of care, with particular impact in the cancer, neurological and rare disease space, personalising the experience of care for the individual. AI-driven drug discovery AI will drive significant improvement in clinical trial design and optimisation of drug manufacturing processes, and, in general, any combinatorial optimisation process in healthcare could be replaced by AI. We have already seen the beginnings of this with the recent announcements by DeepMind and AlphaFold, which now sets the stage for better understanding disease processes, predicting protein structures and developing more targeted therapeutics (for both rare and more common diseases; Fig 3).48,49 Fig 3. Open in a new tab An overview of the main neural network model architecture for AlphaFold.49 MSA = multiple sequence alignment.
Precision medicine New curative therapies Over the past decade, synthetic biology has produced developments like CRISPR gene editing and some personalised cancer therapies. However, the life cycle for developing such advanced therapies is still extremely inefficient and expensive. In future, with better access to data (genomic, proteomic, glycomic, metabolomic and bioinformatic), AI will allow us to handle far more systematic complexity and, in turn, help us transform the way we understand, discover and affect biology. This will improve the efficiency of the drug discovery process by helping better predict early which agents are more likely to be effective and also better anticipate adverse drug effects, which have often thwarted the further development of otherwise effective drugs at a costly late stage in the development process. This, in turn will democratise access to novel advanced therapies at a lower cost. AI empowered healthcare professionals In the longer term, healthcare professionals will leverage AI in augmenting the care they provide, allowing them to provide safer, standardised and more effective care at the top of their licence; for example, clinicians could use an ‘AI digital consult’ to examine ‘digital twin’ models of their patients (a truly ‘digital and biomedical’ version of a patient), allowing them to ‘test’ the effectiveness, safety and experience of an intervention (such as a cancer drug) in the digital environment prior to delivering the intervention to the patient in the real world.
Challenges We recognise that there are significant challenges related to the wider adoption and deployment of AI into healthcare systems. These challenges include, but are not limited to, data quality and access, technical infrastructure, organisational capacity, and ethical and responsible practices in addition to aspects related to safety and regulation. Some of these issues have been covered, but others go beyond the scope of this current article.
| 2021-07-14T00:00:00 |
2021/07/14
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC8285156/
|
[
{
"date": "2022/12/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/01/01",
"position": 1,
"query": "AI healthcare"
},
{
"date": "2023/01/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/02/01",
"position": 1,
"query": "AI healthcare"
},
{
"date": "2023/02/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/03/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/04/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/06/01",
"position": 1,
"query": "AI healthcare"
},
{
"date": "2023/07/01",
"position": 1,
"query": "AI healthcare"
},
{
"date": "2023/08/01",
"position": 1,
"query": "AI healthcare"
},
{
"date": "2023/09/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/11/01",
"position": 1,
"query": "AI healthcare"
},
{
"date": "2023/11/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2023/12/01",
"position": 2,
"query": "AI healthcare"
},
{
"date": "2023/12/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/01/01",
"position": 1,
"query": "AI healthcare"
},
{
"date": "2024/02/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/03/01",
"position": 1,
"query": "AI healthcare"
},
{
"date": "2024/03/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/05/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/06/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/09/01",
"position": 1,
"query": "AI healthcare"
},
{
"date": "2024/10/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2024/11/01",
"position": 1,
"query": "AI healthcare"
},
{
"date": "2025/01/01",
"position": 1,
"query": "artificial intelligence healthcare"
},
{
"date": "2021/07/26",
"position": 10,
"query": "artificial intelligence healthcare"
},
{
"date": "2021/07/26",
"position": 10,
"query": "artificial intelligence healthcare"
},
{
"date": "2021/07/26",
"position": 7,
"query": "artificial intelligence healthcare"
},
{
"date": "2021/07/26",
"position": 4,
"query": "artificial intelligence healthcare"
},
{
"date": "2021/07/26",
"position": 7,
"query": "artificial intelligence healthcare"
}
] |
What Can Machine Learning Do - Workforce - Brynjolfsson e Mitchel
|
What Can Machine Learning Do - Workforce - Brynjolfsson e Mitchel
|
https://www.scribd.com
|
[] |
What Can Machine Learning Do_Workforce_Brynjolfsson e Mitchel - Free download as PDF File (.pdf), Text File (.txt) or read online for free.
|
Save What Can Machine Learning Do_Workforce_Brynjolfsso... For Later
| 2021-07-28T00:00:00 |
https://www.scribd.com/document/517719168/What-Can-Machine-Learning-Do-Workforce-Brynjolfsson-e-Mitchel
|
[
{
"date": "2021/07/28",
"position": 94,
"query": "machine learning workforce"
},
{
"date": "2021/07/28",
"position": 100,
"query": "machine learning workforce"
}
] |
|
What Jobs Can You Get with a Master's in Artificial Intelligence?
|
What Jobs Can I Get With a Master's in Artificial Intelligence?
|
https://csuglobal.edu
|
[] |
Jobs in this field tend to be lucrative, as Payscale.com reports that the average salary for AI Engineers in 2022 is $92,785. If you're ...
|
Recently, we discussed what you can do with a Master’s Degree in Artificial Intelligence, and what AI engineers actually do. Today, we’ll explore what jobs you can get with a Master’s in Artificial Intelligence.
As part of our discussion, we’ll look closely at a few of the top job titles for AI graduates to help you understand which role might be the best fit for you and your particular interests. We’ll also explore whether an M.S. in AI is really worth getting, and why you should choose to earn your degree online with CSU Global.
Artificial intelligence (AI) is an exciting field on the cutting edge of modern computing, focused on developing technology capable of mimicking human cognitive abilities, and earning your Master’s Degree in this field could be a catalyst for a thrilling career creating the technologies that will shape the future.
Once you’ve learned all about the different jobs available to graduates of artificial intelligence master’s programs, fill out our information request form to learn more about CSU Global’s 100% online Master’s Degree in Artificial Intelligence and Machine Learning. Or, if you’re ready to get started, submit your application today.
What Jobs Does a Master’s in Artificial Intelligence Make You Eligible For?
Completing your Master’s Degree in Artificial Intelligence can open the door to a world of stimulating and cutting-edge career paths within the high-tech industry, including roles in management or upper leadership.
There are many roles for which you’d be qualified after earning your M.S. in AI, but for today, we’ll review three of the most popular job titles for people who complete AI Master’s Programs.
AI Engineer/Scientist / 2022 Median Pay: $92,785
AI Engineers work with teams of software developers and business analysts to develop technical solutions using artificial intelligence and machine learning technologies. This could include data mining, training machine learning models for specific tasks, researching improvements for existing AI algorithms, or creating entirely new solutions.
Jobs in this field tend to be lucrative, as Payscale.com reports that the average salary for AI Engineers in 2022 is $92,785.
If you’re interested in exploring the intersection of software engineering and artificial intelligence, and you’re excited by the idea of using AI to solve complex business problems, then you may be interested in pursuing a career as an AI Engineer.
Software Developer / 2021 Median Pay: $109,020
Software developers combine their creativity with technological expertise to design the applications and other programs run on computers, phones, and other devices. They may help design AI software or use machine learning to improve the algorithms behind the software.
According to the Bureau of Labor Statistics, software developers are in high demand. BLS expects employment for this role to grow 25% from 2021 to 2031, much faster than the average rate of growth for all industries, noting that the need for advanced, sophisticated computer software across all industries will drive this growth.
If you’re interested in designing, testing, and developing software, and you want to be on the leading edge of the field using artificial intelligence to improve computer software, then you may be interested in a career as a software developer.
Computer and Information Research Scientists / 2021 Median Pay: $131,490
Computer and Information Research Scientists are innovators who solve complex computing problems for a range of industries, from business and medicine to science and other fields.
These scientists use complex computer models to understand computing problems, leveraging advanced data science and machine learning strategies to develop new approaches to artificial intelligence.
This field is also in high demand. According to BLS, the employment of computer and information research scientists is projected to grow by 21% from 2021 to 2031.
It’s not an easy field to break into and the work is incredibly complex, but if you want to work at the cutting edge of computing helping to design and develop next-generation technologies, then this could be the perfect role for you.
These are only a few of the more popular job titles available to graduates from Master’s in Artificial Intelligence programs. In reality, there are many other roles that you could pursue after completing your Master’s Degree in AI.
Is a Master’s Degree in Artificial Intelligence Worth It?
Yes, a Master’s Degree in Artificial Intelligence is definitely worth it, especially if you earn your degree from an accredited and widely respected institution, such as CSU Global.
The field of AI is growing rapidly, as artificial intelligence is being utilized for more industries than ever before, with AI-driven algorithms helping to shape everything from healthcare delivery to automotive manufacturing.
Earning your Master’s in Artificial Intelligence gives you a solid grounding in the foundational tenets and advanced concepts involved in this complex and ever-evolving field.
The material you study in a master’s program will help you gain the flexible capabilities required to succeed in this challenging field, from developing a thorough understanding of deep-learning libraries such as Tensorflow, to learning how to develop computer solutions capable of modeling human behavior.
While it may seem like a big investment of time and energy, earning your Master’s in Artificial Intelligence is an important step to take if you want to take on challenging roles in the exciting and innovative field of artificial intelligence.
Can You Get an Accredited M.S. in Artificial Intelligence Online?
Yes, you can earn a regionally accredited M.S. Degree in Artificial Intelligence online from CSU Global.
Our accelerated online Master’s program offers much more flexibility and freedom compared to traditional in-person programs, making it an excellent option for anyone with existing work or family responsibilities.
Our online M.S. in AI program was designed to help you juggle competing responsibilities, allowing you to pursue new educational credentials while enjoying:
No set times or locations for classes.
Monthly class starts.
Accelerated, eight-week courses.
If you’ve already got a busy schedule and existing responsibilities, then you should consider studying online with CSU Global.
Why Should You Consider Studying Artificial Intelligence at CSU Global?
Our online Master’s in Artificial Intelligence and Machine Learning is an excellent option for anyone eager to pursue a career in the innovative and exciting field of artificial intelligence.
If you’re interested in programming, math, probability, and statistics, then our Master’s program is the perfect way to build the skills and academic credentials you need to put those interests to use as an AI professional.
Our M.S. in AI program is regionally accredited by the Higher Learning Commission and widely respected by industry professionals, while CSU Global itself is recognized as an industry leader in online education, having recently earned:
A #1 ranking for Best Online Colleges & Schools in Colorado from Best Accredited Colleges.
A #10 ranking for Best Online Colleges for ROI from OnlineU.
A #1 ranking for Best Online Colleges in Colorado from Best Colleges.
Finally, CSU Global offers competitive tuition rates and a Tuition Guarantee to ensure your tuition rate won’t increase from enrollment through graduation.
To get additional details about our regionally accredited, 100% online Master’s in AI and Machine Learning program, please give us a call at 800-462-7845, or fill out our Information Request Form.
Ready to get started today? Apply now!
| 2021-08-02T00:00:00 |
2021/08/02
|
https://csuglobal.edu/blog/what-jobs-can-you-get-masters-artificial-intelligence
|
[
{
"date": "2021/08/02",
"position": 96,
"query": "artificial intelligence wages"
},
{
"date": "2021/08/02",
"position": 98,
"query": "artificial intelligence wages"
},
{
"date": "2021/08/02",
"position": 95,
"query": "artificial intelligence wages"
}
] |
Why You Have to Automate Upskilling and Reskilling Now | SumTotal
|
Why You Have to Automate Upskilling and Reskilling Now
|
https://www.sumtotalsystems.com
|
[] |
Self-learning technology harnessing AI and machine learning is a much more efficient method of reskilling employees and ensuring an agile ...
|
The need to upskill and reskill employees rapidly and effectively has risen as organizations face unprecedented change and volatility. However, most organizations don't know how to do it right. Brandon Hall Group research found that less than half of organizations believe they are culturally ready to take on personalized learning at scale.
According to the 2021 LinkedIn Workplace Learning Report, 59% of L&D professionals consider upskilling and reskilling a top priority. In its “Future of learning in the wake of COVID-19” report, Deloitte claims at least 85% of organizations want their employees to develop critical human skills like resilience, emotional intelligence, and empathy.
Make Connections to Fill Skills Gaps
With today's rapid digital transformation, employees' current skills and the skills employers actually need are quickly diverging. If these skills gaps are left unaddressed, organizations won't have the internal talent they sorely need.
Many organizations committed to growing their workforce's skills still manually identify and curate the skills they want their employees to develop. This process is unscalable and unpredictable. Essentially, they're spending a lot of time and just hoping for the best when it comes to the efficacy of their talent development programs.
In Brandon Hall Group’s HCM Outlook 2021 study, 60% of organizations place a high value on determining upskilling and reskilling priorities in the face of changing business conditions.
“Even companies where personalized learning is a priority, and time, money and resources are available, they face many challenges with personalized learning at scale,” said Brandon Hall Group CEO Mike Cooke. “The thing that companies said was challenging most often was that their managers don’t have insights into what their employees are learning. This element is often overlooked and managers can play a huge role in contextual, flow-of-work learning.”
It is becoming increasingly difficult for today's learners to access the knowledge and information they need when and where they need it. Learning feels disjointed and separate from the job, preventing people from taking an active role in their own development. This ultimately leads to low employee growth, which hinders organizational improvement.
Digital or Bust
More core processes are going digital. Larger segments of the population are working remotely, making it urgent to rethink everyday processes that are now online and automated. Employees in existing roles need skills to deal with processes that are becoming more virtual.
Digital tools are necessary when addressing the skills of the future in a modern workforce. For learners to collaborate and gain experience applying their skills to specific situations and roles, they must connect with their peers and their managers from day one. Unless employees and managers are connected, and training can be accessed virtually at the point of need and in the flow of work, it will be impossible to upskill and reskill effectively.
Align Employees and Organizational Goals
Organizations that can align internal skillsets with business objectives in real-time will enjoy a competitive advantage over rivals with manual reskilling programs. Self-learning technology harnessing AI and machine learning is a much more efficient method of reskilling employees and ensuring an agile workforce.
According to Brandon Hall Group’s 2021 report Leveraging Technology to Reskill Employees at Scale, modern learning and talent development platforms help keep employees engaged by providing career pathing.
By beginning with an inventory that takes stock of each employee’s skillset, organizations can identify gaps. Then, organizations can create learning paths to upskill employees with relevant knowledge. For example, career path builders within a learning management system (LMS) can show an employee’s readiness to move into another position. This is determined based on a configurable set of criteria and can even factor in peer assessments.
By pairing this with detailed individual development plans, organizations can ensure employees obtain the skills they need. AI powers precise, automated suggestions for learning activities, goals, content, and other tools to help employees improve key skills. In the end, learning should be targeted and tailored for learners.
Other Ways to Automate Upskilling and Reskilling
Another way automation can improve employee upskilling and reskilling is to digitize all the mundane L&D tasks that often fall on HR. For example, the following L&D tasks could be automated:
Registering an employee for training sessions
Monitoring the required training for an employee
Creating and sending training materials for an employee
Making historical training records available to employees
Maintaining an employee's certifications and regulatory requirements
By streamlining even basic administrative tasks like these, L&D professionals can spend more time on high-value tasks.
The automation provided by modern learning management systems is critical to ensure employees can remain effective at their jobs in an ever-changing world, while also keeping organizations at the forefront of their industries.
Looking for additional help with upskilling and reskilling? Check out "3 Steps to Upskill and Reskill Employees."
| 2021-08-24T00:00:00 |
https://www.sumtotalsystems.com/blog/why-automate-upskilling-and-reskilling-now
|
[
{
"date": "2021/08/24",
"position": 48,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 45,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 48,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 49,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 50,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 44,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 48,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 49,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 49,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 48,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 49,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 44,
"query": "reskilling AI automation"
},
{
"date": "2021/08/24",
"position": 45,
"query": "reskilling AI automation"
}
] |
|
Top Employers for Artificial Intelligence (A.I.), Machine Learning Jobs
|
Top Employers for Artificial Intelligence (A.I.), Machine Learning Jobs
|
https://www.dice.com
|
[
"Nick Kolakowski"
] |
What does this tell us? Consulting, finance, healthcare, and technology companies are collecting a lot of the A.I. talent out there, for varying ...
|
Let’s say you’re interested in working with artificial intelligence (A.I.), perhaps with a focus on machine learning. Why not? Although the market for A.I. skills remains relatively small, those who’ve mastered the core concepts can potentially earn quite a bit in compensation—and work on some very cool projects.
Even if you have the right skills, though, where do you even begin looking for jobs? Many companies don’t bother to post their A.I.-related jobs publicly, since they often have a candidate already in mind for the role. Other A.I. specialists are drawn directly from academia.
Fortunately, there are some companies that post for A.I. jobs in significant quantities. CompTIA recently ran an analysis via Burning Glass, a database of millions of job postings, and discovered the top employers for A.I. job postings in August. Take a look:
What does this tell us? Consulting, finance, healthcare, and technology companies are collecting a lot of the A.I. talent out there, for varying reasons. Consulting companies no doubt wish to contract out these A.I. workers to clients, almost certainly at a healthy premium. Finance, healthcare, and technology companies, meanwhile, all have cutting-edge projects that desperately need A.I. talent—Amazon has a multitude of A.I. efforts in the proverbial pipeline, for example, including making its Alexa digital assistant even smarter.
Microsoft, Dell, and finance firms such as Wells Fargo, meanwhile, all need A.I. to help wrangle and mine huge databases for crucial insights. A recent study of A.I. use-cases showed many companies using the technology in the context of sales, CRM, chatbots, cybersecurity, and marketing automation—all things important to small and large organizations alike.
“The pandemic has accelerated digital transformation and changed how we work,” Khali Henderson, Senior Partner at BuzzTheory and vice chair of CompTIA’s Emerging Technology Community, commented as part of that study. “We learned—somewhat painfully—that traditional tech infrastructure doesn’t provide the agility, scalability and resilience we now require. Going forward, organizations will invest in technologies and services that power digital work, automation and human-machine collaboration. Emerging technologies like AI and IoT will be a big part of that investment, which IDC pegs at $656 billion globally this year.”
Of course, A.I. and machine learning jobs will go more mainstream over the next several years. According to Burning Glass, jobs that heavily involve machine learning are predicted to grow 76.3 percent over the next 10 years. More than 220,000 job postings over the past 12 months mentioned “machine learning” in a meaningful way—quite a large number for a “niche” technology. If you’re interested in A.I., just wait—the opportunities will likely increase.
| 2021-09-10T00:00:00 |
2021/09/10
|
https://www.dice.com/career-advice/top-employers-for-artificial-intelligence-a-i-machine-learning-jobs
|
[
{
"date": "2021/09/10",
"position": 95,
"query": "artificial intelligence employers"
}
] |
Privacy and artificial intelligence: challenges for protecting health ...
|
Privacy and artificial intelligence: challenges for protecting health information in a new era - BMC Medical Ethics
|
https://bmcmedethics.biomedcentral.com
|
[
"Murdoch",
"Health Law Institute",
"Faculty Of Law",
"University Of Alberta",
"Edmonton",
"Ab",
"Blake Murdoch",
"Search Author On",
"Author Information",
"Corresponding Author"
] |
Here, I outline and consider privacy concerns with commercial healthcare AI, focusing on both implementation and ongoing data security.
|
Concerns with access, use and control
AI have several unique characteristics compared with traditional health technologies. Notably, they can be prone to certain types of errors and biases [20,21,22,23], and sometimes cannot easily or even feasibly be supervised by human medical professionals. The latter is because of the “black box” problem, whereby learning algorithms’ methods and “reasoning” used for reaching their conclusions can be partially or entirely opaque to human observers [10, 18]. This opacity may also apply to how health and personal information is used and manipulated if appropriate safeguards are not in place. Notably, in response to this problem, many researchers have been developing interpretable forms of AI that will be easier to integrate into medical care [24]. Because of the unique features of AI, the regulatory systems used for approval and ongoing oversight will also need to be unique.
A significant portion of existing technology relating to machine learning and neural networks rests in the hands of large tech corporations. Google, Microsoft, IBM, Apple and other companies are all “preparing, in their own ways, bids on the future of health and on various aspects of the global healthcare industry [25].” Information sharing agreements can be used to grant these private institutions access to patient health information. Also, we know that some recent public–private partnerships for implementing machine learning have resulted in poor protection of privacy. For example, DeepMind, owned by Alphabet Inc. (hereinafter referred to as Google), partnered with the Royal Free London NHS Foundation Trust in 2016 to use machine learning to assist in the management of acute kidney injury [22]. Critics noted that patients were not afforded agency over the use of their information, nor were privacy impacts adequately discussed [22]. A senior advisor with England’s Department of Health said the patient info was obtained on an “inappropriate legal basis” [26]. Further controversy arose after Google subsequently took direct control over DeepMind’s app, effectively transferring control over stored patient data from the United Kingdom to the United States [27]. The ability to essentially “annex” mass quantities of private patient data to another jurisdiction is a new reality of big data and one at more risk of occurring when implementing commercial healthcare AI. The concentration of technological innovation and knowledge in big tech companies creates a power imbalance where public institutions can become more dependent and less an equal and willing partner in health tech implementation.
While some of these violations of patient privacy may have occurred in spite of existing privacy laws, regulations, and policies, it is clear from the DeepMind example that appropriate safeguards must be in place to maintain privacy and patient agency in the context of these public–private partnerships. Beyond the possibility for general abuses of power, AI pose a novel challenge because the algorithms often require access to large quantities of patient data, and may use the data in different ways over time [28]. The location and ownership of servers and computers that store and access patient health information for healthcare AI to use are important in these scenarios. Regulation should require that patient data remain in the jurisdiction from which it is obtained, with few exceptions.
Strong privacy protection is realizable when institutions are structurally encouraged to cooperate to ensure data protection by their very designs [29]. Commercial implementations of healthcare AI can be manageable for the purposes of protecting privacy, but it introduces competing goals. As we have seen, corporations may not be sufficiently encouraged to always maintain privacy protection if they can monetize the data or otherwise gain from them, and if the legal penalties are not high enough to offset this behaviour. Because of these and other concerns, there have been calls for greater systemic oversight of big data health research and technology [30].
Given we have already seen such examples of corporate abuse of patient health information, it is unsurprising that issues of public trust can arise. For example, a 2018 survey of four thousand American adults found that only 11% were willing to share health data with tech companies, versus 72% with physicians [31]. Moreover, only 31% were “somewhat confident” or “confident” in tech companies’ data security [28]. In some jurisdictions like the United States, this has not stopped hospitals from sharing patient data that is not fully anonymized with companies like Microsoft and IBM [32]. A public lack of trust might heighten public scrutiny of or even litigation against commercial implementations of healthcare AI.
The problem of reidentification
Another concern with big data use of commercial AI relates to the external risk of privacy breaches from highly sophisticated algorithmic systems themselves. Healthcare data breaches haven risen in many jurisdictions around the world, including the United States [33, 34], Canada [35,36,37], and Europe [38]. And while they may not be widely used by criminal hackers at this time, AI and other algorithms are contributing to a growing inability to protect health information [39, 40]. A number of recent studies have highlighted how emerging computational strategies can be used to identify individuals in health data repositories managed by public or private institutions [41]. And this is true even if the information has been anonymized and scrubbed of all identifiers [42]. A study by Na et al., for example, found that an algorithm could be used to re-identify 85.6% of adults and 69.8% of children in a physical activity cohort study, “despite data aggregation and removal of protected health information [43].” A 2018 study concluded that data collected by ancestry companies could be used to identify approximately 60% of Americans of European ancestry and that, in the near future, the percentage is likely to increase substantially [44]. Furthermore, a 2019 study successfully used a “linkage attack framework”—that is, an algorithm aimed at re-identifying anonymous health information—that can link online health data to real world people, demonstrating “the vulnerability of existing online health data [45].” And these are just a few examples of the developing approaches that have raised questions about the security of health information framed as being confidential. Indeed, it has been suggested that today’s “techniques of re-identification effectively nullify scrubbing and compromise privacy [46].”
This reality potentially increases the privacy risks of allowing private AI companies to control patient health information, even in circumstances where “anonymization” occurs. It also raises questions of liability, insurability and other practical issues that differ from instances where state institutions directly control patient data. Considering the variable and complex nature of the legal risk private AI developers and maintainers could take on when dealing with high quantities of patient data, carefully constructed contracts will need to be made delineating the rights and obligations of the parties involved, and liability for the various potential negative outcomes.
One way that developers of AI systems can potentially obviate continuing privacy concerns is through the use of generative data. Generative models develop the ability to generate realistic but synthetic patient data with no connection to real individuals [47, 48]. This can enable machine learning without the long term use of real patient data, though it may initially be needed to create the generative model.
| 2021-12-14T00:00:00 |
2021/12/14
|
https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-021-00687-3
|
[
{
"date": "2021/09/15",
"position": 58,
"query": "artificial intelligence healthcare"
}
] |
2021 Data/AI Salary Survey - O'Reilly Media
|
2021 Data/AI Salary Survey
|
https://www.oreilly.com
|
[
"Mike Loukides"
] |
The average annual salary for employees who worked in data or AI was $146,000. Most salaries were between $100,000 and $150,000 yearly (34%); ...
|
In June 2021, we asked the recipients of our Data & AI Newsletter to respond to a survey about compensation. The results gave us insight into what our subscribers are paid, where they’re located, what industries they work for, what their concerns are, and what sorts of career development opportunities they’re pursuing.
While it’s sadly premature to say that the survey took place at the end of the COVID-19 pandemic (though we can all hope), it took place at a time when restrictions were loosening: we were starting to go out in public, have parties, and in some cases even attend in-person conferences. The results then provide a place to start thinking about what effect the pandemic had on employment. There was a lot of uncertainty about stability, particularly at smaller companies: Would the company’s business model continue to be effective? Would your job still be there in a year? At the same time, employees were reluctant to look for new jobs, especially if they would require relocating—at least according to the rumor mill. Were those concerns reflected in new patterns for employment?
Executive Summary
The average salary for data and AI professionals who responded to the survey was $146,000.
The average change in compensation over the last three years was $9,252. This corresponds to an annual increase of 2.25%. However, 8% of the correspondents reported decreased compensation, and 18% reported no change.
We don’t see evidence of a “great resignation.” 22% of respondents said they intended to change jobs, roughly what we would have expected. Respondents seemed concerned about job security, probably because of the pandemic’s effect on the economy.
Average compensation was highest in California ($176,000), followed by Eastern Seaboard states like New York and Massachusetts.
Compensation for women was significantly lower than for men (84%). Salaries were lower regardless of education or job title. Women were more likely than men to have advanced degrees, particularly PhDs.
Many respondents acquired certifications. Cloud certifications, specifically in AWS and Microsoft Azure, were most strongly associated with salary increases.
Most respondents participated in training of some form. Learning new skills and improving old ones were the most common reasons for training, though hireability and job security were also factors. Company-provided training opportunities were most strongly associated with pay increases.
Demographics
The survey was publicized through O’Reilly’s Data & AI Newsletter and was limited to respondents in the United States and the United Kingdom. There were 3,136 valid responses, 2,778 from the US and 284 from the UK. This report focuses on the respondents from the US, with only limited attention paid to those from the UK. A small number of respondents (74) identified as residents of the US or UK, but their IP addresses indicated that they were located elsewhere. We didn’t use the data from these respondents; in practice, discarding this data had no effect on the results.
Of the 2,778 US respondents, 2,225 (81%) identified as men, and 383 (14%) identified as women (as identified by their preferred pronouns). 113 (4%) identified as “other,” and 14 (0.5%) used “they.”
The results are biased by the survey’s recipients (subscribers to O’Reilly’s Data & AI Newsletter). Our audience is particularly strong in the software (20% of respondents), computer hardware (4%), and computer security (2%) industries—over 25% of the total. Our audience is also strong in the states where these industries are concentrated: 42% of the US respondents lived in California (20%), New York (9%), Massachusetts (6%), and Texas (7%), though these states only make up 27% of the US population.
Compensation Basics
The average annual salary for employees who worked in data or AI was $146,000. Most salaries were between $100,000 and $150,000 yearly (34%); the next most common salary tier was from $150,000 to $200,000 (26%). Compensation depended strongly on location, with average salaries highest in California ($176,000).
The average salary change over the past three years was $9,252, which is 2.25% per year (assuming a final salary equal to the average). A small number of respondents (8%) reported salary decreases, and 18% reported no change. Economic uncertainty caused by the pandemic may be responsible for the declines in compensation. 19% reported increases of $5,000 to $10,000 over that period; 14% reported increases of over $25,000. A study by the IEEE suggests that the average salary for technical employees increased 3.6% per year, higher than our respondents indicated.
39% of respondents reported promotions in the past three years, and 37% reported changing employers during that period. 22% reported that they were considering changing jobs because their salaries hadn’t increased during the past year. Is this a sign of what some have called a “great resignation”? Common wisdom has it that technical employees change jobs every three to four years. LinkedIn and Indeed both recommend staying for at least three years, though they observe that younger employees change jobs more often. LinkedIn elsewhere states that the annual turnover rate for technology employees is 13.2%—which suggests that employees stay at their jobs for roughly seven and a half years. If that’s correct, the 37% that changed jobs over three years seems about right, and the 22% who said they “intend to leave their job due to a lack of compensation increase” doesn’t seem overly high. Keep in mind that intent to change and actual change are not the same—and that there are many reasons to change jobs aside from salary, including flexibility around working hours and working from home.
64% of the respondents took part in training or obtained certifications in the past year, and 31% reported spending over 100 hours in training programs, ranging from formal graduate degrees to reading blog posts. As we’ll see later, cloud certifications (specifically in AWS and Microsoft Azure) were the most popular and appeared to have the largest effect on salaries.
The reasons respondents gave for participating in training were surprisingly consistent. The vast majority reported that they wanted to learn new skills (91%) or improve existing skills (84%). Data and AI professionals are clearly interested in learning—and that learning is self-motivated, not imposed by management. Relatively few (22%) said that training was required by their job, and even fewer participated in training because they were concerned about losing their job (9%).
However, there were other motives at work. 56% of our respondents said that they wanted to increase their “job security,” which is at odds with the low number who were concerned about losing their job. And 73% reported that they engaged in training or obtained certifications to increase their “hireability,” which may suggest more concern about job stability than our respondents would admit. The pandemic was a threat to many businesses, and employees were justifiably concerned that their job could vanish after a bad pandemic-influenced quarter. A desire for increased hireability may also indicate that we’ll see more people looking to change jobs in the near future.
Finally, 61% of the respondents said that they participated in training or earned certifications because they wanted a salary increase or a promotion (“increase in job title/responsibilities”). It isn’t surprising that employees see training as a route to promotion—especially as companies that want to hire in fields like data science, machine learning, and AI contend with a shortage of qualified employees. Given the difficulty of hiring expertise from outside, we expect an increasing number of companies to grow their own ML and AI talent internally using training programs.
Salaries by Gender
To nobody’s surprise, our survey showed that data science and AI professionals are mostly male. The number of respondents tells the story by itself: only 14% identified as women, which is lower than we’d have guessed, though it’s roughly consistent with our conference attendance (back when we had live conferences) and roughly equivalent to other technical fields. A small number (5%) reported their preferred pronoun as “they” or Other, but this sample was too small to draw any significant comparisons about compensation.
Women’s salaries were sharply lower than men’s salaries, averaging $126,000 annually, or 84% of the average salary for men ($150,000). That differential held regardless of education, as Figure 1 shows: the average salary for a woman with a doctorate or master’s degree was 82% of the salary for a man with an equivalent degree. The difference wasn’t quite as high for people with bachelor’s degrees or who were still students, but it was still significant: women with bachelor’s degrees or who were students earned 86% or 87% of the average salary for men. The difference in salaries was greatest between people who were self-taught: in that case, women’s salaries were 72% of men’s. An associate’s degree was the only degree for which women’s salaries were higher than men’s.
Figure 1. Women’s and men’s salaries by degree
Despite the salary differential, a higher percentage of women had advanced degrees than men: 16% of women had a doctorate, as opposed to 13% of men. And 47% of women had a master’s degree, as opposed to 46% of men. (If those percentages seem high, keep in mind that many professionals in data science and AI are escapees from academia.)
Women’s salaries also lagged men’s salaries when we compared women and men with similar job titles (see Figure 2). At the executive level, the average salary for women was $163,000 versus $205,000 for men (a 20% difference). At the director level, the difference was much smaller—$180,000 for women versus $184,000 for men—and women’s salaries were actually higher than those at the executive level. It’s easy to hypothesize about this difference, but we’re at a loss to explain it. For managers, women’s salaries were $143,000 versus $154,000 for men (a 7% difference).
Career advancement is also an issue: 18% of the women who participated in the survey were executives or directors, compared with 23% of the men.
Figure 2. Women’s and men’s salaries by job title
Before moving on from our consideration of the effect of gender on salary, let’s take a brief look at how salaries changed over the past three years. As Figure 3 shows, the percentage of men and women respondents who saw no change was virtually identical (18%). But more women than men saw their salaries decrease (10% versus 7%). Correspondingly, more men saw their salaries increase. Women were also more likely to have a smaller increase: 24% of women had an increase of under $5,000 versus 17% of men. At the high end of the salary spectrum, the difference between men and women was smaller, though still not zero: 19% of men saw their salaries increase by over $20,000, but only 18% of women did. So the most significant differences were in the midrange. One anomaly sticks out: a slightly higher percentage of women than men received salary increases in the $15,000 to $20,000 range (8% versus 6%).
Figure 3. Change in salary for women and men over three years
Salaries by Programming Language
When we looked at the most popular programming languages for data and AI practitioners, we didn’t see any surprises: Python was dominant (61%), followed by SQL (54%), JavaScript (32%), HTML (29%), Bash (29%), Java (24%), and R (20%). C++, C#, and C were further back in the list (12%, 12%, and 11%, respectively).
Discussing the connection between programming languages and salary is tricky because respondents were allowed to check multiple languages, and most did. But when we looked at the languages associated with the highest salaries, we got a significantly different list. The most widely used and popular languages, like Python ($150,000), SQL ($144,000), Java ($155,000), and JavaScript ($146,000), were solidly in the middle of the salary range. The outliers were Rust, which had the highest average salary (over $180,000), Go ($179,000), and Scala ($178,000). Other less common languages associated with high salaries were Erlang, Julia, Swift, and F#. Web languages (HTML, PHP, and CSS) were at the bottom (all around $135,000). See Figure 4 for the full list.
Figure 4. Salary vs. programming language
How do we explain this? It’s difficult to say that data and AI developers who use Rust command a higher salary, since most respondents checked several languages. But we believe that this data shows something significant. The supply of talent for newer languages like Rust and Go is relatively small. While there may not be a huge demand for data scientists who use these languages (yet), there’s clearly some demand—and with experienced Go and Rust programmers in short supply, they command a higher salary. Perhaps it is even simpler: regardless of the language someone will use at work, employers interpret knowledge of Rust and Go as a sign of competence and willingness to learn, which increases candidates’ value. A similar argument can be made for Scala, which is the native language for the widely used Spark platform. Languages like Python and SQL are table stakes: an applicant who can’t use them could easily be penalized, but competence doesn’t confer any special distinction.
One surprise is that 10% of the respondents said that they didn’t use any programming languages. We’re not sure what that means. It’s possible they worked entirely in Excel, which should be considered a programming language but often isn’t. It’s also possible that they were managers or executives who no longer did any programming.
Salaries by Tool and Platform
We also asked respondents what tools they used for statistics and machine learning and what platforms they used for data analytics and data management. We observed some of the same patterns that we saw with programming languages. And the same caution applies: respondents were allowed to select multiple answers to our questions about the tools and platforms that they use. (However, multiple answers weren’t as frequent as for programming languages.) In addition, if you’re familiar with tools and platforms for machine learning and statistics, you know that the boundary between them is fuzzy. Is Spark a tool or a platform? We considered it a platform, though two Spark libraries are in the list of tools. What about Kafka? A platform, clearly, but a platform for building data pipelines that’s qualitatively different from a platform like Ray, Spark, or Hadoop.
Just as with programming languages, we found that the most widely used tools and platforms were associated with midrange salaries; older tools, even if they’re still widely used, were associated with lower salaries; and some of the tools and platforms with the fewest users corresponded to the highest salaries. (See Figure 5 for the full list.)
The most common responses to the question about tools for machine learning or statistics were “I don’t use any tools” (40%) or Excel (31%). Ignoring the question of how one does machine learning or statistics without tools, we’ll only note that those who didn’t use tools had an average salary of $143,000, and Excel users had an average salary of $138,000—both below average. Stata ($120,000) was also at the bottom of the list; it’s an older package with relatively few users and is clearly falling out of favor.
The popular machine learning packages PyTorch (19% of users, $166,000 average salary), TensorFlow (20%, $164,000), and scikit-learn (27%, $157,000) occupied the middle ground. Those salaries were above the average for all respondents, which was pulled down by the large numbers who didn’t use tools or only used Excel. The highest salaries were associated with H2O (3%, $183,000), KNIME (2%, $180,000), Spark NLP (5%, $179,000), and Spark MLlib (8%, $175,000). It’s hard to trust conclusions based on 2% or 3% of the respondents, but it appears that salaries are higher for people who work with tools that have a lot of “buzz” but aren’t yet widely used. Employers pay a premium for specialized expertise.
Figure 5. Average salary by tools for statistics or machine learning
We see almost exactly the same thing when we look at data frameworks (Figure 6). Again, the most common response was from people who didn’t use a framework; that group also received the lowest salaries (30% of users, $133,000 average salary).
In 2021, Hadoop often seems like legacy software, but 15% of the respondents were working on the Hadoop platform, with an average salary of $166,000. That was above the average salary for all users and at the low end of the midrange for salaries sorted by platform.
The highest salaries were associated with Clicktale (now ContentSquare), a cloud-based analytics system for researching customer experience: only 0.2% of respondents use it, but they have an average salary of $225,000. Other frameworks associated with high salaries were Tecton (the commercial version of Michelangelo, at $218,000), Ray ($191,000), and Amundsen ($189,000). These frameworks had relatively few users—the most widely used in this group was Amundsen with 0.8% of respondents (and again, we caution against reading too much into results based on so few respondents). All of these platforms are relatively new, frequently discussed in the tech press and social media, and appear to be growing healthily. Kafka, Spark, Google BigQuery, and Dask were in the middle, with a lot of users (15%, 19%, 8%, and 5%) and above-average salaries ($179,000, $172,000, $170,000, and $170,000). Again, the most popular platforms occupied the middle of the range; experience with less frequently used and growing platforms commanded a premium.
Figure 6. Average salary by data framework or platform
Salaries by Industry
The greatest number of respondents worked in the software industry (20% of the total), followed by consulting (11%) and healthcare, banking, and education (each at 8%). Relatively few respondents listed themselves as consultants (also 2%), though consultancy tends to be cyclic, depending on current thinking on outsourcing, tax law, and other factors. The average income for consultants was $150,000, which is only slightly higher than the average for all respondents ($146,000). That may indicate that we’re currently in some kind of an equilibrium between consultants and in-house talent.
While data analysis has become essential to every kind of business and AI is finding many applications outside of computing, salaries were highest in the computer industry itself, as Figure 7 makes clear. For our purposes, the “computer industry” was divided into four segments: computer hardware, cloud services and hosting, security, and software. Average salaries in these industries ranged from $171,000 (for computer hardware) to $164,000 (for software). Salaries for the advertising industry (including social media) were surprisingly low, only $150,000.
Figure 7. Average salary by industry
Education and nonprofit organizations (including trade associations) were at the bottom end of the scale, with compensation just above $100,000 ($106,000 and $103,000, respectively). Salaries for technical workers in government were slightly higher ($124,000).
Salaries by State
When looking at data and AI practitioners geographically, there weren’t any big surprises. The states with the most respondents were California, New York, Texas, and Massachusetts. California accounted for 19% of the total, with over double the number of respondents from New York (8%). To understand how these four states dominate, remember that they make up 42% of our respondents but only 27% of the United States’ population.
Salaries in California were the highest, averaging $176,000. The Eastern Seaboard did well, with an average salary of $157,000 in Massachusetts (second highest). New York, Delaware, New Jersey, Maryland, and Washington, DC, all reported average salaries in the neighborhood of $150,000 (as did North Dakota, with five respondents). The average salary reported for Texas was $148,000, which is slightly above the national average but nevertheless seems on the low side for a state with a significant technology industry.
Salaries in the Pacific Northwest were not as high as we expected. Washington just barely made it into the top 10 in terms of the number of respondents, and average salaries in Washington and Oregon were $138,000 and $133,000, respectively. (See Figure 8 for the full list.)
The highest-paying jobs, with salaries over $300,000, were concentrated in California (5% of the state’s respondents) and Massachusetts (4%). There were a few interesting outliers: North Dakota and Nevada both had very few respondents, but each had one respondent making over $300,000. In Nevada, we’re guessing that’s someone who works for the casino industry—after all, the origins of probability and statistics are tied to gambling. Most states had no respondents with compensation over $300,000.
Figure 8. Average salary by state
The lowest salaries were, for the most part, from states with the fewest respondents. We’re reluctant to say more than that. These states typically had under 10 respondents, which means that averaging salaries is extremely noisy. For example, Alaska only had two respondents and an average salary of $75,000; Mississippi and Louisiana each only had five respondents, and Rhode Island only had three. In any of these states, one or two additional respondents at the executive level would have a huge effect on the states average. Furthermore, the averages in those states are so low that all (or almost all) respondents must be students, interns, or in entry-level positions. So we don’t think we can make any statement stronger than “the high paying jobs are where you’d expect them to be.”
Job Change by Salary
Despite the differences between states, we found that the desire to change jobs based on lack of compensation didn’t depend significantly on geography. There were outliers at both extremes, but they were all in states where the number of respondents was small and one or two people looking to change jobs would make a significant difference. It’s not terribly interesting to say that 24% of respondents from California intend to change jobs (only 2% above the national average); after all, you’d expect California to dominate. There may be a small signal from states like New York, with 232 respondents, of whom 27% intend to change jobs, or from a state like Virginia, with 137 respondents, of whom only 19% were thinking of changing. But again, these numbers aren’t much different from the total percentage of possible job changers.
If intent to change jobs due to compensation isn’t dependent on location, then what does it depend on? Salary. It’s not at all surprising that respondents with the lowest salaries (under $50,000/year) are highly motivated to change jobs (29%); this group is composed largely of students, interns, and others who are starting their careers. The group that showed the second highest desire to change jobs, however, had the highest salaries: over $400,000/year (27%). It’s an interesting pairing: those with the highest and lowest salaries were most intent on getting a salary increase.
26% of those with annual salaries between $50,000 and $100,000 indicated that they intend to change jobs because of compensation. For the remainder of the respondents (those with salaries between $100,000 and $400,000), the percentage who intend to change jobs was 22% or lower.
Salaries by Certification
Over a third of the respondents (37%) replied that they hadn’t obtained any certifications in the past year. The next biggest group replied “other” (14%), meaning that they had obtained certifications in the past year but not one of the certifications we listed. We allowed them to write in their own responses, and they shared 352 unique answers, ranging from vendor-specific certifications (e.g., DataRobot) to university degrees (e.g., University of Texas) to well-established certifications in any number of fields (e.g., Certified Information Systems Security Professional a.k.a. CISSP). While there were certainly cases where respondents used different words to describe the same thing, the amount of unique write-in responses reflects the great number of certifications available.
Cloud certifications were by far the most popular. The top certification was for AWS (3.9% obtained AWS Certified Solutions Architect-Associate), followed by Microsoft Azure (3.8% had AZ-900: Microsoft Azure Fundamentals), then two more AWS certifications and CompTIA’s Security+ certification (1% each). Keep in mind that 1% only represents 27 respondents, and all the other certifications had even fewer respondents.
As Figure 9 shows, the highest salaries were associated with AWS certifications, the Microsoft AZ-104 (Azure Administrator Associate) certification, and the CISSP security certification. The average salary for people listing these certifications was higher than the average salary for US respondents as a whole. And the average salary for respondents who wrote in a certification was slightly above the average for those who didn’t earn any certifications ($149,000 versus $143,000).
Figure 9. Average salary by certification earned
Certifications were also associated with salary increases (Figure 10). Again AWS and Microsoft Azure dominate, with Microsoft’s AZ-104 leading the way, followed by three AWS certifications. And on the whole, respondents with certifications appear to have received larger salary increases than those who didn’t earn any technical certifications.
Figure 10. Average salary change by certification
Google Cloud is an obvious omission from this story. While Google is the third-most-important cloud provider, only 26 respondents (roughly 1%) claimed any Google certification, all under the “Other” category.
Among our respondents, security certifications were relatively uncommon and didn’t appear to be associated with significantly higher salaries or salary increases. Cisco’s CCNP was associated with higher salary increases; respondents who earned the CompTIA Security+ or CISSP certifications received smaller increases. Does this reflect that management undervalues security training? If this hypothesis is correct, undervaluing security is clearly a significant mistake, given the ongoing importance of security and the possibility of new attacks against AI and other data-driven systems.
Cloud certifications clearly had the greatest effect on salary increases. With very few exceptions, any certification was better than no certification: respondents who wrote in a certification under “Other” averaged a $9,600 salary increase over the last few years, as opposed to $8,900 for respondents who didn’t obtain a certification and $9,300 for all respondents regardless of certification.
Training
Participating in training resulted in salary increases—but only for those who spent more than 100 hours in a training program. As Figure 11 shows, those respondents had an average salary increase of $11,000. This was also the largest group of respondents (19%). Respondents who only reported undertaking 1–19 hours of training (8%) saw lower salary increases, with an average of $7,100. It’s interesting that those who participated in 1–19 hours of training saw smaller increases than those who didn’t participate in training at all. It doesn’t make sense to speculate about this difference, but the data does make one thing clear: if you engage in training, be serious about it.
Figure 11. Average salary change vs. hours of training
We also asked what types of training respondents engaged in: whether it was company provided (for which there were three alternatives), a certification program, a conference, or some other kind of training (detailed in Figure 12). Respondents who took advantage of company-provided opportunities had the highest average salaries ($156,000, $150,000, and $149,000). Those who obtained certifications were next ($148,000). The results are similar if we look at salary increases over the past three years: Those who participated in various forms of company-offered training received increases between $11,000 and $10,000. Salary increases for respondents who obtained a certification were in the same range ($11,000).
Figure 12. Average salary change vs. type of training
The Last Word
Data and AI professionals—a rubric under which we include data scientists, data engineers, and specialists in AI and ML—are well-paid, reporting an average salary just under $150,000. However, there were sharp state-by-state differences: salaries were significantly higher in California, though the Northeast (with some exceptions) did well.
There were also significant differences between salaries for men and women. Men’s salaries were higher regardless of job title, regardless of training and regardless of academic degrees—even though women were more likely to have an advanced academic degree (PhD or master’s degree) than were men.
We don’t see evidence of a “great resignation.” Job turnover through the pandemic was roughly what we’d expect (perhaps slightly below normal). Respondents did appear to be concerned about job security, though they didn’t want to admit it explicitly. But with the exception of the least- and most-highly compensated respondents, the intent to change jobs because of salary was surprisingly consistent and nothing to be alarmed at.
Training was important, in part because it was associated with hireability and job security but more because respondents were genuinely interested in learning new skills and improving current ones. Cloud training, particularly in AWS and Microsoft Azure, was the most strongly associated with higher salary increases.
But perhaps we should leave the last word to our respondents. The final question in our survey asked what areas of technology would have the biggest effect on salary and promotions in the coming year. It wasn’t a surprise that most of the respondents said machine learning (63%)—these days, ML is the hottest topic in the data world. It was more of a surprise that “programming languages” was noted by just 34% of respondents. (Only “Other” received fewer responses—see Figure 13 for full details.) Our respondents clearly aren’t impressed by programming languages, even though the data suggests that employers are willing to pay a premium for Rust, Go, and Scala.
There’s another signal worth paying attention to if we look beyond the extremes. Data tools, cloud and containers, and automation were nearly tied (46, 47, and 44%). The cloud and containers category includes tools like Docker and Kubernetes, cloud providers like AWS and Microsoft Azure, and disciplines like MLOps. The tools category includes tools for building and maintaining data pipelines, like Kafka. “Automation” can mean a lot of things but in this context probably means automated training and deployment.
Figure 13. What technologies will have the biggest effect on compensation in the coming year?
We’ve argued for some time that operations—successfully deploying and managing applications in production—is the biggest issue facing ML practitioners in the coming years. If you want to stay on top of what’s happening in data, and if you want to maximize your job security, hireability, and salary, don’t just learn how to build AI models; learn how to deploy applications that live in the cloud.
In the classic movie The Graduate, one character famously says, “There’s a great future in plastics. Think about it.” In 2021, and without being anywhere near as repulsive, we’d say, “There’s a great future in the cloud. Think about it.”
| 2021-09-15T00:00:00 |
2021/09/15
|
https://www.oreilly.com/radar/2021-data-ai-salary-survey/
|
[
{
"date": "2021/09/15",
"position": 92,
"query": "artificial intelligence wages"
}
] |
SQ11. How has AI impacted socioeconomic relationships?
|
SQ11. How has AI impacted socioeconomic relationships?
|
https://ai100.stanford.edu
|
[] |
AI has not been responsible for large aggregate economic effects. But that may be because its impact is still relatively localized to narrow ...
|
The Story So Far
The recovery from the 2008–2009 recession was sluggish both in North America and Western Europe.4 Unemployment, which had spiked to multi-decade highs, came down only slowly. This weak recovery was happening at the same time as major innovations in the field of AI and a new wave of startup activity in high tech. To pick one example, it seemed like self-driving cars were just around the corner and would unleash a mass displacement of folks who drive vehicles for a living. So AI (sometimes confusingly referred to as “robots”) became a scapegoat for weak labor markets.5
To some extent, this narrative of “new technology is taking away jobs” has recent precedents—the two prior labor market recoveries, beginning in 1991 and 2001, also started out weak, and that weakness was subsequently associated with technological innovation.6 But the possibility of applying the same narrative to the 2008–2009 recession was quickly dispelled by the post-2009 data. Productivity—the amount of economic output that can be produced by a given amount of economic inputs—grew at an exceptionally slow rate during the 2010s, both in the US and in many other countries,7 suggesting job growth was weak because economic growth was weak, not because technology was eliminating jobs. Employment grew slowly, but so did GDP in western countries.8 And, after a decade of sluggish recovery, in early 2020 (on the eve of the COVID-19 pandemic), the share of prime-working-age Americans with a job reached its highest level since 2001.9 In Western Europe, that share hit its highest level since at least 2005. The layperson narrative of the interplay between artificial intelligence technology and the aggregate economy had run ahead, and to some extent become disconnected, from the reality that economists were measuring “on the ground”.
Data from the US Bureau of Labor Statistics shows that employment as a fraction of the population reached a 20-year high right before the pandemic, suggesting that the growth of AI is not yet producing large-scale unemployment. From: https://fred.stlouisfed.org/series/LNS12300060#0
In other words, worries by citizens, journalists and policymakers about widespread disruption of the global labor market by AI have been premature. Other forces have been much more disruptive to workers’ livelihoods. And, at least in the 2010s, the labor market has been far more capable of healing than many commentators expected.
AI and Inequality
AI has frequently been blamed for both rising inequality or stagnant wage growth, both in the United States and beyond. Given the history of skill-biased technological change may have played a role in generating inequality,10 this worry is reasonable to consider. Looking back, the evidence here is mixed, but it’s mostly clear that, in the grand scheme of rising inequality, AI has thus far played a very small role.
The first reason, most importantly, is that the bulk of the increase in economic inequality across many countries predates significant commercial use of AI. Arguably, it began in the 1980s.11 The causes for its increase over that period are hard to disentangle and are much debated—globalization, macroeconomic austerity, deregulation, technological innovation, and even changing social norms could all have played a role. Unless we are willing to call all of these disparate societal trends “AI,” there’s no way to pin current economic inequality on AI.
The second reason is that, even in the most recent decade, the most significant factors negatively impacting the labor market have not been AI related. Aggregate demand was weak in the US and many western countries during the early years of the decade, keeping wage growth weak (particularly for less educated workers). And, to some degree, impacts that are directly attributable to technology are not necessarily attributable to AI specifically; for example, consider the relatively large impact of technologies like camera phones, which wiped out the large photography firm Kodak.12
Localized Impact
In sectors where AI is more prevalent—software and financial services, for example—its labor-market impact is likely to be more meaningful. Yet even in those industries and US states where AI job postings (an imperfect proxy) are more prevalent, they only account for a one to three percent share of total postings. Global corporate investment in AI was $68 billion in 2020, which is a non-trivial sum but small in relative terms: gross private investment over all categories in the US alone was almost $4 trillion in 2020.13 It’s not always easy to differentiate AI’s impact from other, older forms of technological automation, but it likely reduces the amount of human labor going into repetitive tasks.14
How the Pie Is Sliced
Economists have historically viewed technology as increasing total economic value (making the pie bigger), while acknowledging that such growth can create winners and losers (some people may end up with smaller slices than when the entire pie was smaller). But it’s also conceivable that some new technologies, including AI, might end up simply reslicing a pie of unchanged size. Stated differently, these technologies might be adopted by firms simply to redistribute surplus/gains to their owners.15 That situation would parallel developments over recent decades like tax cuts and deregulation, which have had a small positive effect on economic growth at best16 but have asymmetrically benefited the higher end of the income and wealth distributions. In such a case, AI could have a big impact on the labor market and economy without registering any impact on productivity growth. No evidence of such a trend is yet apparent, but it may become so in the future and is worth watching closely.
Market Power
AI’s reliance on big data has led to concerns that monopolistic access to data disproportionately increases market power. If that’s correct, then, over time, firms that acquire particularly large amounts of data will capture monopoly profits at the expense of consumers, workers, and other firms.17 This explanation is often offered for the dominance of big tech by a small number of very large, very profitable firms. (And it might present an even bigger risk if “data monopolies” are allowed by regulators to reduce competition across a wider range of industries.) Yet over the past few decades, consolidation and market power have increased across a range of industries as diverse as airlines and cable providers—so, at the present moment, access to and ownership of data are at most just one factor driving growing concentration of wealth and power.18 Still, as data and AI propagate across more of the economy, data as a driver of economic concentration could become more significant.
The Future
To date, the economic significance of AI has been comparatively small—particularly relative to expectations, among both optimists and pessimists, of massive transformation of the economy. Other forces—globalization, the business cycle, and a pandemic—have had a much, much bigger and more intense impact in recent decades.
But the situation may very well change in the future, as the new technology permeates more and more of the economy and expands in flexibility and power. Economists have offered several explanations for this lag; other technologies that ultimately had a massive impact experienced a J-curve, where initial investment took decades to bear fruit.20 What should we expect in the context of AI?
First, there is a possibility that the pandemic will accelerate AI adoption; according to the World Economic Forum, business executives are currently expressing an intent to increase automation.21 Yet parallel worries during the prior economic expansion failed to materialize,22 and hard evidence of accelerating automation on an aggregate scale is hard to find.23
Second, AI will contend with another extremely powerful force: demographics. Populations are aging across the world. In some western countries, workforces are already shrinking. It may be that instead of “killing jobs,” AI will help alleviate the crunch of retiring workforces.24
Third, technological change takes place over a long time, oftentimes longer than expected.25 It took decades for electricity26 and the first wave of information technology27 to have a noticeable impact on economic data; any future wave of technological innovation is also unlikely to hit all corners of the economy at once. (This insight also helps to contextualize relative disappointment in areas like self-driving vehicles.28 Change can be slow, even when it’s real.) A “hot” labor market in which some sectors of the economy expand labor demand even as others shrink is a useful insurance policy against persistent technology-driven unemployment.
Fourth, AI and other cutting-edge technologies may end up driving inequality. We may eventually see technologically-driven mass unemployment. Even if jobs remain plentiful, the automation-resistant jobs might end up being primarily relatively low-paying service-sector jobs. In the middle of the 20th century, western governments encountered and mitigated such challenges via effective social policy and regulation; since the 1970s, they have been more reluctant to do so. To borrow a phrase from John Maynard Keynes, if AI really does end up increasing “economic possibilities for our grandchildren,”29 society and government will have it within their means to ensure those possibilities are shared equitably. For example, unconditional transfers such as universal basic income—which can be costly in a world dependent on human labor but could be quite affordable in a world of technology-fueled prosperity and are less of a disorganized patchwork than our current safety net—could play a significant role.30 But if policymakers under react, as they have to other economic and labor pressures buffeting workers over the past few decades, innovations may simply result in a pie that is sliced ever more unequally.
[1] https://penelope.uchicago.edu/Thayer/e/roman/texts/suetonius/12caesars/vespasian*.html
[2] https://en.wikipedia.org/wiki/Luddite
[3] https://conversableeconomist.blogspot.com/2014/12/automation-and-job-loss-fears-of-1964.html
[4] https://slate.com/business/2019/12/the-four-mistakes-that-turned-the-2010s-into-an-economic-tragedy.html
[5] https://money.cnn.com/2010/06/10/news/economy/unemployment_layoffs_structural.fortune/index.htm
[6] Dale W. Jorgenson, Mun S. Ho, and Kevin J. Stiroh, "A retrospective look at the U.S. productivity growth resurgence," Journal of Economic Perspectives, Volume 22, Number 1, Winter 2008 https://scholar.harvard.edu/files/jorgenson/files/retrosprctivelookusprodgrowthresurg_journaleconperspectives.pdf
[7] Karim Foda, "The productivity slump: a summary of the evidence," August 2016 https://www.brookings.edu/research/the-productivity-slump-a-summary-of-the-evidence/
[8] Edward P. Lazear and James R. Spletzer, "The United States Labor Market: Status Quo or A New Normal?" https://www.kansascityfed.org/documents/6938/Lazear_Spletzer_JH2012.pdf
[9] U.S. Bureau of Labor Statistics, Employment-Population Ratio - 25-54 Yrs., retrieved from Federal Reserve Bank of St. Louis August 26, 2021 https://fred.stlouisfed.org/series/LNS12300060
[10] This thesis is controversial: See David Card and John E. DiNardo, "Skill Biased Technological Change and Rising Wage Inequality: Some Problems and Puzzles," Journal of Labor Economics, Volume 20, October 2002 https://www.nber.org/papers/w8769; Daron Acemoglu, "Technical Change, Inequality, and the Labor Market," Journal of Economic Literature, Volume 40, Number 1, March 2002. https://www.aeaweb.org/articles?id=10.1257/0022051026976 .
[11] https://www.cbpp.org/research/poverty-and-inequality/a-guide-to-statistics-on-historical-trends-in-income-inequality
[12] https://techcrunch.com/2012/01/21/what-happened-to-kodaks-moment/
[13] Daniel Zhang, Saurabh Mishra, Erik Brynjolfsson, John Etchemendy, Deep Ganguli, Barbara Grosz, Terah Lyons, James Manyika, Juan Carlos Niebles, Michael Sellitto, Yoav Shoham, Jack Clark, and Raymond Perrault, “The AI Index 2021 Annual Report,” AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, March 2021 https://aiindex.stanford.edu/wp-content/uploads/2021/03/2021-AI-Index-Report_Master.pdf
[14] https://hbr.org/2016/12/wall-street-jobs-wont-be-spared-from-automation
[15] https://www.theguardian.com/technology/2019/apr/07/uk-businesses-using-artifical-intelligence-to-monitor-staff-activity
[16] https://www.everycrsreport.com/reports/R45736.html, https://www.nber.org/system/files/working_papers/w28411/w28411.pdf
[17] https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data
[18] https://www.imf.org/-/media/Files/Publications/WEO/2019/April/English/ch2.ashx
[19] https://www.nber.org/system/files/working_papers/w24001/w24001.pdf
[20] https://www.nber.org/system/files/working_papers/w25148/w25148.pdf
[21] https://www.weforum.org/press/2020/10/recession-and-automation-changes-our-future-of-work-but-there-are-jobs-coming-report-says-52c5162fce/, https://www.theguardian.com/technology/2020/nov/27/robots-replacing-jobs-automation-unemployment-us
[22] http://www3.weforum.org/docs/WEF_Future_of_Jobs.pdf
[23] https://www.economist.com/special-report/2021/04/08/robots-threaten-jobs-less-than-fearmongers-claim
[24] https://www.nber.org/digest/jul18/automation-can-be-response-aging-workforce
[25] https://www.scientificamerican.com/article/despite-what-you-might-think-major-technological-changes-are-coming-more-slowly-than-they-once-did/
[26] https://www.bbc.com/news/business-40673694
[27] Daniel E. Sichel and Stephen D. Oliner, "Information Technology and Productivity: Where are We Now and Where are We Going?" SSRN, May 2002 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=318692
[28] https://www.wired.com/story/future-of-transportation-self-driving-cars-reality-check/
[29] John Maynard Keynes, “Economic Possibilities for our Grandchildren (1930),” in Essays in Persuasion, Harcourt Brace, 1932, retrieved from https://www.aspeninstitute.org/wp-content/uploads/files/content/upload/Intro_and_Section_I.pdf
[30] Annie Lowrey, Give People Money: How A Universal Basic Income Would End Poverty, Revolutionize Work, And Remake The World, Crown Publishing, 2019 https://www.penguinrandomhouse.com/books/551618/give-people-money-by-annie-lowrey/
| 2021-01-01T00:00:00 |
2021/01/01
|
https://ai100.stanford.edu/gathering-strength-gathering-storms-one-hundred-year-study-artificial-intelligence-ai100-2021-1-1
|
[
{
"date": "2021/09/16",
"position": 88,
"query": "AI economic disruption"
},
{
"date": "2021/09/16",
"position": 83,
"query": "AI economic disruption"
},
{
"date": "2021/09/16",
"position": 86,
"query": "AI economic disruption"
},
{
"date": "2021/09/16",
"position": 88,
"query": "AI economic disruption"
}
] |
How Much Do Artificial Intelligence (A.I.) and Data Jobs Actually Pay?
|
How Much Do Artificial Intelligence (A.I.) and Data Jobs Actually Pay?
|
https://www.dice.com
|
[
"Nick Kolakowski"
] |
Salaries increased an average of 2.25 percent per year. Average compensation was highest in California ($176,000), which hosts many of the large ...
|
Artificial intelligence (A.I.) and data jobs are hot right now. But how much do these roles actually pay? A new report suggests that, in exchange for specializing in cutting-edge technology, you can earn quite a bit.
Specifically, O’Reilly pegs the average salary of data and A.I. professionals at $146,000 per year (that’s from 2,778 respondents in the U.S. and 284 in the U.K.). Salaries increased an average of 2.25 percent per year. Average compensation was highest in California ($176,000), which hosts many of the large companies that rely on A.I. and data expertise, such as Google and other Silicon Valley giants.
Moreover, respondents seemed happy in their roles. “We don’t see evidence of a ‘great resignation,’” the report added. “[Twenty-two percent] of respondents said they intended to change jobs, roughly what we would have expected. Respondents seemed concerned about job security, probably because of the pandemic’s effect on the economy.”
If you want to land a job in A.I. and/or data, the key is training. Some 64 percent of respondents participated in some kind of training or certification courses, with 31 percent saying they’d spent 100 hours in training over the past year. While 22 percent said that training was required by their job, a stunning 91 percent said they wanted to learn new skills, while 84 percent said they were driven by a desire to improve their existing skill-set.
“When we looked at the most popular programming languages for data and AI practitioners, we didn’t see any surprises,” the report continued. “Python was dominant (61%), followed by SQL (54%), JavaScript (32%), HTML (29%), Bash (29%), Java (24%), and R (20%). C++, C#, and C were further back in the list (12%, 12%, and 11%, respectively).”
There’s every possibility that A.I. and machine learning jobs will go more mainstream over the next several years, offering opportunity to more technologists to land high compensation by specializing in the field. According to Burning Glass, jobs that heavily involve machine learning are predicted to grow 76.3 percent over the next 10 years. More than 220,000 job postings over the past 12 months mentioned “machine learning” in a meaningful way—quite a large number for a “niche” technology. But in order to actually land an A.I. or a data-related job, you’ll need to know your stuff—which means lots of training.
| 2021-09-20T00:00:00 |
2021/09/20
|
https://www.dice.com/career-advice/how-much-do-artificial-intelligence-a-i-and-data-jobs-actually-pay
|
[
{
"date": "2021/09/20",
"position": 51,
"query": "AI wages"
},
{
"date": "2021/09/20",
"position": 90,
"query": "artificial intelligence wages"
},
{
"date": "2021/09/20",
"position": 48,
"query": "AI wages"
},
{
"date": "2021/09/20",
"position": 46,
"query": "AI wages"
},
{
"date": "2021/09/20",
"position": 39,
"query": "artificial intelligence wages"
},
{
"date": "2021/09/20",
"position": 77,
"query": "artificial intelligence wages"
},
{
"date": "2021/09/20",
"position": 85,
"query": "AI wages"
},
{
"date": "2021/09/20",
"position": 39,
"query": "artificial intelligence wages"
},
{
"date": "2021/09/20",
"position": 81,
"query": "artificial intelligence wages"
},
{
"date": "2021/09/20",
"position": 48,
"query": "AI wages"
},
{
"date": "2021/09/20",
"position": 88,
"query": "artificial intelligence wages"
},
{
"date": "2021/09/20",
"position": 84,
"query": "artificial intelligence wages"
},
{
"date": "2021/09/20",
"position": 89,
"query": "artificial intelligence wages"
},
{
"date": "2021/09/20",
"position": 46,
"query": "AI wages"
},
{
"date": "2021/09/20",
"position": 51,
"query": "AI wages"
},
{
"date": "2021/09/20",
"position": 89,
"query": "artificial intelligence wages"
},
{
"date": "2021/09/20",
"position": 47,
"query": "AI wages"
},
{
"date": "2021/09/20",
"position": 79,
"query": "artificial intelligence wages"
},
{
"date": "2021/09/20",
"position": 41,
"query": "AI wages"
},
{
"date": "2021/09/20",
"position": 83,
"query": "artificial intelligence wages"
},
{
"date": "2021/09/20",
"position": 82,
"query": "artificial intelligence wages"
}
] |
Patient apprehensions about the use of artificial intelligence in ...
|
Patient apprehensions about the use of artificial intelligence in healthcare
|
https://www.nature.com
|
[
"Richardson",
"Jordan P.",
"Biomedical Ethics Research Program",
"Mayo Clinic Rochester",
"Rocheste",
"Smith",
"Curtis",
"Watson",
"Zhu",
"Kern Center For The Science Of Healthcare Delivery"
] |
Our results indicate that patients have multiple concerns, including concerns related to the safety of AI, threats to patient choice, potential increases in ...
|
We conducted 15 focus groups with 87 participants between November 2019 and February 2020. Each focus group had between three and seven participants and lasted 90 min. Approximately half of our participants were female (49.4%) and the average age of participants was 53.5 years old. A majority of participants were white (93.1%) and non-Hispanic/Latino (94.3%). Most participants had an education level higher than a high school degree (87.3%). Approximately one in five participants had experience working in technology or computer science (19.5%) for an average of 17.6 years. Nearly half of our participants had experience working in healthcare or health science (44.8%) for an average of 17.1 years. No participants reported any prior experience with AI impacting their healthcare. A detailed description of these and other participant characteristics is presented in Table 1.
Table 1 Characteristics of 87 patients who participated in focus groups examining attitudes about the use of AI in healthcare. Full size table
In what follows, we describe several major themes that emerged during focus-group discussions of healthcare AI. These themes reflect multiple sources of patient concern and excitement about applications of AI in medicine. We found that, while patients are generally enthusiastic about the possibility of AI improving their care, they are also concerned about the safety and oversight of healthcare AI. We describe these concerns about AI safety, the importance of patient choice, concerns about rising healthcare costs, questions about data quality, and view of the security of AI systems below.
Participants were excited about healthcare AI but wanted assurances about safety
In general, participants reported enthusiasm about the ability of AI to be a positive force in medicine. They felt healthcare AI was compatible with the goals of medicine: to heal as many patients as possible. Participants were supportive of developing AI tools for a variety of different healthcare applications.
I feel good about it. I think it has the ability to be better. I mean, it’s not a human. It’s got more data, so probably. … [I]t probably has more intelligence; it just has more information to work with to try to come up with a proper diagnosis. … I don’t think you will cure a lot of diseases without that advanced intellect. Obviously, we’ve come a long way with the human brain, but we could probably go a lot farther and speed the process with AI. (FG14).
Our participants also reported being aware that healthcare AI was still an emerging technology. As such, participants felt that it still had potential to be used in many different creative and positive ways.
This was often expressed as a sentiment of hopefulness, coupled with an acknowledgment that these benefits could only be realized through thoughtful implementation.
I feel like the future of AI just depends on how we choose to use it. The impact will be what we choose it to be. … Because it’s moldable, it’s not going to do anything that we don’t allow it to do. (FG7).
Participants urged caution in developing and implementing AI tools. They reported a need for a careful transition period to ensure that any AI tool used in their care is well-tested and accurate.
So when this intelligence is built we have to test it, right? We have to test it to make sure that it’s helping correctly, and that to me represents a big challenge and one we don’t wanna jump into and see what happens. We’ve gotta be very careful there. (FG4).
Participants also called for oversight and regulatory protections against potential harms. While participants often could not articulate what these regulations should include or who should enact them, they felt additional protections were necessary for AI tools.
It’s gonna do what it’s gonna do. I think that I look forward with excitement… but I agree that there’s definitely some flaws, and there definitely needs to be some markers in there, at least right now, that can also protect people. But it’s going to be positive if we can get those safe markers in. (FG11).
Patients expect their clinicians to ensure AI safety
Participants reported that they felt their clinicians should act as a safeguard to buffer patients from the potential harms that might result from mistakes made by healthcare AI. One way this was commonly expressed was in terms of their healthcare providers retaining final discretion over treatment plans and maintaining responsibility for patient care.
I believe the doctor always has the responsibility to be checking for you, and you’re his responsibility, you know? The AI is not responsible; that’s just a tool. (FG13).
Other participants were comfortable extending more authority to AI tools, but still calling for their providers to provide “checks and balances” or “second opinions” on recommendations generated by healthcare AI. Most participants felt strongly that an AI algorithm should not have the ability to act autonomously in a clinical setting, stressing that both treatment decisions and the monitoring of ongoing care should be done by a human provider.
I’d be okay with them telling a doctor what to do, but I don’t know that I’d want a machine doing the treatment, especially depending on what it is. Aiding, sure, they already do that with robotics and CT scans and all that, but I want a human there making sure that it’s doing what it’s supposed to. (FG13).
Participants also noted the uniqueness of each patient, and commented on the resultant individuality required in approaching medical decision making. They viewed the providers’ role in using AI as one of adapting AI recommendations to the patient’s unique personal situation, ensuring that they are not harmed and that patients follow through with clinical recommendations.
[I]t’s important to take into account that people, depending on what the AI comes out with, people might not be willing to go with what that is, they might need alternates. And also just the question of creativity, like what if the solution were actually something where you would have to think outside the box? … What if it’s something they haven’t encountered before? (FG13).
Preservation of patient choice and autonomy
Participants reported that the preservation of choice was an important factor in their overall comfort with applications of AI in healthcare. They felt that patients should have the right to choose to have an AI tool used in their care and be able to opt-out of AI involvement if they felt strongly.
I think it all comes back to choice, though, I think everybody’s getting the mentality that, and maybe I’m wrong, but that an AI is being pushed, but at the end of the day, our choice is still our choice, and it’s not being taken away. (FG 15).
In addition to the ability to choose whether an AI tool is used or not, participants wanted to have the ability to dispute the recommendations of an AI algorithm, or correct those recommendations if they believed they were in error. Participants were uncomfortable relying solely on recommendations made by an AI without being able to evaluate the rationale for those recommendations directly themselves.
So I’d rather know what they’re observing and, if it’s [AI] wrong, I would [want to] be able to correct it rather than have them just collect data and make assumptions. (FG 13).
Concerns about healthcare costs and insurance coverage
Participants also voiced concerns that AI tools might increase healthcare costs and that those costs might be passed on to patients. While participants acknowledged that AI might make the delivery of some healthcare services more efficient, they anticipated high development and deployment costs. They felt that adding another advanced technology would likely increase the cost of their healthcare.
So it sounds expensive, and health care is already fairly expensive. To go on his note, a lot of times you can get something that works just as well for a lot less or you could get something super fancy, that makes you think, hey I got this big fancy thing, but it really doesn’t do any better than the original cheaper version. (FG 9).
Additionally, participants worried about the impact that AI recommendations could have on what types of treatment their insurance providers would cover. For example, some participants were concerned that an AI algorithm might recommend a treatment that they could not afford. Similarly, others worried that insurance companies might chose to cover only those treatments that are supported by AI recommendations, thereby taking away some of the discretion traditionally reserved for physicians.
Is insurance only gonna cover what the machine says it is and not look for anything else? There is no reason for further diagnostics because the machine already did it? I mean we already have a situation in our healthcare system where money comes into play for diagnosing things. (FG 9).
Participants recognized how the ability of AI to draw connections and make highly accurate predictions from images or complex symptoms could be very helpful. However, they were concerned that new types of predictions could result in new forms of discrimination. Participants were especially worried that insurance companies would use AI to discover otherwise unknown medical information that could be used to deny coverage or increase premiums.
I mean, … that information is wonderful, but who’s gonna get it after the doctors look at it is my big thing. Is the insurance company gonna take it, and now all of a sudden … my premium doubles for health insurance? (FG1).
Ensuring data integrity
Participants considered the impact of data quality on AI tools and their recommendations, and had several concerns related to the way healthcare AI might be developed using flawed datasets, potentially resulting in harm to patients. They felt data from the electronic health record was not accurate enough to be reliable in teaching healthcare AI, citing personal experiences with errors they had found in their own health records.
There’s a lot of discrepancies in the medical record I must say, especially now that you can see your portal. I know I’ve seen things saying that certain things were done or about myself and procedures that were totally not true. So I’ve had a lot of different things in my medical chart that are inaccurate, very inaccurate, so if they’re training an artificial intelligence that this is facts, it’s like, well no. (FG 4).
Participants were also concerned about the possibility that AI tools might reinforce existing biases in healthcare datasets. They explained that this could happen as a result of an inherently biased learning dataset or from developers unintentionally incorporating their own bias into an AI algorithm.
Prejudices that people can have, like it could absorb those or it could be taught to work against them, like a lot of people who are overweight have said that their providers assume that that’s the cause and ignore doing other tests or pursuing other avenues, and if an AI wasn’t going to make the assumption that that was what was the problem, then that would be good, but if it was learning from people around it that it should make that assumption, then it would perpetuate the problem. (FG 13).
Risks of technology-dependent systems
Participants also expressed concerns about technological systems that might be highly dependent on new AI technologies and worried that some risks might be exacerbated if AI were to be widely deployed in medicine. One such concern was a worry about a systems-level crash or mass technological failure, and the impact this might have on a clinical system that is heavily reliant on AI tools.
I have some background in electronics, and one thing you can guarantee with electronics is they will fail. Might not be now, might never happen in 10, 20 years. The way things are made, ‘cause I’ve actually worked in the industry of making medical equipment, it’s all about using the cheapest method to get the end result. Well, electronics fail. They just do. (FG9).
Additionally, participants brought up examples of bad actors hacking into AI systems and manipulating these tools for nefarious purposes.
I was just gonna say another concern that I think I would have, just because of the way our world is evolving and revolving, is can that artificial intelligence be hacked? Who can control that? …I don’t know. Because any time you have a computerized program, I don’t care what anybody says, it can and it will get hacked because there’s always somebody that’s out there just to do evil rather than good. (FG15).
These concerns were compounded by the perception that healthcare providers could easily become overly dependent on AI tools, and over time might not be able to provide high-quality care if access to those healthcare AI tools was unavailable.
If they were to get hacked or a system goes down … like what’s the contingency plan, but what is the contingency plan? If you have all these doctors who are so used to having this artificial intelligence read all these, and they don’t have the skill of reading it, then what happens? (FG6).
| 2021-09-21T00:00:00 |
https://www.nature.com/articles/s41746-021-00509-1
|
[
{
"date": "2021/09/21",
"position": 21,
"query": "artificial intelligence healthcare"
}
] |
|
What Is Machine Learning (ML)? - IBM
|
What Is Machine Learning (ML)?
|
https://www.ibm.com
|
[] |
The biggest challenge with artificial intelligence and its effect on the job market will be helping people to transition to new roles that are ...
|
Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, neural networks is actually a sub-field of machine learning, and deep learning is a sub-field of neural networks.
The way in which deep learning and machine learning differ is in how each algorithm learns. "Deep" machine learning can use labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. The deep learning process can ingest unstructured data in its raw form (e.g., text or images), and it can automatically determine the set of features which distinguish different categories of data from one another. This eliminates some of the human intervention required and enables the use of large amounts of data. You can think of deep learning as "scalable machine learning" as Lex Fridman notes in this MIT lecture1.
Classical, or "non-deep," machine learning is more dependent on human intervention to learn. Human experts determine the set of features to understand the differences between data inputs, usually requiring more structured data to learn.
Neural networks, or artificial neural networks (ANNs), are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network by that node. The “deep” in deep learning is just referring to the number of layers in a neural network. A neural network that consists of more than three layers, which would be inclusive of the input and the output can be considered a deep learning algorithm or a deep neural network. A neural network that only has three layers is just a basic neural network.
Deep learning and neural networks are credited with accelerating progress in areas such as computer vision, natural language processing (NLP), and speech recognition.
See the blog post “AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference?” for a closer look at how the different concepts relate.
| 2021-09-22T00:00:00 |
https://www.ibm.com/think/topics/machine-learning
|
[
{
"date": "2021/09/22",
"position": 100,
"query": "machine learning job market"
},
{
"date": "2021/09/22",
"position": 100,
"query": "machine learning job market"
},
{
"date": "2021/09/22",
"position": 99,
"query": "machine learning job market"
}
] |
|
Artificial Intelligence (AI) in Healthcare | Oracle
|
Artificial Intelligence (AI) in Healthcare
|
https://www.oracle.com
|
[] |
The healthcare industry is transitioning to the cloud with AI capabilities for improved data management, accuracy, predictive modeling and ...
|
Why AI in Healthcare is The Future
The world is seeing a global shift towards artificial intelligence (AI) in the healthcare industry. Part of this stems from the healthcare industry’s transition towards a cloud environment for data management; with the cloud, data is now available on a real-time scale for further analysis. But rather than rely on staff to meticulously comb through data, artificial intelligence enables a much efficient—and in many cases, much more accurate—process.
As AI's capabilities increase, everything from internal operations to medical records benefits from integrating predictive modeling, automatic report generation, and other artificial intelligence features. Let's take a look at four specific use cases for AI in healthcare:
| 2021-10-06T00:00:00 |
https://www.oracle.com/artificial-intelligence/what-is-ai/ai-in-healthcare/
|
[
{
"date": "2021/10/06",
"position": 68,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 66,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 64,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 65,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 56,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 81,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 57,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 57,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 56,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 56,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 78,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 77,
"query": "AI healthcare"
},
{
"date": "2021/10/06",
"position": 78,
"query": "AI healthcare"
}
] |
|
The potential impact of AI on UK employment and the demand for skills
|
The potential impact of AI on UK employment and the demand for skills
|
https://www.gov.uk
|
[] |
This research provides estimates of the potential impact of artificial intelligence (AI) and related technologies on the UK labour market.
|
We use some essential cookies to make this website work. We’d like to set additional cookies to understand how you use GOV.UK, remember your settings and improve government services. We also use cookies set by other sites to help us deliver content from their services.
You have accepted additional cookies. You can change your cookie settings at any time.
You have rejected additional cookies. You can change your cookie settings at any time.
| 2021-10-08T00:00:00 |
https://www.gov.uk/government/publications/the-potential-impact-of-ai-on-uk-employment-and-the-demand-for-skills
|
[
{
"date": "2021/10/08",
"position": 85,
"query": "AI impact jobs"
},
{
"date": "2021/10/08",
"position": 84,
"query": "AI impact jobs"
},
{
"date": "2021/10/08",
"position": 82,
"query": "AI impact jobs"
},
{
"date": "2021/10/08",
"position": 75,
"query": "AI impact jobs"
},
{
"date": "2021/10/08",
"position": 81,
"query": "AI impact jobs"
},
{
"date": "2021/10/08",
"position": 80,
"query": "AI impact jobs"
},
{
"date": "2021/10/08",
"position": 76,
"query": "AI impact jobs"
}
] |
|
Radical Proposal: Universal Basic Income to Offset Job Losses Due ...
|
Radical Proposal: Universal Basic Income to Offset Job Losses Due to Automation
|
https://hai.stanford.edu
|
[
"Katharine Miller"
] |
Andrew Yang asserted that technological advances, including AI, will deprive one in three American workers of their jobs during the next 12 years.
|
During his campaign to become the 2020 Democratic candidate for president of the United States, Andrew Yang asserted that technological advances, including AI, will deprive one in three American workers of their jobs during the next 12 years.
To avoid the economic crisis that such job losses would precipitate, Yang proposed that the United States government give every American adult a monthly $1,000 “Freedom Dividend” – also called a universal basic income, or UBI.
Although Yang’s candidacy for president failed, his proposal for universal basic income continues to gain momentum across the country.
Indeed, Yang says, a recent poll found that about two-thirds of Americans are now in favor of universal basic income. “This is now very much a mainstream policy that we’re going to see implemented,” he says.
In fact, the U.S. government’s stimulus payments to ordinary Americans during the COVID-19 pandemic are considered a type of UBI; and several American cities, including Stockton, California; Los Angeles; Newark, New Jersey; and St. Paul, Minnesota, have recently piloted their own “guaranteed income” programs.
Yang says UBI payments would benefit every American by permanently growing the economy and creating millions of jobs. “A universal basic income would enable millions of Americans to meaningfully transition in the time of economic transformation, including that brought by AI,” he says, “and it would improve our strength, health, and mental health; kids’ ability to learn; our civic engagement; and our public trust, confidence, and optimism.”
Yang presented his UBI proposal anew at Stanford HAI’s “Policy and AI: Four Radical Proposals for a Better Society” conference, held Nov. 9-10, 2021. Below, watch the full presentation.
Universal Basic Income: How It Would Work
The idea of providing a guaranteed income to every adult has been around since the nation’s founding, when Thomas Paine proposed a “tax on heritage” to ensure a basic income for young people starting out without wealth. During the Nixon presidency, a version of universal basic income was almost passed by Congress. And Alaska has paid an annual oil dividend to all Alaska residents since 1982.
Like Alaska’s program, Yang’s proposed Freedom Dividend has almost no strings attached: All U.S. citizens over age 18 would be eligible.
Read all the proposals:
The cost of enacting Yang’s universal basic income proposal would be covered by implementing a value-added tax (VAT), which 160 other countries have already done, Yang says. A VAT taxes the value added by every company in the manufacturing and distribution supply chain for every product. And, Yang says in his new book, Forward: Notes on the Future of Our Democracy, “because [the VAT] is baked into the supply chain, it’s impossible to wriggle out of.”
Yang also theorizes that UBI payments will bring about cost savings due to increased employment, and reduced incarceration, homelessness, and emergency room use.
Critiques of UBI
Conservative critics worry that guaranteed income programs might affect recipients’ behavior by reducing their motivation to work or encouraging drug and alcohol abuse. But decades of research support Yang’s claim that cash transfers have minimal impact on these behaviors. And early results from a 2-year guaranteed income pilot program in Stockton suggest that programs targeted at low-income people reduce unemployment, allow people to pay down their debts, and improve their emotional well-being.
In addition, Americans’ recent experience with stimulus payments during the pandemic has shown us that this kind of support is immensely helpful, Yang says. “We know that money went to food, fuel, school supplies, and keeping a roof over people’s heads.”
Some other major criticisms of UBIs: They are extremely expensive and might exacerbate inequality rather than reduce it. Benefits programs that target aid to low-income people are effective, these critics say. And paying $1,000 monthly to every American citizen regardless of need is essentially regressive.
But Yang says making payments uniform reduces the stigma around need-based benefits. In addition, administrative issues often arise with targeted relief, he says, pointing to a recent pandemic-era program that authorized $46.5 billion in aid to renters, 89% of which hadn’t been distributed eight months later. “The fact is, the government does a poor job of administering a lot of these programs,” he says.
Putting cash into the hands of the people who are struggling is a clear win for people and families, Yang says. “Even if you distribute a uniform amount to everyone, the people at the bottom benefit the most immediately.”
Stanford HAI’s mission is to advance AI research, education, policy and practice to improve the human condition. Learn more.
| 2021-10-20T00:00:00 |
https://hai.stanford.edu/news/radical-proposal-universal-basic-income-offset-job-losses-due-automation
|
[
{
"date": "2021/10/20",
"position": 14,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 16,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 16,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 28,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 17,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 16,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 16,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 16,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 24,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 17,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 16,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 16,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 20,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 21,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 20,
"query": "universal basic income AI"
},
{
"date": "2021/10/20",
"position": 20,
"query": "universal basic income AI"
}
] |
|
EEOC Launches Initiative on Artificial Intelligence and Algorithmic ...
|
EEOC Launches Initiative on Artificial Intelligence and Algorithmic Fairness
|
https://www.eeoc.gov
|
[] |
WASHINGTON – The U.S. Equal Employment Opportunity Commission (EEOC) is launching an initiative to ensure that artificial intelligence (AI) and ...
|
WASHINGTON – The U.S. Equal Employment Opportunity Commission (EEOC) is launching an initiative to ensure that artificial intelligence (AI) and other emerging tools used in hiring and other employment decisions comply with federal civil rights laws that the agency enforces, EEOC Chair Charlotte A. Burrows announced today at a Genius Machines 2021 event.
“Artificial intelligence and algorithmic decision-making tools have great potential to improve our lives, including in the area of employment,” Burrows said. “At the same time, the EEOC is keenly aware that these tools may mask and perpetuate bias or create new discriminatory barriers to jobs. We must work to ensure that these new technologies do not become a high-tech pathway to discrimination.”
The initiative will examine more closely how technology is fundamentally changing the way employment decisions are made. It aims to guide applicants, employees, employers, and technology vendors in ensuring that these technologies are used fairly, consistent with federal equal employment opportunity laws.
“Bias in employment arising from the use of algorithms and AI falls squarely within the Commission’s priority to address systemic discrimination,” Burrows said. “While the technology may be evolving, anti-discrimination laws still apply. The EEOC will address workplace bias that violates federal civil rights laws regardless of the form it takes, and the agency is committed to helping employers understand how to benefit from these new technologies while also complying with employment laws.”
As part of the new initiative, the EEOC plans to:
Establish an internal working group to coordinate the agency’s work on the initiative;
Launch a series of listening sessions with key stakeholders about algorithmic tools and their employment ramifications;
Gather information about the adoption, design, and impact of hiring and other employment-related technologies;
Identify promising practices; and
Issue technical assistance to provide guidance on algorithmic fairness and the use of AI in employment decisions.
The new initiative will build on the previous work of the Commission in this area. The Commission has been examining the issue of AI, people analytics, and big data in hiring and other employment decisions since at least 2016. That year, the EEOC held a public meeting on the equal employment opportunity implications of big data in the workplace. Additionally, the EEOC’s systemic investigators received extensive training in 2021 on the use of AI in employment practices.
The EEOC advances opportunity in the workplace by enforcing federal laws prohibiting employment discrimination. More information is available at www.eeoc.gov. Stay connected with the latest EEOC news by subscribing to our email updates.
| 2021-10-28T00:00:00 |
https://www.eeoc.gov/newsroom/eeoc-launches-initiative-artificial-intelligence-and-algorithmic-fairness
|
[
{
"date": "2021/10/28",
"position": 40,
"query": "artificial intelligence employers"
},
{
"date": "2021/10/28",
"position": 54,
"query": "artificial intelligence employers"
},
{
"date": "2021/10/28",
"position": 99,
"query": "artificial intelligence employers"
},
{
"date": "2021/10/28",
"position": 58,
"query": "artificial intelligence employers"
},
{
"date": "2021/10/28",
"position": 60,
"query": "artificial intelligence employers"
},
{
"date": "2021/10/28",
"position": 47,
"query": "artificial intelligence employers"
},
{
"date": "2021/10/28",
"position": 62,
"query": "artificial intelligence employers"
},
{
"date": "2021/10/28",
"position": 49,
"query": "artificial intelligence employers"
},
{
"date": "2021/10/28",
"position": 44,
"query": "artificial intelligence employers"
},
{
"date": "2021/10/28",
"position": 51,
"query": "artificial intelligence employers"
},
{
"date": "2021/10/28",
"position": 46,
"query": "artificial intelligence employers"
}
] |
|
Automation and the Minimum Wage | St. Louis Fed
|
Automation and the Minimum Wage
|
https://www.stlouisfed.org
|
[
"Scott A. Wolla",
"F. Mindy Burton",
"About The Authors"
] |
And, because some tasks are more easily automated, as technology advances, it continues to change the mix of tasks completed by human labor and ...
|
Hard-working Americans deserve sufficient wages to put food on the table and keep a roof over their heads, without having to keep multiple jobs.
—President Joseph Biden
The minimum wage was created by President Franklin Delano Roosevelt in 1938 as part of the Fair Labor Standards Act, which set the wage at 25 cents. The wage applied to a relatively small group of workers and was established “to end starvation wages and intolerable hours,” especially for child laborers.2 The current federal minimum wage is $7.25 and was last changed in 2009.3
Although the nominal minimum wage (green line, Figure 1 below) has been raised several times, the purchasing power of the minimum wage (blue line, Figure 1 below) has varied over time.
For example, although the nominal minimum wage is at a historical high, the real (inflation adjusted) minimum wage is low relative to historical values and has the same purchasing value (red line, Figure 1 below) today as it did in 2007, 2005, 1990, 1989, and 1950. See where the blue line crosses the red line.
Figure 1: Real vs. Nominal Federal Minimum Hourly Wage NOTE: The graph shows the real federal minimum hourly wage (blue line) vs. the nominal federal minimum hourly wage (green line) SOURCE: FRED®, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/graph/?g=Go3J, accessed August 27, 2021.
The Textbook Approach
Although a simple supply and demand model fails to capture the nuances of a minimum wage policy, it is a useful place to start the discussion. The labor market has both a supply side (workers) and a demand side (employers seeking workers). The supply curves tell us how many workers are willing to work at any given wage. The demand curve tells us how many workers employers are willing to hire at any given wage. These curves intersect at the equilibrium wage (labeled W e on Figure 2 below). At equilibrium, the number of people wanting to work equals the number of workers employers want to employ.
Figure 2: The Textbook Approach
If the government thinks that the equilibrium wage is too low, then it might establish a minimum wage that is higher than the equilibrium wage, such as W m . In this case, the minimum wage acts as a price floor. The increase in the wage would cause the quantity of labor supplied (by workers) to increase, as people seeking the higher wage would enter the low-skilled labor market, indicated by the move from Q e to Q s . The quantity of labor demanded (by employers) would decrease as firms adjust to higher labor costs, indicated by the move from Q e to Q d .
The demand curve determines the number of workers hired by employers, and Q d notes the number of workers employed at the higher minimum wage. These workers earn a higher wage than before—they are the beneficiaries of the higher minimum wage.
However, the graph also shows the surplus of labor created by the higher minimum wage as the distance between Q d (employed workers) and Q s (workers seeking employment). In other words, the mandated minimum wage results in more workers seeking employment than there are jobs available—these workers are unemployed.
Models can miss nuances, however, because they are simplified versions of reality. For example, Nobel Prize laureate David Card and Alan Krueger used an innovative research method to examine the effects of an increase in the minimum wage in New Jersey and found no reduction in employment. Their research casts doubt on whether raising the minimum wage causes businesses to lay off workers.4 Although this study was specific to a time and place, it reminds us that even though models are useful tools, they have limitations.
So, let’s dig a little deeper.
Who Earns the Minimum Wage?
Although the minimum wage receives a lot of attention, the 1.1 million workers who earn the minimum wage (or below) make up only 1.5 percent of all hourly workers. Minimum wage workers tend to be young. Of the workers paid at or below the federal minimum wage, nearly half (48 percent) are under 25 years of age and 20 percent are teenagers.5 Even though teenagers are generally not the primary providers of household income, working helps them develop skills and experience.
Tradeoffs: The Pros and Cons of Raising the Federal Minimum Wage
As with the general public, economists fall on both sides of the minimum wage issue.
Proponents of a $15 minimum wage argue that increasing the wage would improve the lives of low-income workers, making it easier for them to afford food, rent, and other necessities, thereby raising their standard of living. Specifically, the Congressional Budget Office (CBO) estimates that raising the minimum wage to $15 per hour by 2025 would increase wages for at least 17 million people and reduce the number of people in poverty by 900,000.6 Proponents also suggest that raising the minimum wage will increase employee morale, reducing an employer’s turnover and hiring/training costs.
Opponents of a $15 minimum wage argue that the policy might hurt the very group of people it intends to help, by reducing the number of low-skilled jobs. Specifically, the CBO estimates that raising the minimum wage to $15 by 2025 would reduce employment by 1.4 million workers, or 0.9 percent.7 Economically, the number of workers hired at a particular business is based on how each worker’s labor contributes to that business’s total revenue. Economic theory suggests that if a worker’s labor results in at least enough additional revenue to offset the worker’s wage, the job will exist, and a worker will be employed. If the government mandates a higher wage, the revenue generated might fall short of labor costs for some workers. In short, for some firms it will no longer be feasible to keep the same number of workers at the higher wage. In this case, a policy intended to support low-income workers might result in job loss for low-skilled workers.
Some economists prefer the earned income tax credit (EITC) as a more targeted way to increase workers’ take-home pay and alleviate poverty. The EITC is an income subsidy (as a refundable tax credit) to low-income families. The benefits phase out slowly, so workers are not penalized as they earn more income. Proponents of the EITC see it as a better option because it directly supplements the incomes of the working poor while minimizing some of the unintended employment consequences associated with raising the minimum wage.
Automation and the Minimum Wage
Automation is the automatically controlled operation of an apparatus, process, or system by mechanical or electronic devices that takes the place of human labor. Automation is not new—it began with the industrial revolution and continued thereafter—but more and more tasks are becoming automated.
Robots and artificial intelligence are modern variations of automation. Although science fiction droids may come to mind when robots are mentioned, a robot is simply a device or algorithm that replaces human tasks. For example, a thermostat is a device that turns your furnace on or off as the temperature in your house changes. Unlike other physical capital or forms of technology, robots can be programmed to perform many repetitive tasks and do not need a human operator.
Often, we think of jobs being automated, but jobs are actually composed of a series of tasks. Each of these tasks can either be completed by human labor or by physical capital. Tasks (and jobs) that are routine and repetitive are more susceptible to automation. And, because some tasks are more easily automated, as technology advances, it continues to change the mix of tasks completed by human labor and those completed by automation.
Think of an assembly line used to manufacture automobiles. When the automotive industry began in the early 1900s, humans with tools completed nearly all steps of the process; but over time, robots took over many of the key steps—such as welding and painting. Parts of the process that require more dexterity, however, are still completed by humans.8
Complements and Substitutes
The move toward automation is driven by economics. A firm will consider substituting capital for labor for a given task when the marginal cost of producing goods with capital is less expensive than the marginal cost of producing goods with labor. This substitution is known as the displacement effect. There are two forces at work here:
The cost of capital: As technology advances and becomes more widely adopted, the cost of performing tasks with capital often falls (in inflation-adjusted terms). The cost of labor: As wages for low-skilled labor rise due to labor market conditions, regulations, or minimum wage laws, they can make the tasks workers do more susceptible to displacement.
Increasing the minimum wage provides economic incentives for firms to adopt new technologies that replace workers: That is, a higher minimum wage raises the cost of labor and increases the range of tasks that are susceptible to displacement by automation—especially the tasks of minimum wage jobs, which tend to be labor intensive and composed of low-skill tasks.
For example, consider the self-checkout lanes at grocery stores and digital kiosks at a fast-rood restaurant that substitute for employees, or the robot arms in an assembly line that complete simple tasks that human hands once did. As research bears out, increases in the minimum wage decrease the share of automatable employment held by low-skilled workers; it also suggests that the largest effects are felt in manufacturing and by older workers, females, and Black workers.9
Preparing for the Future
Although some fear a “robot apocalypse” that substitutes human labor with machines, it is important to remember that automation also complements human labor. Even though automation replaces low-skilled workers, it also creates job opportunities for higher-skilled workers.10 Using a historical example, the spreadsheet eliminated many “bookkeeper” jobs but created high-skilled jobs for people who could analyze the numbers, such as accountants and management consultants.11 In manufacturing, companies that eliminate some low-skill assembly line jobs will likely need higher-skilled employees to operate, troubleshoot, and maintain new equipment and reward those employees with higher wages.
Students can prepare for a changing world by building skills that complement technological change rather than those that can be easily substituted.
And, since routine and repetitive tasks are the most susceptible to displacement by automation, students and workers should also strive to develop their human capital by learning skills in areas that require a higher level of skill and training. Andrew McAfee recommends that students prepare for the future by pursuing a double major, one in the liberal arts (to develop critical thinking skills) and another in the sciences (to develop quantitative and technological skills).12 Economics is well positioned between these categories: It is a social science that explains human and institutional behavior, and it leans on quantitative (mathematical) models and data to explain and test theories.
Conclusion
Increasing the minimum wage is a controversial issue. Although a higher minimum wage can provide higher income for low-wage workers, it can also reduce the number of job opportunities for those workers. Some of the reduction in jobs occurs because a higher minimum wage increases production costs, causing firms to shift away from, or stop, production of some goods. A higher minimum wage can also result in employers using automation to replace more expensive human labor; however, automation can also create job opportunities for higher-skilled workers. Students concerned about automation can prepare for the future by acquiring job skills that complement technology.
| 2021-11-01T00:00:00 |
2021/11/01
|
https://www.stlouisfed.org/publications/page-one-economics/2021/11/01/automation-and-the-minimum-wage
|
[
{
"date": "2021/11/01",
"position": 99,
"query": "job automation statistics"
},
{
"date": "2021/11/01",
"position": 92,
"query": "job automation statistics"
},
{
"date": "2021/11/01",
"position": 93,
"query": "job automation statistics"
},
{
"date": "2021/11/01",
"position": 92,
"query": "job automation statistics"
},
{
"date": "2021/11/01",
"position": 61,
"query": "job automation statistics"
},
{
"date": "2021/11/01",
"position": 93,
"query": "job automation statistics"
},
{
"date": "2021/11/01",
"position": 90,
"query": "job automation statistics"
}
] |
Automation Doesn't Just Create or Destroy Jobs — It Transforms Them
|
Automation Doesn’t Just Create or Destroy Jobs — It Transforms Them
|
https://hbr.org
|
[
"Ashley Nunes",
"Is A Senior Research Associate At Harvard Law School. He Was Previously A Research Scientist At The Massachusetts Institute Of Technology."
] |
The World Economic Forum estimates that by 2025, technology will create at least 12 million more jobs than it destroys, a sign that in the long ...
|
The Covid-19 pandemic has accelerated the adoption of cutting-edge technologies. From contactless cashiers to welding drones to “chow bots” — machines that serve up salads on demand — automation is fundamentally transforming, rather than merely touching, every aspect of daily life. This prospect may well please consumers. Forsaking human folly for algorithmic (and mechanistic) perfection means better, cheaper, and faster service.
| 2021-11-02T00:00:00 |
2021/11/02
|
https://hbr.org/2021/11/automation-doesnt-just-create-or-destroy-jobs-it-transforms-them
|
[
{
"date": "2021/11/02",
"position": 28,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 25,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 28,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 78,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 27,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 27,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 28,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 26,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 75,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 28,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 70,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 77,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 27,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 67,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 73,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 63,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 27,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 72,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 65,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 66,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 69,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 26,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 70,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 27,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 27,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 67,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 68,
"query": "automation job displacement"
},
{
"date": "2021/11/02",
"position": 53,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 50,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 48,
"query": "job automation statistics"
},
{
"date": "2021/11/02",
"position": 49,
"query": "job automation statistics"
}
] |
Automation: 3 ways to ease job loss fears | The Enterprisers Project
|
Automation: 3 ways to ease job loss fears
|
https://enterprisersproject.com
|
[
"Balakrishna",
"Bali",
"November"
] |
Automation: 3 ways to ease job loss fears · 1. Emphasize the value of problem-finding · 2. Reskilling for life · 3. Learning for all. One reason ...
|
After experiencing huge economic disruption during the pandemic, many people worry that automation will make things worse. History shows that ever since the Industrial Revolution, automation has indeed disrupted employment and the wage structure, but it has also created more jobs with time. In fact, according to a World Economic Forum report published last year, 97 million jobs will be created by 2025, significantly exceeding the 85 million it expects will be lost.
Automation will reengineer processes, reorganize tasks, and eventually create more jobs – many of which we’ve never done before. These jobs will require new higher-order skills that will be in great demand and in short supply in all parts of the world.
This situation presents an opportunity to tackle both automation-related job loss and the global skills shortage at the same time. But doing so will require new skills and learning models from job seekers as well as education providers, business organizations, and other members of the employment ecosystem. Leaders must also address the automation-related job loss worries directly.
[ How can automation free up more staff time for innovation? Get the free eBook: Managing IT with Automation. ]
How to address job loss anxiety related to automation: 3 tips
Keep the following three principles in mind to help your team members become more comfortable sharing jobs and workplaces with software-powered machines and automatons.
1. Emphasize the value of problem-finding
New talent and seasoned employees alike need to focus on discovering and articulating problems. Intelligent digital technologies are already proving to be better than humans at resolving well-framed issues. But what they cannot do is find and frame problems that are yet unknown; these capabilities remain the exclusive domain of human beings.
Educational institutions, often bound by rigid curriculums designed to impart information about defined solutions, must pivot to inculcate creative thinking, curiosity, and exploration among students from an early age. Similarly, corporations should strive to develop an innovation culture as well as training programs such as design thinking workshops that inspire employees at all levels to become problem finders.
In the WEF research, 94 percent of the business leaders surveyed said they expected their employees to acquire new skills on the job; employees who show initiative in exploring new areas to expand into and deliver value will simultaneously upgrade their skills to improve their chances of moving up in their careers, despite automation.
[ Automation can help you hold onto talented people. Read also: How automation strategy can help you retain IT talent. ]
2. Reskilling for life
The best way to find employment even when jobs are being automated is to stay employable. With the half-life of jobs shrinking fast across industries (especially at junior levels), employees will need to upskill and reskill to move up.
This will happen not once or twice but several times in a typical career, turning employees into lifelong learners who are constantly learning in bite sizes. Since each employee will have unique learning needs in terms of content, timing, intensity, and duration, a one-size-fits-all solution will never work. Instead, individual employees should be empowered to decide their own terms of learning. Digital platforms such as Udemy, Coursera, Google Career Certificates, and others will be able to deliver modularized, personalized, micro-learning at scale.
3. Learning for all
One reason for the pervasive skills gap in our economy is that too many people have no access to digital education. To address this, policymakers, academic institutions, and industry leaders must come together to democratize education. They need to make higher education more inclusive so even those from less privileged backgrounds can benefit from it.
Skip to bottom of list More on automation
This is another area where digital learning platforms, with their flexibility and accessibility, can help. Corporations can also expand their talent pools by including candidates with non-STEM education by hiring based on skills – not only degrees – and by employing candidates from marginalized and disadvantaged backgrounds.
According to a recent survey from PwC, many workers continue to be concerned about losing their jobs to automation and believe their positions will become obsolete soon. Reskilling is the solution. With AI taking away menial tasks, people need to develop new higher-order skills to improve their confidence and readiness to take on bigger, more strategic roles. The interaction of digitally skilled workers and automation will unlock and expand human potential.
To prepare for this, companies should strive to create a culture of always-on, lifelong learning, including job rotations and training/apprenticeships, to ensure that their employees are better equipped for a digital future.
[ Where is your team's digital transformation work stalling? Get the eBook: What's slowing down your Digital Transformation? 8 questions to ask. ]
| 2021-11-04T00:00:00 |
https://enterprisersproject.com/article/2021/10/automation-3-ways-ease-job-loss-fears
|
[
{
"date": "2021/11/04",
"position": 74,
"query": "automation job displacement"
}
] |
|
Master's in Artificial Intelligence Salary - University of Bridgeport
|
Master’s in Artificial Intelligence Salary
|
https://www.bridgeport.edu
|
[
"University Of Bridgeport"
] |
Entry-level machine learning engineers earn an average of $93,412 annually. A late-career machine learning engineer can earn an average annual ...
|
Artificial intelligence (AI) and machine learning specialists have never been so in demand. As more and more industries rely on AI-based technology to reach customers and provide services, the need for professionals who hold a master’s degree in Artificial Intelligence will increase. In fact, between 2015 and 2019, the AI Industry grew 270%. According to the Bureau of Labor Statistics, this trend will continue over the next ten years, with employment predicted to grow by 22%, much faster than the national average for other careers.
While this job growth is notable, you may find yourself wondering what the master’s in Artificial Intelligence salary potential is. After all, job stability is a wonderful thing to have, but having a high earning potential can be just as important. Here are just a few careers that a master’s in Artificial Intelligence can prepare you for. As you’ll see, jobs in this field are well paid and ever-growing in demand.
Data Science Specialist
Data science specialists, or data scientists, use statistical computing and programming languages to analyze, process, and model data. After interpreting the results, they create plans of action for the companies or organizations that they work for.
Salary Range
Entry-level data scientists can earn an average of $85,004 annually. Over the course of their career, they can expect this salary to increase with years of experience. A late-career data scientist may expect to earn an average of $134,110 annually.
Machine Learning Engineer
Machine learning engineers work as parts of larger data science teams made up of data scientists, data analysts, data architects, and administrators. Their role is to create and design AI algorithms that are capable of analyzing data in order to learn and predict outcomes of specific situations. In other words, they build and design self-automated predictive models.
Salary Range
Entry-level machine learning engineers earn an average of $93,412 annually. A late-career machine learning engineer can earn an average annual salary of $148,869, depending on years of experience, location, and employer.
Software Engineer
Software engineers work with designers to develop and enhance the ways in which software functions. They may also work with programmers and coders to develop their software. They work with their clients or employers to design, develop, and test a system or application according to their needs and specifications. Software engineers will be expected to know programming languages such as C++, Java, Python, C#.Net, and Ruby. As they grow in experience and expertise, software engineers may be promoted to the role of “senior software engineer.” In this position, they will be expected to guide and review the work of junior engineers, in addition to their work writing and modifying software. This title also comes with an increase in annual salary.
Salary Range
The salary range for software engineers varies widely based on education level, years of experience, areas of expertise, and of course, employer. However, the average base salary for a software engineer is $87,824 per year. Late-career engineers earn an average of $115,716 annually.
Robotics Engineer
Robotics engineers design and maintain robots, their software, and their electronic systems. Some robotics engineers specialize in the mechanical manufacturing of robots, while others may focus on developing automated systems. Robotics engineers may work individually or as members of a team. They may also conduct research to aid in the design of efficient and reliable robotics systems. It should be noted that some employers may require their robotics engineers to pursue further study, up to a doctorate, in order to grow in their careers.
Salary Range
Entry-level robotics engineers can expect to earn an average salary of $77,427 per year. A mid-career robotics engineer can earn an average salary of $96,191 annually with a master’s in AI in hand. Late-career robotics engineers can expect to earn an average salary of $100,555. Robotics engineers can expect, however, to earn more should they be promoted to titles such as “Senior Robotics Engineer” over the course of their career. As with all positions in the artificial intelligence field, salary can depend on location, employer, and skill level.
Artificial Intelligence experts have been responsible for some of the last decade’s most impressive advancements in technology. From self-driving cars to digital assistants, the technological landscape has been molded and shaped by those who work in artificial intelligence and machine learning. With a master’s degree in Artificial Intelligence, you can be part of the next generation of innovators, expanding the capabilities of mankind by inventing creative solutions to life’s problems. Take the first step in your journey by earning your degree at University of Bridgeport.
Are you ready to launch a career in Artificial Intelligence? Request more information about University of Bridgeport’s master’s in artificial intelligence program!
| 2021-11-12T00:00:00 |
2021/11/12
|
https://www.bridgeport.edu/news/masters-in-artificial-intelligence-salary-expectations/
|
[
{
"date": "2021/11/12",
"position": 64,
"query": "artificial intelligence wages"
},
{
"date": "2021/11/12",
"position": 37,
"query": "artificial intelligence wages"
},
{
"date": "2021/11/12",
"position": 65,
"query": "artificial intelligence wages"
},
{
"date": "2021/11/12",
"position": 51,
"query": "artificial intelligence wages"
},
{
"date": "2021/11/12",
"position": 58,
"query": "artificial intelligence wages"
},
{
"date": "2021/11/12",
"position": 67,
"query": "artificial intelligence wages"
},
{
"date": "2021/11/12",
"position": 58,
"query": "artificial intelligence wages"
},
{
"date": "2021/11/12",
"position": 61,
"query": "artificial intelligence wages"
},
{
"date": "2021/11/12",
"position": 57,
"query": "artificial intelligence wages"
},
{
"date": "2021/11/12",
"position": 43,
"query": "artificial intelligence wages"
},
{
"date": "2021/11/12",
"position": 49,
"query": "artificial intelligence wages"
},
{
"date": "2021/11/12",
"position": 35,
"query": "artificial intelligence wages"
}
] |
Automation by the numbers: 11 stats to know | The Enterprisers Project
|
Automation by the numbers: 11 stats to know
|
https://enterprisersproject.com
|
[
"Kevin Casey",
"November"
] |
60 percent: Automation-related fears about job security are very real. In a PwC survey of 32,500 workers worldwide, roughly six out of every 10 ...
|
Automation pervades most other contemporary IT trends. Cloud and cloud-native? You’re talking about automation. Security and DevSecOps? Again, you’re talking about automation. Talent and culture? Yep, you’re still talking about automation.
IT is both in the midst of its own automation transformation and also an indispensable catalyst of organization-wide automation strategies. It’s actually difficult to exaggerate the role that automation is playing in businesses and industries of all kinds today. (The “automate all the things” meme has a kernel of truth to it.)
[ How can automation free up more staff time for innovation? Get the free eBook: Managing IT with Automation. ]
Automation’s reach extends beyond any single industry, business function, job role, or technology. Let’s dig into 11 statistics (and then some) that reflect the outsized influence of automation.
11 statistics on the state of automation
60 percent: Automation-related fears about job security are very real. In a PwC survey of 32,500 workers worldwide, roughly six out of every 10 (61 percent) of respondents said they’re worried that automation (of all types) is putting many jobs at risk in the future.
[ Also read: Automation vs. IT jobs: 3 ways leaders can address layoff fears. ]
X = Y: Bet you didn’t think we’d break out the algebra so soon, but here we are: X = Y, where X represents “time spent on current tasks at work by humans” and Y represents “time spent on current tasks at work” by machines. Those numbers will be more or less equal in 2025, according to the World Economic Forum. Today, people still spend more time on those same tasks than machines.
The organization’s Future of Jobs report notes that this balance won’t be distributed evenly, however, and work that requires critical thinking and problem-solving will still favor humans: “Algorithms and machines will be primarily focused on the tasks of information and data processing and retrieval, administrative tasks, and some aspects of traditional manual labor. The tasks where humans are expected to retain their comparative advantage include managing, advising, decision-making, reasoning, communicating, and interacting.”
97 million vs. 85 million: The same World Economic Forum report estimates that 85 million jobs will be displaced as a result of that shift in the division of work, with more of it moving to machines. That said, the report also predicts the creation of around 97 million new roles as a result of the same shift toward automation.
77 percent: Terms like reskilling and upskilling may sound buzzwordy, but the practices they represent are serious: The capabilities required for many of those new roles likely don’t exist today. The PwC survey found that most people are willing to learn and then some: 77 percent of respondents said they’re “ready to learn new skills or completely retrain,” and 40 percent of people reported “successfully improving their digital skills” during the pandemic.
1/4: While machine learning (ML) and other forms of artificial intelligence (AI) underpin a lot of IT automation discussion, it’s still relatively early days for this (big) category. Approximately one-quarter of respondents in O’Reilly’s AI Adoption in the Enterprise 2021 report indicated “mature” AI initiatives, defined in this context as having revenue-generating AI in production. That rate basically remains unchanged compared with O’Reilly’s 2020 report. O’Reilly received three times the number of responses with the same amount of promotion, something the firm attributes to growing interest in AI overall.
[ Related read: Why automation progress stalls: 3 hidden culture challenges. ]
35 percent: That’s the percentage of organizations in the O’Reilly report that are actively evaluating AI, meaning they’re running a trial or proof of concept. Another 26 percent said they’re “considering” AI but haven’t started any formal work. Just 13 percent said they’re not using AI now and aren’t considering doing so in the foreseeable future.
1: The #1 challenge for these organizations is hiring: There aren’t enough people with skills in AI, machine learning, and data science. You may think you’ve heard that line before, but a lack of AI skills only took over the top spot this year, unseating culture challenges (which dropped to number four, suggesting that people are starting to get used to the idea of increasing automation).
“That shortage has been predicted for several years; we’re finally seeing it,” writes report author Mike Loukides, VP of content strategy at O’Reilly Media.
58 percent: The pandemic appears to have sped up automation initiatives rather than slow them down: A 2020 global executive survey conducted by Deloitte found a 58 percent increase in intelligent automation initiatives underway compared with the previous year.
Skip to bottom of list More on automation
73 percent: That meant that nearly three out of four execs (73 percent) said they had an intelligent automation initiative in Deloitte’s 2020 survey.
Intelligent automation – what’s that? Definitions of intelligent automation vary, but the term usually refers to a combination of technologies, including but not limited to robotic process automation (RPA), low-code or no-code tools, and AI technologies. The “intelligent” part usually reflects that technologies like RPA can’t learn on their own like a machine learning algorithm, for example.
88 percent: IT automation isn’t just about AI and ML – it also includes the extensive, expanding roles that automation plays in modern environments. Think in terms of infrastructure as code, configuration management, security automation, container orchestration, and more. Kubernetes is now everyday IT jargon for that very reason. In Red Hat's 2021 State of Kubernetes Security report, 88 percent of the IT pros surveyed said their organizations are using Kubernetes, with 74 percent using it in production.
74 percent: The State of Kubernetes Security report also found that nearly three-quarters of respondents (74 percent) have adopted DevSecOps. A full 25 percent of organizations said their DevSecOps implementation is in an advanced stage that integrates and automates security throughout their software pipeline.
[ Want to learn more about building and deploying Kubernetes Operators? Get the free O'Reilly eBooks: Kubernetes Operators: Automating the Container Orchestration Platform and Kubernetes patterns for designing cloud-native apps. ]
| 2021-11-15T00:00:00 |
https://enterprisersproject.com/article/2021/11/automation-11-statistics-know
|
[
{
"date": "2021/11/15",
"position": 89,
"query": "job automation statistics"
},
{
"date": "2021/11/15",
"position": 69,
"query": "job automation statistics"
},
{
"date": "2021/11/15",
"position": 89,
"query": "job automation statistics"
}
] |
|
AI Will Create 97 Million Jobs, But Workers Don't Have the Skills ...
|
AI Will Create 97 Million Jobs, But Workers Don’t Have the Skills Required (Yet)
|
https://allwork.space
|
[
"Emma Ascott",
"Emma Ascott Is A Contributing Writer For Allwork.Space Based In Phoenix",
"Arizona. She Graduated Walter Cronkite At Arizona State University With A Bachelor S Degree In Journalism",
"Mass Communication In Emma Has Written About A Multitude Of Topics",
"Such As The Future Of Work",
"Politics",
"Social Justice",
"Money",
"Tech",
"Government Meetings"
] |
Despite the misconception that automation and AI decreases job opportunities, it may actually prompt a huge spike in new positions. According to ...
|
The World Economic Forum estimates that 85 million jobs will be replaced by machines with AI by 2025.
Despite the misconception that automation and AI decreases job opportunities, it may actually prompt a huge spike in new positions.
The question is no longer whether AI will change the workplace; it’s how companies can successfully use it in ways that enable – not replace – the human workforce.
Artificial intelligence technologies have reduced repetitive work and enhanced work efficiency, and as a result, almost every industry in the world is planning to leverage AI or has already implemented it in their business.
Despite the misconception that automation and AI decreases job opportunities, it may actually prompt a huge spike in new positions. According to the World Economic Forum Future of Jobs Report, 85 million jobs will be replaced by machines with AI by the year 2025.
Advertisements
While that statistic might make you uneasy, the same report states that 97 million new jobs will be created by 2025 due to AI.
The question is no longer whether AI will change the workplace; it’s how companies can successfully use it in ways that enable – not replace – the human workforce. AI will help to make humans faster, more efficient, and more productive.
Advertisements
What jobs will AI actually replace?
It’s true that AI will threaten some unskilled jobs through automation, but it will also potentially create different kinds of jobs that require new skill sets that will be developed through training.
AI can be used in manufacturing to make processes more efficient while also keeping human workers out of harm’s way. Opportunities to leverage AI and machine learning in manufacturing include product development, logistics optimization, predictive maintenance, and robotics.
In all likelihood, AI will take over jobs that require copying, pasting, transcribing, and typing.
In areas such as medical diagnosis, speech translation, and accounting, AI has outperformed humans in every way.
Advertisements
But AI will not be able to replace human judgment. AI is just the most recent manifestation of ongoing workplace evolution.
Finding the things that are worth teaching to the AI will still always be a job in the hands of humans.
While this might not be true forever, AI isn’t capable of developing complex strategies or thinking critically through complicated scenarios. There is a certain element of human intuition that’s critical, and many people will turn to AI to assist them in thinking through problems – but ultimately, humans will make the decision.
What jobs will AI create?
AI has accelerated demand for positions like machine learning engineers, robotics engineers, and data scientists over the last five years.
A number of positions are already developing around AI, such as AI trainers, individuals to support data science, and capabilities related to modeling, computational intelligence, machine learning, mathematics, psychology, linguistics, and neuroscience.
PwC estimated that the healthcare industry will benefit the most from the use of AI, where job opportunities could increase by nearly 1 million. In the near future, the requirement for AI-assisted healthcare technician jobs will see an upward surge.
AI is already playing a major role in the automated transportation sector. Companies like Uber and Google are investing millions of dollars into AI-driven self-driving cars and trucks. As this mode of transportation picks up in the future, it will create plenty of vacancies for AI and machine learning engineers.
As AI gets implemented in every industry, the demand for an AI maintenance workforce is going to skyrocket. Companies would need large amounts of AI developers and engineers to maintain their systems.
Advertisements
AI will help companies scale up. If AI and machine learning algorithms can competently use large amounts of big data, it will help companies perform better. It will also increase the employee retention rate and help in new customer acquisition, which will create new job opportunities as companies begin to scale up and grow.
Humans will need to up-skill
According to Mohit Josh at Bengaluru, India-based Infosys, there aren’t enough qualified workers available to fill all of the AI-related roles.
“Currently there is a widespread shortage of talent that possesses the knowledge and capabilities to properly build, fuel, and maintain these technologies within their organizations. The lack of well-trained professionals who can build and direct a company’s AI and digital transformation journeys noticeably hinders progress and continues to be a major hurdle for businesses,” he said.
Businesses should enforce on-the-job training and re-skilling. With the proper staff powering AI, employees are able to focus on other critical activities and boost productivity.
Advertisements
To prepare workers for the millions of new jobs that AI will inevitably create, organizations will have to provide significant resources for upskilling their workforces.
As well as this, employees will need to take personal responsibility for their career development in a world of rapid technological change.
Sean Chou, CEO of Chicago-based automation technology firm Catalytic, is a strong advocate of upskilling as a way to support both his organization and his employees in adapting to AI-enhanced ways of working.
“AI means less employee time spent searching for and manually formatting data, leaving them with more time for analysis and decision-making. This time savings also means that workers have more time for upskilling. That’s a win-win, since employees become more valuable to themselves and the company, while employee morale and retention are boosted,” Chou said.
Advertisements
| 2021-11-19T00:00:00 |
2021/11/19
|
https://allwork.space/2021/11/ai-will-create-97-million-jobs-but-workers-dont-have-the-skills-required-yet/
|
[
{
"date": "2021/11/19",
"position": 77,
"query": "AI job creation vs elimination"
},
{
"date": "2021/11/19",
"position": 78,
"query": "AI job creation vs elimination"
},
{
"date": "2021/11/19",
"position": 77,
"query": "AI job creation vs elimination"
},
{
"date": "2021/11/19",
"position": 78,
"query": "AI job creation vs elimination"
}
] |
Automation helped kill up to 70% of the US's middle-class jobs since ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
CMV: AI+automation will cause massive job displacement and even if it doesn't replace everyone, society will still suffer greatly. 90 upvotes ...
|
https://www.businessinsider.com/automation-labor-market-wage-inequality-middle-class-jobs-study-2021-6
No wonder there are not many good paying jobs around and wages have been mostly stagnant. Couple this with outsourcing and mass immigration and the result is the current job market.
| 2021-12-01T00:00:00 |
https://www.reddit.com/r/jobs/comments/r5uz1v/automation_helped_kill_up_to_70_of_the_uss/
|
[
{
"date": "2021/12/01",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2021/12/01",
"position": 70,
"query": "automation job displacement"
},
{
"date": "2021/12/01",
"position": 72,
"query": "automation job displacement"
}
] |
|
Sun Developing Machine Learning Workforce For Earth Science ...
|
Sun Developing Machine Learning Workforce For Earth Science Studies
|
https://science.gmu.edu
|
[
"Elizabeth Grisham"
] |
This project aims to build the fundamental curriculum materials and train the new-era workforce who will perform the critical geospatial data analysis work in ...
|
Ziheng Sun, Research Assistant Professor, Center for Spatial Information Science and Systems (CSISS),received $16,530 from the University of Washington on a subaward from the National Science Foundation for the project: "CyberTraining: Implementation: Medium: GeoSMART: Developing a Machine Learning workforce for earth science studies through training and curriculum development."
Sun will assist University of Washington (UW) researchers in hosting training sessions in hack weeks, co-draft the solicitation of Earth Science Information Partners (ESIP) Lab mini-grant funding opportunities, work with the UW team to organize, develop, and improve the training format and curriculum, serve as coordinator to engage the ESIP machine learning cluster with this project, help the UW team to establish the JupyterHub/Binders/Colab platform for hack weeks, teach community members how to manage their machine learning workflows using Geoweaver, and collect feedbacks from community members.
"This project aims to build the fundamental curriculum materials and train the new-era workforce who will perform the critical geospatial data analysis work in the future. The project will solve the problems caused by lack of systematic learning materials for Earth science students who wish to enrich their toolkit with modern technologies such as deep learning and cloud computing. In a long-term perspective, the project outputs will significantly contribute to relieve the workforce shortage stress against the high demand of Earth domain experts with a background on artificial intelligence," Sun said.
Funding for this award began in September 2021 and will end in late August 2022.
| 2021-12-02T00:00:00 |
https://science.gmu.edu/news/sun-developing-machine-learning-workforce-earth-science-studies
|
[
{
"date": "2021/12/02",
"position": 50,
"query": "machine learning workforce"
},
{
"date": "2021/12/02",
"position": 49,
"query": "machine learning workforce"
}
] |
|
AI and the Future of Work: What We Know Today - The Gradient
|
AI and the Future of Work: What We Know Today
|
https://thegradient.pub
|
[] |
There is no doubt that AI is becoming both more pervasive and more capable and able to support more types of tasks. The universe of industries ...
|
Part I: Impacts on Jobs and the Nature of Work
One of the most important issues in contemporary societies is the impact of automation and intelligent technologies on human work. Concerns with the impact of mechanization on jobs and unemployment go back centuries, at least since the late 1500’s, when Queen Elizabeth I turned down William Lee’s patent applications for an automated knitting machine for stockings because of fears that it might turn human knitters into paupers. [2] In 1936, an automotive industry manager at General Motors named D.L. Harder coined the term “automation” to refer to the automatic operation of machines in a factory setting. Ten years later, when he was a Vice President at Ford Motor company, he established an “Automation Department” which led to widespread usage of the term.[3]
The origins of intelligent automation trace back to US and British advances in fire-control radar for operating anti-aircraft guns to defend against German V-1 rockets and aircraft during World War II. After the war, these advances motivated the MIT mathematician Norbert Weiner to develop the concept of “cybernetics”, a theory of machines and their potential based on feedback loops, self-stabilizing systems, and the ability to autonomously lean and adapt behavior.[4] In parallel, the Dartmouth Summer Research Project on Artificial Intelligence workshop was held in 1956 and is recognized as the founding event of artificial intelligence as a research field.[5]
Since that decade, workplace automation, cybernetic-inspired advanced feedback systems for both analogue and digital machines, and digital computing based artificial intelligence (together with the overall field of computer science) have advanced in parallel and co-mingled with one another. Additionally, opposing views of these developments have co-existed with one side highlighting the positive potential for more capable and intelligent machines to serve, benefit and elevate humanity, and the other side highlighting the negative possibilities and threats including mass unemployment, physical harm and loss of control. There has been a steady stream of studies from the 1950’s to the present assessing the impacts of machine automation on the nature of work, jobs and employment, with each more recent study considering the capability enhancements of the newest generation of automated machines.
To contribute to a better understanding of the contemporary realities of AI workplace deployments, the two of us (Davenport and Miller) recently completed 29 case studies of people doing their everyday work with AI-enabled smart machines.[6] Twenty-three of these examples were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. In this essay, we compare our findings on job and workplace impacts to those reported in the MIT Task Force on the Work of the Future report, as we consider that to be the most comprehensive recent study on this topic.
MIT established its Work of the Future Task Force in 2018 as an “institute-wide initiative to understand how emerging technologies are changing the nature of human work and the skills required—and how we can design and leverage technological innovations for the benefit of everyone in society.”[7] The task force focused on understanding the current and forthcoming impacts of advanced automation—in particular, artificial intelligence and robotics—on the nature of work, on productivity and jobs, and on labor markets and employment trends. Their final report was published in November 2020 and mostly focused on the situation in the US, though their field studies also included visits to German factories. They also extensively reviewed research studies on the workforce, employment, and labor market impacts of automation—with emphasis on impacts of AI and robotics—from all over the world.[8] The task force effort included in-depth field studies in five industry areas: insurance, healthcare, vehicle driving (autonomous vehicles), warehousing and logistics, and manufacturing.[9]
Our case studies also included examples from insurance, healthcare, and manufacturing settings, as well as from various other service sector settings, other production operation settings, and field work settings for public safety and infrastructure operations. A listing of our case studies organized by the functional areas that the AI system is supporting is shown in Table 1 below.
AI system is used to support the following functional area in each case Case Name Sales and Business Development Morgan Stanley: Financial Advisors and The Next Best Action System ChowNow: Growth Operations and RingDNA Stitch Fix: AI-Assisted Clothing Stylists Arkansas State University: Fundraising with Gravyty Product Development Management Shopee: The Product Manager’s Role in AI Driven E-Commerce Administrative Operations Haven Life & Mass Mutual: The Digital Life Underwriter Radius Financial Group: Intelligent Mortgage Processing DBS Bank: AI-Driven Transaction Surveillance Medical Diagnosis and Treatment Record Coding with AI Dentsu: RPA for Citizen Automation Developers IT and analytics support 84.51° & Kroger: AutoML To Improve Data Science Productivity Mandiant: AI Support for Cyber Threat Attribution Customer and product support DBS Digibank India: Customer Science for Customer Service Intuit: AI-Assisted Writing with Writer.Com Lilt: The Computer-Assisted Translator Governance and Ethics Salesforce: Architects of Ethical AI Practices Professional Services (medical, legal) The Dermatologist: AI-Assisted Skin Imaging Good Doctor Technology: Intelligent Telemedicine in Southeast Asia Osler Works: The Transformation of Legal Service Delivery Manufacturing and Other Production Operations PCB Linear: AI Enabled Virtual Reality for Employee Training Seagate: Improving Automated Visual Inspection of Wafers and Fab Tooling with AI Stanford Health Care: Robotic Pharmacy Operations Fast Food Hamburger Outlets: Flippy--Robotic Assistants for Fast Food Preparation FarmWise: Digital Weeders for Robotic Weeding of Farm Fields Public Safety and Infrastructure Operations Wilmington, North Carolina Police Department: AI Driven Policing Certis: AI Support for the Multi-Faceted Security Guard at Jewel Changi Airport Southern California Edison: Machine Learning Safety Data Analytics for Front Line Accident Prevention Mass Bay Transit Authority: AI Assisted Diesel Oil Analysis for Train Maintenance Singapore Land Transport Authority: Rail Network Management in a Smart City
Table 1. Case studies in the forthcoming Davenport/Miller book, “Working with AI: Real Stories of Human Machine Collaboration”, forthcoming, MIT Press 2022.
We compare three of the six major conclusions extracted from the MIT task force final report with our case study findings where our study efforts overlap. In the first two areas, the task force’s conclusions are entirely consistent with what we found. In the third area we observed some differences between the MIT study’s findings and our own. We conclude with brief comments on the three other MIT Task Force conclusions that were beyond the scope of our study effort because we feel that these other national level policy issues are important for readers of this essay to be aware of. Quotations colored in blue are directly extracted from the MIT Work of the Future task force reports.
Technology Is Not Replacing Human Labor En Masse Anytime Soon
The first MIT task force conclusion addresses whether technology will replace human labor:
Technological change is simultaneously replacing existing work and creating new work. It is not eliminating work altogether.
No compelling historical or contemporary evidence suggests that technological advances are driving us toward a jobless future. On the contrary, we anticipate that in the next two decades, industrialized countries will have more job openings than workers to fill them, and that robotics and automation will play an increasingly crucial role in closing these gaps.
Their report acknowledges that intelligent machines are thus far capable of completing particular tasks. In most cases they cannot perform entire jobs, and are seldom able to automatically perform entire business processes. This makes it very unlikely that large-scale automation of human labor will take place over the next few decades. Indeed, in all of our case studies, the organizations involved said that AI and robotics had freed up workers to perform more complex tasks, and human workers had not lost jobs because of automation or AI. Several of the jobs we described across our collection of case examples are new and wouldn’t exist without AI. Many of the companies we profiled were growing (in part because of their effective use of digital and AI technologies), so they needed all their human workers to keep up with growth.
The MIT task force report highlights that from a broad economic perspective, growth of economies, demographics, and restrictive immigration policies will make it is far more likely that many jobs will go unfilled over the next few decades because labor is in short supply, at least in most of the world’s largest economies. World Bank statistics point in the same direction as this assessment, indicating that in 11 of the world’s 12 largest economies, fertility rates (births per woman) have been well below replacement levels and the proportion of the population age 65 and over has been on an increasing trajectory.[10] The inevitable implication is that at least in these 11 economies that are currently the world’s largest, human labor will increasingly be in short supply.
If industrialized countries will have more job openings than workers to fill them even with increasing workplace usage of AI and robotics and other types of technologies- as predicted by the MIT report- this suggests that signs of this trend should already be visible in countries where labor is already in especially short supply. In fact, recent work by the economists Daron Acemoglu and Pascual Restrepo provides evidence that, “Indeed, automation technologies have made much greater inroads in countries with more rapidly aging populations,” and that “the adoption and development of these technologies are receiving a powerful boost from demographic changes throughout the world and especially from rapidly-aging countries such as Germany, Japan and South Korea.”[11]
These reasons explain why the MIT task force report forecasts that neither the US nor the world at large is heading towards a future where there is not enough work for people to do as a result of greater usage of more sophisticated automation. More likely, in the decades to come, most of the world’s largest economies will make even greater use of AI, robotics and other existing types of automation to keep their economic output from shrinking given their slowing or even declining labor force participation rates. The remaining human labor will be indispensable in making this transition, though of course people will need the education and upskilling required to participate in this effort.
Organizational Changes from AI Are Happening Gradually
The second task force conclusion sheds light on the confusing dichotomy between the rapid pace of AI technology development as viewed from R&D and tech start-up announcements and the much slower pace at which organizations are able to absorb and productively harness AI and robotic capabilities. It is described here:
Momentous impacts of technological change are unfolding gradually.
Spectacular advances in computing and communications, robotics, AI, and manufacturing processes are reshaping industries as diverse as insurance, retail, healthcare, manufacturing, and logistics and transportation. But we observe substantial time lags, often on the scale of decades, from the birth of an invention to its broad commercialization, assimilation into business processes, widespread adoption, and impacts on the workforce … Indeed, the most profound labor market effects of new technology that we found were less due to robotics and AI than to the continuing diffusion of decades-old (though much-improved) technologies of the Internet, mobile and cloud computing, and mobile phones. This timescale of change provides the opportunity to craft policies, develop skills, and foment investments to constructively shape the trajectory of change toward the greatest social and economic benefit.
Across our 29 case studies, we also observed that new AI-based systems, their supporting platforms and infrastructure, and their surrounding work processes do not materialize easily or quickly. It takes time for an organization to orchestrate the deep collaborations and complex deployment efforts across the ecosystem of job roles that need to be involved within the company internally, and also within key external partner organizations (vendors, and sometimes customers).[12] Most of our case examples were the result of multi-year AI deployment and related process improvement efforts that started a well before we interviewed system users and other company personnel. Our interviews occurred after the companies had started to realize tangible improvements in efficiency and effectiveness after deploying and stabilizing a new AI system.
Major process changes, especially those involving AI systems, require up-front and ongoing investments, not only in the direct software and hardware aspects of the technology, but also in complementary enabling efforts (e.g., data acquisition, data engineering, infrastructure enhancement, new types of testing and validation efforts) and organizational adjustments (e.g., policy and process changes, new types of reviews for bias, fairness and other aspects of responsible AI usage) required to harness the new capabilities. On top of this, we learned from some of our case studies that managing the organizational change effort, educating the relevant parts of the workforce about using a new AI model, and gaining employee trust to use the new AI-enabled systems in their everyday work can sometimes take far longer than developing and testing the model.
Indeed, new AI developments are proceeding at breakneck speed, but bringing everything together across technology, people, and job roles in any real-world work setting is a very complex, time intensive and iterative undertaking that extends over longer time periods.
The MIT task force elaborated on this slow adaptation process:
As this report documents, the labor market impacts of technologies like AI and robotics are taking years to unfold … in each instance where the Task Force focused its expertise on specific technologies, we found technological change — while visible and auguring vast potential — moving less rapidly, and displacing fewer jobs, than portrayed in popular accounts. New technologies themselves are often astounding, but it can take decades from the birth of an invention to its commercialization, assimilation into business processes, standardization, widespread adoption, and broader impacts on the workforce.[13]
The “Productivity J-Curve” phenomenon described by Professor Erik Brynjolfsson and his colleagues[14] provides a conceptual framework and explanation for why the observed rate of AI and robotics assimilation within a specific company is a slow and gradual process. In their research brief prepared for the MIT task force, they described the productivity J-curve phenomenon as follows:
… new technologies take time to diffuse, to be implemented, and to reach their full economic potential. For a transformative new technology like AI, it is not enough to simply “pave the cow paths” by making existing systems better. Instead, productivity growth from new technologies depends on the invention and implementation of myriad complementary investments and adjustments. The result can be a productivity J-curve, where productivity initially falls, but then recovers as the gains from these intangible investments are harvested.
Productivity growth is the most important single driver of higher living standards, and technological progress is the primary engine of productivity growth. Thus, it is troubling that despite impressive advances in AI and digital technologies, measured productivity growth has slowed since 2005.
While there are many reasons for this, the most important is that technological advances typically don’t translate into improvements in productivity unless and until complementary innovations are developed. These include many intangible assets such as new business processes, business models, skills, techniques, and organizational cultures. The need for myriad complementary innovations is substantial, especially in the case of fundamental technology advancements such as AI. Yet, these complementary innovations can take years or even decades to create and implement; in the meantime, measured productivity growth can fall below trends as real resources are devoted to investments in these innovations. Eventually, productivity growth not only returns to normal but even exceeds its previous rates. This pattern is called a Productivity J-Curve.
Of course, a company can use a cloud-based or other AI application that does not require deep levels of integration with its existing technical infrastructure or processes. In such cases, the time span required to realize benefits could be short, and there may not be much or any productivity J-curve effect. We observed this type of situation in only two of our cases. One was with a private practice dermatologist who made use of a cloud-based AI-enabled system that his patients would also use at home so he could track the progress of high-risk dermatology cases.[15] In the second example, a company would simply send their documents requiring professional caliber translation to a cloud-based “translation-as-a-service” provider that combined human translators with AI support systems to achieve highly demanding or non-standard language translation in a way that is highly productive as well as nuanced, context-specific, and appropriately edited.[16] Such situations exist, but have an inherently smaller degree of impact on the company’s productive capabilities exactly because there is no deep integration with or improvements to existing infrastructure and processes. All of our other case examples required deep integration and/or major supporting changes to their internal processes that extended over multi-year time periods.
For example, while we were preparing our case study on AI-enabled financial transaction surveillance at DBS Bank, the company’s Chief Analytics Officer Sameer Gupta shared with us:
In my view, the reason this effort has been so successful is that it was not just been about analytics and AI. The team looked at how they run the entire function of transaction surveillance, transforming how they do this function end-to-end. This transformation has been supported, supplemented and augmented by analytics. But even with the best analytics models, had we not done all the other changes involved in this transformation, we would not have obtained the very impressive results that we ended up achieving. I see this as a successful business transformation that was augmented by analytics.
Sameer Gupta’s comment illustrates how AI system deployments require supporting implementation of many other business and organizational adjustments. In two of our case studies, large firms purchased a subsidiary to speed up their journey of capability development: MassMutual’s purchase of Haven Life for digital underwriting and Kroger’s purchase of 84.51o for data science capabilities. Despite acquiring entire organizational units with strong capabilities for creating and using the AI-based systems, the two large parent firms still had to go through a multi-year process to integrate both the technical capabilities as well as the “way of working” capabilities of these newly acquired subsidiaries into their overall ecosystems.
There is no escaping the reality that it takes substantial effort over an extended period of time for a company to make the necessary supporting complimentary investments and adjustments—above and beyond the direct investments and efforts required to design, build and deploy new AI systems—to assimilate these new technologies in ways that lead to substantial increases in productivity. Senior management in both the private and public sectors overseeing investments in AI and other advanced automation projects need to understand and anticipate the extended time periods required for an organization to make the necessary complementary investments, innovations and adjustments to go beyond merely deploying the technology. They also need to anticipate the productivity J-curve effect.
But it can be worth the effort. All of our case examples provide examples of productive capacity improvements either in terms of task or process output capacity, quality, or a combination of both.
Augmentation Much More So Than Automation
The MIT report emphasizes that augmentation is both a more desirable and more common outcome than large-scale automation. Augmentation is where employers create workplaces that combine smart machines with humans in close partnerships—symbiotically taking advantage of both human intelligence and machine intelligence. In other words, the AI system is used to complement the capabilities of a human worker (or vice versa). Economists use the term “automation” to refer to situations where the deployment of a machine in the workplace (including AI systems and robots) results in direct substitution, and the human worker who was previously doing that job is replaced by the machine (and the company may or may not redeploy that worker elsewhere within the company).[17] Most of our 29 case studies were examples of augmentation, and from what we observed, AI augmentation is largely quite successful. For the few cases that involved full automation of a limited set of tasks, there was still a need for humans to supervise and support the continuous improvement of the fully automated task or process, and to handle special cases and disruptions. The fact that both our case studies and the MIT task force's field studies observed far more instances of worker augmentation than full automation is consistent with the key points above that technology is not replacing human labor en masse anytime soon, and that changes in organizational “ways of working” are happening gradually.
The MIT task force effort included an imaginative and increasingly plausible view of how augmentation can be taken to even higher levels and expand into new types of applications. These ideas come from the task force research brief on “Artificial Intelligence and the Future of Work”.[18] The brief’s authors emphasize “thinking less about people OR computers and more about people AND computers.” They elaborated as follows:
By focusing on human-computer groups—superminds—we can move away from thinking of AI as a tool for replacing humans by automating tasks, to thinking of AI as a tool for augmenting humans by collaborating with them more effectively. As we’ve just seen, AI systems are better than humans at some tasks such as crunching numbers, finding patterns, and remembering information. Humans are better than AI systems at tasks that require general intelligence—including non-routine reasoning and defining abstractions—and interpersonal and physical skills that machines haven’t yet mastered. By working together, AI systems and humans can augment and complement each other’s skills.
The possibilities here go far beyond what most people usually think of when they hear a phrase like “putting humans in the loop.” Instead of AI technologies just being tools to augment individual humans, we believe that many of their most important uses will occur in the context of groups of humans. As the Internet has already demonstrated, another very important use of information technology—in addition to AI—will be providing hyperconnectivity: connecting people to other people, and often to computers, at much larger scales and in rich new ways that were never possible before.
That’s why we need to move from thinking about putting humans in the loop to putting computers in the group.
Using technology to attain new levels of collective coordination and intelligence is not at all far-fetched. We already see this occurring to some extent in real-world situations in two of our case studies. In our Singapore Land Transport Authority (LTA) Smart City rail network management example, the FASTER system predicts impending operational disturbances and supports operations center personnel in their efforts to bridge rail operators, LTA, and all relevant government authorities who would be involved in responding to any type of disruptive incident in the rail network system.[19] In our Certis Jewel Changi Airport example, an AI-enabled multi-service orchestration platform handles the consolidation and integration of all incoming video and sensor inputs and front-line worker reports from the 10 story Jewel mega-mall, generates alerts, and augments the ability of a centralized smart operations center team of humans to do complex situation assessment, response planning, and ‘man-in-the-middle’ coordination and communication across multiple stakeholders. These stakeholders include ground staff at Jewel mall, senior management at Certis and Jewel, and external parties including the ambulance teams, medical facilities, and the government authorities.[20] Both of these examples are in Singapore—a city-state economy and society making the future happen now. Over time, we expect to see more examples where smart-machine augmentation happens at the level of teams, departments, and entire business groups and organizations, and not just at the level of individual employees.
Part II: Skills, Labor Markets, and Policy Issues
Education, Training and Skill Development
The MIT task force examined education, training, and skill development issues to meet the needs of increasing usage of AI, robotics and other technologies, as well as to address opportunity creation for middle and low wage workers. They explained that their policy focus was on education and training for adults, particularly those whose work is more vulnerable to automation. This includes those in lower-wage jobs, those whose education pathways do not include four-year college degrees, and those who are displaced mid-career. Their key conclusion pertaining to education and training is as follows:
Fostering opportunity and economic mobility necessitates cultivating and refreshing worker skills.
Enabling workers to remain productive in a continuously evolving workplace requires empowering them with excellent skills programs at all stages of life: in primary and secondary schools, in vocational and college programs, and in ongoing adult training programs.
Of course, this conclusion is applicable to all segments of the workforce of any country’s economy, though it is especially important for those in the segments more vulnerable to being displaced by automation. Across the work settings of our 29 case studies, we only interviewed people who were gainfully employed, highly engaged with the technology and process changes that had successfully taken place in their work setting, and for the most part enthusiastic about working with or managing the new AI-enabled systems in their workplace. Obviously, the types of things we learned about training and skill development given this segment of people we encountered—all in relatively positive and promising employment situations-- will be quite different from recommendations focused on people in highly vulnerable segments or segments lacking promise and opportunity.
We also found that frontline workers, in order to collaborate effectively with smart machines in their work, needed new skills. However, in contrast to the MIT report, we did not find that those skills had been acquired through “excellent skills programs” sponsored by schools, colleges, and employers. Instead, most of the new skills were acquired on the job, or by employees who were personally motivated to acquire new skills on their own.
Leading higher education institutions have already started to adopt new AI-related skills programs, but many institutions still have not done so. While some progressive employers have internally implemented AI-related skills programs, many have not. As such, the majority of existing employees in most countries are largely on their own to develop these skills. Singapore is an exception due to the SkillsFuture national initiative to provide continuing education for the existing workforce, and also due to the AI Singapore educational outreach programs.
As the education and training policy recommendations of the MIT task force final report focused more on basic skill development needs for those at risk at being excluded from workforce participation or promising employment opportunities, they did not comment on the more nuanced issue of the importance of hybridized business and IT skills that we found in many of our case studies. In our examples, organizations had to deepen their internal capabilities in IT and expand into related areas for digital transformation and data science/AI. Frontline system users had to learn how to work with the systems. Supervisors and frontline managers had to work through the process changes and learn how to manage in the new setting. Technology staff had to hybridize their skills to include business and domain understanding. Business users had to develop digital thinking and technological savviness. In addition, workers needed to move into new types of roles which spanned and integrated business and technology (for example, product management, data governance, ethical AI practices).
While both self-motivated learning and IT/business hybridization are not easy to accomplish, they are relatively straightforward to do successfully for those in the workforce with the highest levels of education (undergraduate degrees and post-graduate degrees). In fact, the MIT task force report shows that in recent decades, at least in US labor markets, those in the workforce with highest levels of education have mostly done well.[21]
A Warning About Polarization of Labor Markets
Our focused set of case studies did not address long-term economic, employment and labor market issues. But the MIT Work of the Future task force analyzed US economy and labor market trends over decades leading up to the present, highlighting the stark realities of employment polarization and diverging job quality. They spotlighted the decline in the proportion of “middle-skill jobs” in the US labor market and the fact that wages for those in low-skilled occupations have stagnated for several decades. The task force explained the situation as follows[22]:
This ongoing process of machine substitution for routine human labor tends to increase the productivity of educated workers whose jobs rely on information, calculation, problem-solving, and communication — workers in medicine, marketing, design, and research, for example. It simultaneously displaces the middle-skill workers who in many cases provided these information-gathering, organizational, and calculation tasks. These include sales workers, office workers, administrative support workers, and assembly line production positions.
Ironically, digitalization has had the smallest impact on the tasks of workers in low-paid manual and service jobs, such as food service workers, cleaners, janitors, landscapers, security guards, home health aides, vehicle drivers, and numerous entertainment and recreation workers. Performing these jobs demands physical dexterity, visual recognition, face-to-face communications, and situational adaptability, which remain largely out of reach of current hardware and software but are readily accomplished by adults with modest levels of education. As middle-skill occupations have declined, manual and service occupations have become an increasingly central job category for those with high school or lower education. This polarization likely will not come to a halt any time soon.
The task force’s observation that US labor market employment polarization has been the status quo situation for over four decades now—and that the degree of polarization is more extreme in the US than in other advanced economies that have experienced positive productivity growth over past decades—led to their three additional conclusions:
Rising labor productivity has not translated into broad increases in incomes because labor market institutions and policies have fallen into disrepair.
Improving the quality of jobs requires innovation in labor market institutions.
Investing in innovation will drive new job creation, speed growth, and meet rising competitive challenges.
We feel these additional national-level policy conclusions made by the task force are important to highlight here for readers of this essay. These additional three conclusions, when combined with the other conclusions discussed above, set the stage for what is perhaps the strongest statement in their final report[23]:
Yet, if our research did not confirm the dystopian vision of robots ushering workers off of factory floors or artificial intelligence rendering superfluous human expertise and judgment, it did uncover something equally pernicious: Amidst a technological ecosystem delivering rising productivity, and an economy generating plenty of jobs (at least until the COVID-19 crisis), we found a labor market in which the fruits are so unequally distributed, so skewed toward the top, that the majority of workers have tasted only a tiny morsel of a vast harvest.[24]
These conclusions are the foundations of important warning statements made by the MIT task force team that need to be heeded by senior managers, C-suite executives and board of director members in the private sector as well as by civil servants and elected government officials. Even though their statements are directly aimed at the situation in the US, the threats associated with excluding major segments of the workforce from sharing the fruits of productivity improvement and wealth creation apply to managers and government officials in all countries. The task force’s final report cautioned[25]:
Where innovation fails to drive opportunity, however, it generates a palpable fear of the future: the suspicion that technological progress will make the country wealthier while threatening livelihoods of many. This fear exacts a high price: political and regional divisions, distrust of institutions, and mistrust of innovation itself.
The last four decades of economic history give credence to that fear. The central challenge ahead, indeed the work of the future, is to advance labor market opportunity to meet, complement, and shape technological innovations. This drive will require innovating in our labor market institutions by modernizing the laws, policies, norms, organizations, and enterprises that set the “rules of the game.”
Part III: Conclusion
The value of our 29 case studies summarized in Table 1 is that they provide real-world examples in every-day operational work settings of how people are already successfully collaborating with smart machines to improve business capabilities. This is not the future of work. It is already happening now in a growing number of organizations.
There is no doubt that AI is becoming both more pervasive and more capable and able to support more types of tasks. The universe of industries and jobs that already make use of AI as part of daily work is large and is growing rapidly. We foresee that in the coming years, many more workers will be asked or even required to work with smart machines. We suspect doing so would enhance their employability while refusing to do so would hinder their employment prospects.
Our examples, along with the conclusions and supporting field studies of the MIT Work of the Future Task Force report, are also a counter to the doom-and-gloom view that AI will destroy jobs. It is definitely changing work, but not destroying it.
As AI and other forms of advanced automation continue to diffuse across an entire economy, there are other aspects of the story that go beyond our case study documentation of successful and positive company efforts to combine human capabilities and machine capabilities to improve business performance in their workplace. The MIT Work of the Future task effort provides a broader view of these changes by illuminating the multiple sides of this unfolding journey from an economy-wide employment and labor market perspective. They conclude that we must drive forward with innovation- including the increased usage of AI and robotics- in order to create the new products, services and industries which lead to new job opportunities for all segments of the workforce, not just for those at the highest levels of attainment for income and education. Their conclusions also highlight the risks and perils of failing to advance labor market opportunity in light of the persistent labor market polarization, especially in the US.
[1] Our original essay on “AI and Jobs: Two Perspectives” was published on the AI Singapore website on 02 September 2021, https://aisingapore.org/2021/09/artificial-intelligence-and-work-two-perspectives/ . This is a substantially expanded and revised version prepared for The Gradient with guidance from the Gradient editorial staff.
[2] See Encyclopedia Britannica, “William Lee, English Inventor,” https://www.britannica.com/biography/William-Lee and the Wikipedia entry on William Lee, https://en.wikipedia.org/wiki/William_Lee_(inventor).
[3] Katsundo Hitomi, “Automation-its concept and a short history,”Technovation, 14(2), 1994.
[4] Thomas Rid, Rise of the Machines: A Cybernetic History, New York, W.W. Norton & Company, 2016.
[5] See the Wikipedia entry on the Dartmouth workshop, https://en.wikipedia.org/wiki/Dartmouth_workshop
[6] Thomas H. Davenport and Steven M. Miller. Working with AI: Real Stories of Human-Machine Collaboration. MIT Press, forthcoming in 2022.
[7] This description of the purpose of the MIT Future of Work Task Force is stated on their website homepage at https://workofthefuture.mit.edu/.
[8] David Autor, David Mindell, and Elisabeth Reynolds, “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines,” report published by the MIT Task Force on the Work of the Future, November 2020. We alter the order of presenting the six main conclusions of the MIT task force report.
[9] All of the MIT Future of Work Task Force field study reports can be found on either their Research Brief webpage https://workofthefuture.mit.edu/research-type/briefs/ or their Working Paper webpage https://workofthefuture.mit.edu/research-type/working-papers/ . These two webpages also include a number of other investigative studies that were part of the overall task force effort.
[10] See the World Bank Open Data website at https://data.worldbank.org/. According to their most recent data on GDP in current US dollars, the world’s 12 largest economies were US, China, Japan, Germany, India, UK, France, Italy, Brazil, Canada, Russian Republic and Korea Republic (S. Korea). Statistics on fertility rate (births per woman) and population ages 65 and above (% of total) are available through this website. The only one country of the 12 largest economies where the fertility rate was not well below replacement level was India, where it was 2.2 births per woman, and declining.
[11] Daron Acemoglu and Pascual Restrepo, “Demographics and Automation”, January 2021. Forthcoming in Review of Economic Studies.
[12] Thomas H. Davenport and Steven M. Miller, “Working with Smart Machines,” Asian Management Insights magazine, Vol 8 (1), May 2021, Singapore Management University. https://ink.library.smu.edu.sg/sis_research/5930/
[13] Autor, Mindell and Reynolds (2020).
[14] Erik Brynjolfsson, Seth Benzell, and Daniel Rock, “Understanding and Addressing the Modern Productivity Paradox” research brief published by the MIT Work of the Future Task Force, November 2020. A more in-depth analysis and explanation is given in Erik Brynjolfsson, Daniel Rock, and Chad Syverson, “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies,” American Economic Journal: Macroeconomics, Vol 13 (1), January 2021.
[15] See Tom Davenport, “The Future of Work Now: AI-Assisted Skin Imaging,” Forbes online column, November 03, 2020, https://www.forbes.com/sites/tomdavenport/2020/11/03/the-future-of-work-now-ai-assisted-skin-imaging/?sh=5606e8177e40
[16] See Tom Davenport, “The Future of Work Now: The Computer-Assisted Translator And Lilt,” Forbes online column, June 29, 2020, https://www.forbes.com/sites/tomdavenport/2020/06/29/the-future-of-work-now-the-computer-assisted-translator-and-lilt/?sh=461a4e453890
[17] See David Autor, Anna Salomons, Bryan Seegmiller, ”New Frontiers: The Origins and Content of New Work, 1940-2018”, Working paper, MIT Economics, 26 July 2021. In their conceptual framework, augmentation is where innovations complement labor outputs, and automation is where innovations substitute for labor inputs. They examine how employment levels by occupation have changed in response to augmenting versus automating innovations in combination with market demand increases and reductions.
[18] Thomas W. Malone, Daniela Rus, Robert Laubacher, “Artificial Intelligence and the Future of Work” research brief published by the MIT Task Force on Work of the Future, December 2020.
[19] See Steven M. Miller and Thomas H. Davenport, “A Smarter Way to Manage Mass Transit in a Smart City: Rail Network Management at Singapore’s Land Transport Authority,” AI Singapore website, May 27, 2021, https://aisingapore.org/2021/05/a-smarter-way-to-manage-mass-transit-in-a-smart-city-rail-network-management-at-singapores-land-transport-authority/
[20] See Thomas H. Davenport and Steven M. Miller, “The Future of Work Now: The Multi-Faceted Mall Security Guard At A Multi-Faceted Jewel, “ Forbes online column, September 28 2020. https://www.forbes.com/sites/tomdavenport/2020/09/28/the-future-of-work-now-the-multi-faceted-mall-security-guard-at-a-multi-faceted-jewel/?sh=2074b5ca72ff
[21] Autor, Mindell and Reynolds (2020), Section 2, Labor Markets and Growth; and Autor, Mindell and Reynolds (2019), Section 2, The Paradox of the Present, Section 3, Technology and Work: A Fraught History, and Section 4, Is This Time Different?
[22] Autor, Mindell and Reynolds (2020), Section 2.3, Employment Polarization and Diverging Job Quality.
[23] Autor, Mindell, and Reynolds (2020), Introduction.
[24] Autor, Mindell and Reynolds (2020) go on to explain in their introduction, “Four decades ago, for most U.S. workers, the trajectory of productivity growth diverged from the trajectory of wage growth. This decoupling had baleful economic and social consequences: low paid, insecure jobs held by non-college workers; low participation rates in the labor force; weak upward mobility across generations; and festering earnings and employment disparities among races that have not substantially improved in decades. While new technologies have contributed to these poor results, these outcomes were not an inevitable consequence of technological change, nor of globalization, nor of market forces. Similar pressures from digitalization and globalization affected most industrialized countries, and yet their labor markets fared better.”
[25] Autor, Mindell, and Reynolds (2020), Introduction.
Citation
For attribution in academic contexts or books, please cite this work as
Steven M Miller and Tom Davenport, "AI and the Future of Work: What We Know Today", The Gradient, 2021.
BibTeX citation:
@article{miller2021futureofowork,
author = {Miller, Steven and Davenport, Tom },
title = {AI and the Future of Work: What We Know Today},
journal = {The Gradient},
year = {2021},
howpublished = {\url{https://thegradient.pub/artificial-intelligence-and-work-two-perspectives/} },
}
If you enjoyed this piece and want to hear more, subscribe to the Gradient and follow us on Twitter.
| 2021-12-18T00:00:00 |
2021/12/18
|
https://thegradient.pub/artificial-intelligence-and-work-two-perspectives/
|
[
{
"date": "2021/12/18",
"position": 64,
"query": "future of work AI"
}
] |
24 Alarming Jobs Lost to Automation Statistics - GoRemotely
|
24 Alarming Jobs Lost to Automation Statistics
|
http://goremotely.net
|
[] |
According to estimates, 1.5 million jobs in the US will be lost to automation by 2030. Waiters and waitresses are at 73% risk of losing their ...
|
The rise of modern technology has impacted workers in various ways. Jobs lost to automation statistics uncover shocking figures. The 2020 stats estimate that nearly a quarter of jobs in the US are threatened by AI and robots. On the other hand, advanced hardware and software helped designers, engineers, and other white-collar workers to increase their productivity.
But blue-collar workers have faced the most challenging times. A vast number of factory workers, customer service agents, receptions, and other sorts of employees have been replaced by automation. It can’t be denied that some employees suffer automation more severely than others, the most vulnerable positions being those occupied by low-wage and male workers.
Top Technology Taking Over Jobs Statistics and Facts (Editor’s Choice)
25% of jobs are at considerable risk of automation.
Robots could replace 8.5% of the global labor force.
There are 2.25 million robots in the global labor force now.
According to estimates, 1.5 million jobs in the US will be lost to automation by 2030.
Waiters and waitresses are at 73% risk of losing their jobs due to automation in the UK.
The number of industrial robots has increased by 400% since the 1990s.
10.4 million jobs in the United Kingdom will be automated by 2030.
54% of medical assistants might be replaced by robots.
Jobs Lost to Robots Statistics and Facts
1. An astounding 236 million jobs in China will disappear due to AI and robots.
It’s no secret that robots are seizing humans’ jobs, and a growing number of factories are importing them. This specifically applies to factories that produce vehicles, home appliances, gadgets, etc. The leader among the countries that are replacing their workers this way is China.
Technology taking over jobs statistics reveal that a huge number of jobs in China will vanish throughout the next decade. This implies that 236 million professionals will have to seek another vocation. In Germany, that number is significantly smaller, amounting to “only” 14 million.
2. By 2030, 73 million jobs in the US will die out.
It’s not only jobs in China that are about to go extinct. The US will see 73 million jobs lost to automation by 2030. The same fate will befall 120 million jobs in India and 30 million in Japan. Mexico will face 18 million job losses.
3. 45–60% of EU employees might lose their jobs because of automation.
According to job automation statistics, up to two-thirds of employees in the European Union may be replaced by AI and robots. Even though such forecasts may seem gloomy, this may turn out to be much better than expected.
4. Approximately 96% of employees at risk of job loss may find a better job.
The effect of automation on jobs doesn’t always have to be adverse. Nearly all workers who are at risk of losing their job because of automation have a great chance to find a better one. Closing the Skill Gap 2020 program seeks to reskill 10 million people bound to lose their job. Such a plan will enable workers to gain additional skills and knowledge. In turn, they will be able to apply for better-paying jobs.
5. The need for advanced programming and IT skills will increase by 90% by 2030.
The impact of automation on the employment of IT experts is massive. AI and automation are rapidly increasing the need for advanced IT and software development skills. Throughout the next decade, the demand for these skills will skyrocket by 90% compared to 2016. Similarly, the need for basic digital skills will rise by 69% in the US and 65% in the EU. On the other hand, the demand for fundamental cognitive, manual, and physical skills will drop.
6. On average, 25% of jobs will change because of automation.
Automation of jobs statistics indicate that not only will some professions vanish, but duties and tasks will have to be modified. More precisely, besides the professions bound to disappear by 2030, 50–70% of assignments will considerably change due to automation trends.
7. AI might help US corporations save $1 trillion by 2030.
AI has immense potential when it comes to the finance sector. More precisely, jobs replaced by automation may enable companies to save $1 trillion in productivity gains and reduced expenses due to lower employment figures. Likewise, 30% of the business industry might be significantly affected, especially the positions of insurance agents and investment management jobs.
8. 54% of medical assistants may lose their jobs to AI by 2030.
Robots or AI could hardly replace highly skilled medical staff. But common illnesses shouldn’t be much of a problem for AI. Similarly, STAR (the Smart Tissue Autonomous Robot) is able to carry out simple surgical procedures more accurately than surgeons. Also, 29% of nurses may face a loss of jobs due to automation by 2030, as well as 54% of medical assistants.
Automation in Manufacturing Stats and Facts
9. Robots could replace 8.5% of the global labor force.
AI is steadily and rather rapidly taking over an array of jobs that humans do. The global workforce is bound to face a colossal number of job losses. By 2030, automation will replace as many as 20 million jobs worldwide, as AI replacing jobs statistics disclose. This figure accounts for 8.5% of the international workforce. China only will see the replacement of 14 million jobs.
10. China accounts for approximately 20% of robots across the world.
The largest country in Asia boasts of the greatest number of robots in the world, accounting for 20% of the total number. Since 2004, every new robot in the manufacturing and industry sector has replaced a median of 1.6 employees, as automation and job loss statistics indicate.
11. The overall number of robots around the world amounts to 2.25 million.
Presently, the world industries of all sorts see 2.25 million robots. This number has doubled since the previous decade and increased three times since 2000. By the next decade, it may reach 20 million, with 14 million in China only.
12. According to estimates, 1.5 million jobs in the US will be lost to automation by 2030.
Job losses due to automation stats predict that more than 1.5 million job positions will be obsolete because of robots and AI. The EU is bound to lose about 2 million job positions, while China will lose a staggering 12.5 million jobs. South Korea will lose 800,000, while automation will replace 3 million jobs in other countries.
13. The expenses of making robots dropped by 11% between 2011 and 2016.
Technology replacing jobs statistics show that the cost of automation has decreased significantly. Making robots is now cheaper than ever. Microchips’ processing power, smarter networks, and improved battery life have drastically impacted the manufacturing cost of robots.
14. Around 400,000 jobs in US factories became obsolete due to automation between 1990 and 2007.
Machines have been making jobs obsolete for centuries, and factories are no exception. According to automation in manufacturing statistics, around 400,000 job positions were lost because of automation between 1990 and 2007.
15. 42% of jobs are forever lost due to coronavirus.
The COVID-19 pandemic crisis has urged companies to rely on automation to avoid the spreading of the virus. At the peak of the pandemic, about 40 million jobs were lost. Even though a significant number of jobs will come back after the pandemic is over, many won’t. Jobs lost to automation statistics suggest that 42% of jobs are gone for good.
16. A company spending $100 on equipment has to pay around $3 in taxes.
The US government is doing its best to encourage businesses to automate by offering them tax relief whenever they purchase machinery or software. For the sake of comparison, a company that pays an employee $100 has to pay $30 in taxes. But a company that spends $100 on equipment pays only $3 in taxes.
Who is at the Greatest Danger of Job Loss to AI and Automation?
17. 7.4% of UK employees are at significant risk of losing their job.
The Office for National Statistics (ONS) examined the status of 20 million employees in the UK. The study found that the percentage of jobs at risk of automation amounted to 7.4%. This figure signifies that, out of 20 million respondents, nearly 1.5 million were at high risk of being replaced by AI and robots.
18. Waiters are 73% more likely to be replaced by robots.
Can you imagine that, in just a few years, you will no longer be served by human staff in a restaurant or a bar? Namely, 73% of waiters and waitresses are prone to replacement due to automation in years to come. Although jobs lost to robots statistics aren’t favorable for waiters, they are just a portion of 1.5 million people in the UK who are at high risk of losing their jobs to AI. Other professions from the same high-risk group are shelf fillers, bar staff, and primary sales occupations.
19. Medical practitioners in the UK have an 18% chance of losing their jobs to automation.
The figures are more friendly for medical practitioners. Robots taking over jobs statistics show that less than one-fifth of them are likely to be replaced by robots. The same applies to dental practitioners (21%), secondary and higher education teachers (21% and 20%, respectively), and senior professionals in education (20%).
20. 15.7% of people aged 20–24 are at risk of losing their jobs to automation.
No matter how odd it may sound, young people are more likely to be replaced by robots, as jobs lost to automation statistics show. Namely, nearly 16% of youngsters aged 20-24 may be replaced, as opposed to people aged 30-35 who have only a 1.3% chance to be replaced by robots.
21. 20% of Amazon’s workforce might be robots.
Three years ago, Amazon introduced over a whopping 50,000 new robots. This figure represents a 100% jump compared to 2016. Nowadays, estimations assume that robots make up one-fifth of the company’s workforce.
22. The number of industrial robots has increased by 400% since the 1990s.
Automation in manufacturing statistics reveal an incredible jump in the use of AI and robots in different industries. Namely, over 25 years ago, the number of industrial robots in the European Union amounted to 95,000. Now that figure has risen to 430,000, denoting an increase of 400% over a quarter of a century. In Germany only, 40% of those robots are currently used.
23. 10.4 million UK jobs will be automated by 2030.
Automation job loss statistics show that nearly 30% of jobs in the UK are bound to be automated in the next 10 years. This implies that 10.4 million jobs will see robots and AI instead of human workers by 2030. The most affected workers will be those who don’t have a higher level of education.
24. Non-white workers are over 40% at risk of losing their job.
Non-white employees will undoubtedly experience dire consequences of loss of jobs due to automation. African Americans are at 43.8% risk of being replaced, similarly to Hispanic workers with 47.3% at risk of job loss. Asians, on the other hand, are the least likely to be replaced (38.8%).
Jobs Lost to Automation Statistics—Conclusion
Ever since the Industrial Revolution, automation has been replacing humans. Even though the working class people feared for their jobs at the beginning, technological progress has undoubtedly benefited society. Soon after, no one was worried about the negative impact of innovations on people’s jobs.
However, workers have begun to see jobs lost to automation since 1980. One of the typical professions at the time, the elevator operator, was replaced by buttons installed in the elevators. Nowadays, a colossal number of job positions are at risk of becoming obsolete. According to projections, 1.5 million workers will lose their jobs to automation over the next 10 years in the US.
On the flip side, nearly all workers bound to be replaced will get a chance to apply for better positions. Closing the Skills Gap 2020 is a program specially dedicated to helping workers acquire new skills that will help them find a better job.
| 2022-01-03T00:00:00 |
http://goremotely.net/blog/jobs-lost-to-automation-statistics
|
[
{
"date": "2022/01/03",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 43,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 45,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 45,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 45,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 45,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 45,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 83,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 45,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 45,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 47,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 43,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 55,
"query": "job automation statistics"
},
{
"date": "2022/01/03",
"position": 52,
"query": "job automation statistics"
}
] |
|
Here Are The Top Jobs That Will Be Automated First - Allwork.Space
|
Here Are The Top Jobs That Will Be Automated First
|
https://allwork.space
|
[
"Daniel Lehewych",
"Daniel Has Been Freelance Writing For Over Years Now. He Cover Topics Ranging Politics",
"Philosophy",
"Culture",
"Current Events",
"To Health",
"Fitness",
"Medicine",
"Relationships",
"Mental Health. He Is Currently Completing A Master'S Degree In Philosophy At The Cuny Graduate Center In New York City"
] |
While automation is likely to eliminate many common jobs, it is also expected to create many jobs and replace the ones that have been lost.
|
Artificial intelligence doesn’t just loom over the horizon. It is here now and will expand indefinitely into the future.
The first jobs that will be automated are those which are most formulaic, such as jobs that entail repeating the same task repeatedly – like manufacturing jobs that create products on conveyor belts.
Most of the jobs that the Bureau of Labor Statistics predicts will decline in the next ten years will not experience a significant reduction. Instead, most of the jobs in these sectors are predicted to be retained.
Artificial intelligence doesn’t just loom over the horizon. It is here now and will expand indefinitely into the future.
The topic of AI has been prominent in popular media for nearly a decade now. However, a question of central importance in discussions surrounding AI has been around ever since humans began rapidly improving their technological capabilities during the Industrial Revolution.
Advertisements
“Which jobs will go first?”
This article will clear the air on which jobs are most likely to be automated first.
Advertisements
Before getting into particular industries, it should be noted that automation is unlikely to be the economic version of the Terminator –it is unlikely to be the actual version of the Terminator as well.
While automation is likely to eliminate many common jobs, it is also expected to create many jobs and replace the ones that have been lost. Total employment is predicted to rise even with the advent of automation, so this article shouldn’t be misconstrued as a warning of a coming apocalypse.
Major shifts in the economy like this are complex, but they will not entail the end of human employment.
Manufacturing Jobs
The first jobs that will be automated are those which are most formulaic.
Advertisements
That is, jobs that entail repeating the same task repeatedly – such as manufacturing jobs that create products on conveyor belts – are going to be automated sooner than other jobs that are less repetitively formulaic.
This shouldn’t be a surprise, as manufacturing jobs have been steadily declining in the United States since 1979. And the Bureau of Labor Statistics predicts that this decline will continue indefinitely.
While one source of this decline in manufacturing jobs and employment has been due to the U.S. outsourcing manufacturing, another reason is automation.
Automation is expected to increase substantially in the manufacturing sector. However, this doesn’t mean automation and manufacturing jobs for humans are necessarily mutually exclusive.
For instance, President Biden’s Made in America executive order intends to invest $300 billion in new technology for manufacturing. However, this technology can create five million manufacturing jobs in the U.S.
Even more, the manufacturing sector is expected to increase global productivity from 0.8 percent to 1.4 percent.
Increases in productivity within manufacturing are likewise predicted to improve wages for workers and reduce prices on products for consumers.
If Biden’s Made in America order goes as planned, it may be the key to ending the current supply chain crisis and its economic consequences. The World Economic Forum projects that by 2022, automation will create 133 million new jobs, even while disrupting millions of other jobs
Advertisements
Manufacturing jobs will be automated, but this will not necessarily entail the end of manufacturing jobs. If anything, it will make the manufacturing industry safer, more productive, and incorporate better-paying jobs.
Medicine: Radiology
While most jobs that will be automated first are those that generally require little experience and entail repetitive tasks, this doesn’t mean more complex jobs won’t soon be automated as well.
Most doctors have a long way to go before they need to worry about their jobs being automated. Radiologists, however, may find themselves working alongside robots sooner rather than later.
Significant progress in automating radiology – the interpretation of body imaging – has already been made.
Advertisements
According to the neuroradiologist Robert Schier, “My guess is that in 10 to 20 years, most imaging studies will be read only by machine.” However, others suggest it could be upwards of 50 years until this happens.
In the short term, AI will serve to assist doctors in the interpretation of imaging. Still, as Schier suggests, AI will ultimately take the wheel of interpretation in the long term.
However, this doesn’t necessarily mean that radiologists will be out of work anytime soon.
Doctors who embrace these new technologies are predicted to survive the swarm of automation in radiology. Of course, doctors aren’t always just doctors; often, they’re also business owners, and those who use AI to benefit their medical practice will continue to flourish in to the modern era of automation.
Advertisements
Occupations that will see the sharpest declines in the next 10 years
Apart from the abovementioned occupations, the following industries/jobs will experience the largest declines in employment over the next ten years:
Cashiers
Service jobs
Secretaries and administrative assistants
Retail supervisors
Tellers
Office clerks
Bookkeepers
Accountants
Auditing Clerks
Retail salespersons
Word processors and typists
In each case, these declines are partly due to automation. However, the reduction of these jobs is not expected to be sharp.
For instance, among those listed, the highest decline is seen amongst word processors and typists, with an expected 36 percent decline in employment.
By contrast, according to the Bureau of Labor Statistics, cashier jobs will only experience a 10 percent decline in employment.
Over the majority of jobs that the Bureau of Labor Statistics predicts will decline in the next ten years will not experience a significant dip. Instead, most of the jobs in these sectors are predicted to be retained.
Of the jobs that will first be automated, therefore, most will be retained –albeit with new cyborg coworkers!
The first jobs to be automated are primarily formulaic and repetitive. For instance, jobs that require little experience or engagement, as well as jobs that are so simple that they become boring very quickly, are the ones that will be automated first.
By contrast, the jobs that will not be automated anytime soon are those that are creative and require emotional labor, such as human services, most of the medical field, education, and hospitality.
The hope is that automation will clear the way for more workers to have the opportunity to work in these fields. Still, the degree to which this hope will materialize is unclear.
| 2022-01-04T00:00:00 |
2022/01/04
|
https://allwork.space/2022/01/here-are-the-top-jobs-that-will-be-automated-first/
|
[
{
"date": "2022/01/04",
"position": 81,
"query": "job automation statistics"
},
{
"date": "2022/01/04",
"position": 82,
"query": "job automation statistics"
},
{
"date": "2022/01/04",
"position": 81,
"query": "job automation statistics"
}
] |
6 Charts That Explain Why the AI Job Market is on Fire
|
6 Charts That Explain Why the Machine Learning & AI Job Market is on Fire
|
https://opendatascience.com
|
[
"Sheamus Mcgovern",
"Odsc Team"
] |
As we enter 2022, the AI job market is on fire. Data science, machine learning, data engineering, and similar AI roles continue to occupy ...
|
As we enter 2022, the AI job market is on fire. Data science, machine learning, data engineering, and similar AI roles...
As we enter 2022, the AI job market is on fire. Data science, machine learning, data engineering, and similar AI roles continue to occupy the top spot by many measures, such as salary, desirability, and job prospects. This is all down to demand lead growth. Not only is the AI job market on fire but we believe it will continue to burn brightly. Here’s a look at 6 charts that explain why the AI job market is doing so well.
Chart #1: $83.7 Billion in Venture Capital Funding
Chart 1 from Pitchbook shows AI & ML investing was at an all-time high in 2021. Globally venture capitalists closed 4,021 AI deals with a record amount of funding of $83.7 billion. For startups, the hard work begins once the funds are wired and they need to put the money to work. AI startups spend much of the first and subsequent round funds on engineering and AI/ML talent. Thus, they need to hire the best in the field at an unprecedented pace to ensure success and to get to their next funding round. Additionally, in 2021 although the number of investments dipped slightly, the amount raised per startup increased significantly. Flush with cash, many AI startups are offering unprecedented amounts to already well-compensated and accomplished AI & ML experts, which is helping raise salaries across the board.
For 2022, and the next 5 years, expect more of the same. Record investment and intense competition for top talent. Author’s note: Interested in AI startups? Check out ODSC East AI Start-up Showcase.
Chart Credit: Pitchbook 2021, Q3, Emerging Tech Report
Chart #2: Everybody’s Doing It
Not to be outdone by startups, corporations are weighing in heavily on AI & ML hiring. After years of handwavy hype around AI and related technologies, companies are finally buckling down and doing the hard work of reshaping their businesses to take advantage of AI. Chart 2 from PWC’s 2021 AI report nicely illustrates that: 58% of companies from their representative survey are fully committed to AI & ML. Fully 93% are committed to the AI path, and only a very small percentage of 7% have no plans to do so. That’s an astonishing uptick for a technology that first came on their radar less than 5 years ago.
This chart also illustrates that there is a lot of AI implementation capacity to fill. Removing the 7% with no interest (thus far) and the 25% fully implementing AI still leaves 68% that need to fully engage AI. Much of this will come from new hiring. Thus we expect significant growth in the AI & ML job market over the next decade.
Chart Credit: PWC 2021 AI Survey Report
Chart #3: AI is Eating Software at a Rate Greater Than Moore’s Law
Thanks to Moore’s Law, which predicted computing power will double every two years, software, as Marc Andreessen famously stated, is eating the world. But that was back in 2011 before he knew there was a bigger fish lurking in the pond. According to this interesting chart from chip designer Synopsys, AI-driven software is doubling every 3 to 4 months. The left side of the chart shows the steady progression of Moore’s law. But it pales in comparison to the right side, which shows exponential growth beginning approximately the same time as the Alexnet competition breakthrough in 2012 up to to AplhaGoZero in 2020 (since surpassed).
It’s evident that AI is the bigger fish in the software industry, so expect every aspect of software, from design, development, and deployment, to be consumed by AI over the next decade and beyond. This will lead not only to the reskilling of software and hardware engineering roles, but also the creation of hybrid roles, new roles (see chart 5), and generally increased demand for AI & ML experts.
Above: Chip design through the ages: Now it’s AI’s turn. Chart Credit: Synopsys AI is Eating Software
Chart #4: A Bigger Slice of the Emerging Tech Sector
AI is not the only new(ish) kid on the block, but pundits like to put it in the emerging tech sector. Emerging tech is set to be the driving force for the global economy over the next decade. It’s a long list that includes everything from clean energy and autonomous machines to crypto and quantum computing. Many of these jobs will incorporate aspects of other technologies including AI. For example, there is significant excitement around quantum machine learning and AI-enabled cybersecurity. Chart 4 shows data from the CompTIA recent jobs market report. The emerging tech sector is huge, but AI & ML is taking a very respectable 12.6% (and growing) slice of that.
The net result of this is increased demand for AI & ML in the jobs market. As emerging tech continues to take a bigger slice of the manufacturing and service economy, it will draw in AI & ML experts from around the world.
Chart Data Source CompTIA Tech Jobs Report, December 2021
Chart # 5: ~54,320 Papers on ML & AI Related Topics in 2021
ODSC wasn’t the first data scientist conference when we started back in 2013 (as the Boston Data Festival). Many excellent academic conferences in AI have been around for decades. Although ODSC’s content is more focused on applied and open source technologies, over the last 8 years we’ve kept a very close eye on new AI developments that make the move from academia to the real world. Much of the advancement in AI & ML emerged from either academia or open-source, oftentimes both. A good proxy of what’s to come in the industry can be gauged from research papers published in the field. Chart 5 illustrates data from arXiv.org for papers published in AI, ML, and related topics (computer vision, etc). Between 2018 and 2021 papers published in this field have more than doubled.
Overall we expect academic/institutional research to continue to accelerate, driving new job growth over the next few decades. Note the new fields of AI emerging from research, such as differential privacy, machine learning safety, meta machine learning, just to name a few. These new techniques will drive the industry to new growth as AI becomes more ubiquitous and powerful.
(authors note: if you’re interested in AI research, check out ODSC East’s focus areas – Research Frontiers, Machine Learning Safety, and Responsible AI)
Chart #6: Less Sexy But More Desirable
We’ve come a long way since 2012 when data science was hailed as the sexiest job of the 21st century. Many new roles (or relabeling of existent roles) have since emerged. Data scientist still holds one of the top spots, but machine learning engineer, data engineers, MLOps engineer, and AI engineer are starting to catch up. Chart 6 from Dice’s 2020 jobs report nicely illustrates these trends.The role of data engineering has been around for quite a while (in other guises), but thanks to the voracious data appetite of AI & ML the role has really taken off. The same can be said for the role of machine learning engineer.
Few expect job roles to be stagnant. New roles are quickly emerging that are just as desirable, such as MLOps Engineer, responsible for deploying and maintaining production models, Machine Learning QA/Test Engineer, responsible for embedded models in autonomous systems, and AI Ethicist, responsible for the ethical dimension of AI and machine learning. AI Technical Writer, AI Project Manager, and AI Data Developer are a few more examples of expanding new job roles that will create new desirable positions and demand in the field.
Chart Credit: Dice 2020 Tech Jobs Report
Attend ODSC East 2022 to prepare for the growing AI job market
ODSC East is the leading applied conference for machine learning and artificial intelligence. Our program is geared towards acquiring hands-on experience and insights into the trends, tools, and topics driving the fields of ML & AI to new highs. Join us in April and learn firsthand what’s coming in 2022 and beyond. Register now for 70% off all ticket types.
| 2022-01-11T00:00:00 |
2022/01/11
|
https://opendatascience.com/6-charts-that-explain-why-the-machine-learning-ai-job-market-is-on-fire/
|
[
{
"date": "2022/01/11",
"position": 70,
"query": "machine learning job market"
},
{
"date": "2022/01/11",
"position": 74,
"query": "machine learning job market"
},
{
"date": "2022/01/11",
"position": 73,
"query": "machine learning job market"
},
{
"date": "2022/01/11",
"position": 69,
"query": "machine learning job market"
},
{
"date": "2022/01/11",
"position": 69,
"query": "machine learning job market"
},
{
"date": "2022/01/11",
"position": 71,
"query": "machine learning job market"
},
{
"date": "2022/01/11",
"position": 67,
"query": "machine learning job market"
},
{
"date": "2022/01/11",
"position": 68,
"query": "machine learning job market"
}
] |
How Machine Learning Will Improve the Workforce - Neuraxio
|
How Machine Learning Will Improve the Workforce
|
https://www.neuraxio.com
|
[
"Olivia Rowe"
] |
As part of the Fourth Industrial Revolution, Artificial Intelligence (AI) and Machine Learning (ML) have become part of our daily lives.
|
How Machine Learning Will Improve the Workforce by Olivia Rowe
As part of the Fourth Industrial Revolution, Artificial Intelligence (AI) and Machine Learning (ML) have become part of our daily lives. Across industries, companies have learned to rely on the convenience and insights that these innovations bring. As of 2020, almost 50% of all companies use AI and machine learning to improve operational quality. Companies that have fully integrated AI-driven tech are also estimated to earn 13% more thanks to improved services.
However, aside from enhancing external operations and increasing consumer satisfaction rates, machine learning can accelerate another critical component of business success: the human workforce.
Machine Learning and the Global Workforce
Despite concerns that AI and machine learning will eventually replace human workers, studies prove how the aforementioned technology can fuel profitable and timely changes. For instance, in Kathleen Walch’s report on global AI dominance, she mentions Japan as a first adopter of machine learning. Though most of the country’s efforts have been focused on robotics, this is seen as an integral solution to alleviate the aging population's workforce shortage. This is important in sectors like the strained healthcare system, wherein healthcare workers are vastly outnumbered by older patients. With the use of machine learning applications, tedious tasks like patient record maintenance can be automated.
Two more countries leading the adoption of machine learning within the workforce are China and the United States. After all, these countries have the largest and most well-backed AI ventures worldwide. In the U.S., there's an emphasis on machine learning complementing human workers. Among the more notable examples of this is highlighted in Ben Eubank’s book on AI within HR. He explains that companies are empowering their internal operations by using smart solutions that simplify processes and clear up production backlogs. This creates a more efficient workplace, that recent surveys show is important for attracting and retaining top talent.
Meanwhile, former Google China president Kai-Fu Lee’s book on today’s AI superpowers, explains that China’s machine learning initiatives are market-driven. This means that rather than being based on abstract ideas, the country’s efforts provide tangible benefits that the growing entrepreneurial market appreciates. For instance, since China is one of the most mobile-driven nations, machine learning providers offer companies a means to streamline their customer transactions. Rather than using an employee’s valuable time fulfilling a customer’s order, for example, their AI-powered systems can fulfill this instead. This is expected to create a faster-moving revenue stream, which in turn, can support the creation of 300 million jobs.
How to Introduce Machine Learning to Your Workforce
Of course, while machine learning will undoubtedly increase profitability and scalability for companies, it may still pose concerns for employees. After all, since the dawn of AI in the 50s, there has been a fear that machines may eventually replace us all. Though understandable, employers can address and assuage these worries to enjoy a seamless and beneficial machine learning integration.
So, before you roll out your machine learning efforts, do offer some classes to familiarize your workforce with AI. In this consultant-led training, your employees will be able to understand the technology as well as the rationale behind why you’re adopting them. Plus, here, they'll also learn about the benefits to them, as noted by Guillaume Chevalier’s article on AutoML. When your employees are aware of the technology you’re about to apply, implementation will be faster and less prone to hiccups.
Moreover, emphasize that your employees aren’t being pushed out. As stated in Kevin Cashman’s review of predictions for future automation, history has proven that technology does breed opportunities for growth and transformation. Should you be expecting consequent changes in your labor demands, explain how this will only change their job description, but not their employment.
Admittedly, there will be changes in the workforce following the mainstream adoption of AI. However, so long as companies aim to use machine learning as a means to enhance rather than replace their current workforce, there will be more long-term wins than losses. For more information on how to integrate machine learning within your workforce, visit Neuraxio.
Article exclusively for neuraxio.com by Olivia Rowe.
CC-BY
| 2022-01-12T00:00:00 |
https://www.neuraxio.com/blogs/news/how-machine-learning-will-improve-the-workforce
|
[
{
"date": "2022/01/12",
"position": 64,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 65,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 63,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 66,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 66,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 66,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 65,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 65,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 65,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 65,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 63,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 67,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 67,
"query": "machine learning workforce"
},
{
"date": "2022/01/12",
"position": 66,
"query": "machine learning workforce"
}
] |
|
Understanding the impact of automation on workers, jobs, and wages
|
Understanding the impact of automation on workers, jobs, and wages
|
https://www.brookings.edu
|
[
"Zia Qureshi",
"Cheonsik Woo",
"Eduardo Levy Yeyati",
"Xiang Hui",
"Oren Reshef",
"Tania Babina",
"Anastassia Fedyk"
] |
Automation often creates as many jobs as it destroys over time. Workers who can work with machines are more productive than those without them.
|
Sections Toggle section navigation Sections Print
Editor's note: This is the second in a series of blogs sharing insights from the new book “Shifting Paradigms: Growth, Finance, Jobs, and Inequality in the Digital Economy.”
Since the dawn of the Industrial Revolution, workers like the Luddites in 19th century Britain have feared that they will be replaced by machines and left permanently jobless. To date, these fears have been mostly wrong—but not entirely. In a chapter in “Shifting Paradigms,” I examine the implications of automation for jobs and wages. Automation, jobs, and wages On one hand, automation often creates as many jobs as it destroys over time. Workers who can work with machines are more productive than those without them; this reduces both the costs and prices of goods and services, and makes consumers feel richer. As a result, consumers spend more, which leads to the creation of new jobs.
On the other hand, there are workers who lose out, particularly those directly displaced by the machines and those who must now compete with them. Indeed, digital automation since the 1980s has added to labor market inequality, as many production and clerical workers saw their jobs disappear or their wages decline. New jobs have been created—including some that pay well for highly educated analytical workers. Others pay much lower wages, such as those in the personal services sector. More broadly, workers who can complement the new automation, and perform tasks beyond the abilities of machines, often enjoy rising compensation. However, workers performing similar tasks, for whom the machines can substitute, are left worse off. In general, automation also shifts compensation from workers to business owners, who enjoy higher profits with less need for labor.
Related Books Shifting Paradigms Shifting Paradigms
Very importantly, workers who can gain more education and training, either on the job or elsewhere, can learn new tasks and become more complementary with machines. For instance, while robots have displaced unskilled workers on assembly lines, they have also created new jobs for machinists, advanced welders, and other technicians who maintain the machines or use them to perform new tasks. In general, workers with at least some postsecondary credentials are often made better off, while those without them often suffer losses. The new automation: Is this time different? The “new automation” of the next few decades—with much more advanced robotics and artificial intelligence (AI)—will widen the range of tasks and jobs that machines can perform, and have the potential to cause much more worker displacement and inequality than older generations of automation. This can potentially affect college graduates and professionals much more than in the past. Indeed, the new automation will eliminate millions of jobs for vehicle drivers and retail workers, as well as those for health care workers, lawyers, accountants, finance specialists, and many other professionals. The new automation will eliminate millions of jobs for vehicle drivers and retail workers, as well as those for health care workers, lawyers, accountants, finance specialists, and many other professionals. So we must ask: Is this time really different? Will the ability of workers to adapt to automation by gaining new education and skills be swamped by the frequency and breadth of tasks that machines with AI will perform? AI will increase the challenges many workers will face from automation, while still contributing to higher standards of living due to higher worker productivity. At the same time, we will need a much more robust set of policy responses to make sure that workers can adapt, so that the benefits of automation are broadly shared. Policy implications New and better policies should be adopted in the following areas: education and training, “good job” creation by employers, and wage supplements for workers. Our most important challenge is to improve the breadth and quality of education and training. To become complementary to AI, more workers will need what researchers call 21st century skills. These include communication, complex analytical skills that often require careful judgements of multiple factors, and creativity. The onus is on K-12 and postsecondary schools to adapt and provide greater emphasis on teaching such skills.
At the same time, displaced workers and those facing lower compensation will need to retrain to perform new tasks in new or changing jobs. More workers will need reskilling or upskilling—whether on the job or in higher education institutions (both public and private). We need to provide high-quality training in high-demand sectors of the economy, such as health care, advanced manufacturing, and retail logistics, that improves the earnings of less-educated or displaced workers. Disadvantaged workers will need more support to complete such education, including occupational guidance and child care. And online learning will have to improve, so it can increase access to skill-building for those who must continue to work full time while training. Indeed, many more employees might need “lifelong learning” accounts to pay for such training. As such, policymakers should consider subsidizing employers who retrain workers while taxing those who permanently lay them off in response to automation. New and better policies should be adopted in the following areas: education and training, “good job” creation by employers, and wage supplements for workers. We must also address two other problems. First, if employers tend to replace many workers and not retrain them, we must make sure that such workers can gain “good jobs” to replace those lost. “Good jobs” should pay well and offer both advancement possibility and some security. Tax and subsidy policies for “good job” creation can encourage employers to improve job quality. Mandates on employers can be effective as well, though such mandates must not be so severe and costly that they speed up employer incentives to automate (like a $15 minimum wage might do in low-wage regions of the U.S.). Second, workers might need an enhanced set of supplements to “make work pay,” such as more generous earned income tax credits, better child care and paid leave, and wage insurance that replaces some part of lost wages for the displaced. These will encourage workers to accept new jobs, though some might pay less than the ones they lose. AI and automation will thus create many new challenges for workers, perhaps greater in scope than those created by past automation. But sensible policies can help workers adapt to these changes and share the benefits of the higher productivity that the new technologies will create.
| 2022-01-19T00:00:00 |
https://www.brookings.edu/articles/understanding-the-impact-of-automation-on-workers-jobs-and-wages/
|
[
{
"date": "2022/01/19",
"position": 20,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 43,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 40,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 72,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 39,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 40,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 69,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 20,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 40,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 40,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 20,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 79,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 42,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 74,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 42,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 41,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 41,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 77,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 43,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 40,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 39,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 37,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 75,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 40,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 19,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 41,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 40,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 40,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 19,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 40,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 19,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 36,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 41,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 40,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 40,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 90,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 83,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 41,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 40,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 82,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 19,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 40,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 43,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 38,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 19,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 44,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 40,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 74,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 40,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 41,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 52,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 19,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 40,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 43,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 20,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 45,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 66,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 20,
"query": "automation job displacement"
},
{
"date": "2022/01/19",
"position": 41,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 48,
"query": "AI wages"
},
{
"date": "2022/01/19",
"position": 37,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 40,
"query": "robotics job displacement"
},
{
"date": "2022/01/19",
"position": 42,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 37,
"query": "job automation statistics"
},
{
"date": "2022/01/19",
"position": 42,
"query": "AI job creation vs elimination"
},
{
"date": "2022/01/19",
"position": 46,
"query": "AI wages"
}
] |
|
NYC Artificial Intelligence Hiring Law - GovDocs
|
NYC Artificial Intelligence Hiring Law
|
https://www.govdocs.com
|
[
"Lindsy Wayt",
"Makala Keefe",
"Dana Holle",
"Jenna Fuhrman",
"Hannah Opheim"
] |
Employers' use of artificial intelligence in hiring practices has been growing for years. These types of tools help HR teams in several ways, ...
|
New York City employers next year will have new requirements if they use artificial intelligence in hiring and promotion processes.
The New York City Council in December 2021 passed the law, which amended the city’s administrative code regarding how employers use “automated employment decision tools.” The measure was a response to how these artificial intelligence tools can create unconscious bias.
The law goes into effect Jan. 1, 2023.
New York City: AI in Hiring
Employers’ use of artificial intelligence in hiring practices has been growing for years. These types of tools help HR teams in several ways, including:
Targeting potential applicants
Expanding the search pool
Screening candidates
However, the fairness of this technology has come under scrutiny. In October 2021, the Equal Employment Opportunity Commission (EEOC) announced it would launch an initiative to ensure that artificial intelligence and similar tools used in hiring and other employment decisions comply with federal civil rights laws.
“Artificial intelligence and algorithmic decision-making tools have great potential to improve our lives, including in the area of employment,” EEOC Chair Charlotte A. Burrows said in a statement. “At the same time, the EEOC is keenly aware that these tools may mask and perpetuate bias or create new discriminatory barriers to jobs. We must work to ensure that these new technologies do not become a high-tech pathway to discrimination.”
Illinois has a law on the books, and California and Washington, D.C., have seen proposals regarding the use of artificial intelligence in employment.
New York City: AI in Hiring
Starting next year, New York City employers that use these artificial intelligence tools have new requirements.
The most pressing of these is that employers must conduct a “bias audit.” Essentially, that means an independent auditor assesses the technology’s impact on an individual’s race, ethnicity, or sex.
According to the NYC artificial intelligence hiring law:
The testing of an automated employment decision tool to assess the tool’s disparate impact on persons of any component 1 category required to be reported by employers pursuant to subsection (c) of section 2000e-8 of title 42 of the United States code as specified in part 1602.7 of title 29 of the code of federal regulations.
Employers must also post the summary of the results of the most recent audit on their website.
Notice Requirement
Meanwhile, the NYC artificial intelligence hiring law also includes a notice requirement for employees or candidates who have applied for a job. The notice must include information that:
An automated employment decision tool will be used in connection with the assessment or evaluation of such employee or candidate that resides in the city. The notice must be made no less than 10 business days before such use and allow a candidate to request an alternative selection process or accommodation
The job qualifications and characteristics that an automated employment decision tool will be used in the assessment of such candidate or employee. Such notice shall be made no less than 10 business days before such use
If not disclosed on the employer or employment agency’s website, information about the type of data collected for the automated employment decision tool, the source of such data and the employer or employment agency’s data retention policy shall be available upon written request by a candidate or employee. Such information shall be provided within 30 days of the written request
However, it would not be disclosed when that would violate local, state, or federal law, or interfere with a law enforcement investigation.
Definition of Auditing Tool
According to the text of the law, an “automated employment decision tool” means:
Any computational process, derived from machine learning, statistical modeling, data analytics, or artificial intelligence, that issues simplified output, including a score, classification, or recommendation, that is used to substantially assist or replace discretionary decision making for making employment decisions that impact natural persons.
That definition does not include tools such as junk email filters, antivirus software, etc.
Lastly, employers found in violation of the NYC artificial intelligence hiring law will be assessed a civil penalty of $500 for the first violation, and between $500 and $1,500 for each subsequent violation.
Conclusion
Most large employers use some form of artificial intelligence in hiring. But with equity, diversity and inclusion becoming more prominent in employment law, employers may want to keep an eye out for laws like New York City’s.
| 2022-01-19T00:00:00 |
2022/01/19
|
https://www.govdocs.com/nyc-artificial-intelligence-hiring-law/
|
[
{
"date": "2022/01/19",
"position": 93,
"query": "artificial intelligence hiring"
}
] |
Why 'the future of AI is the future of work' | MIT Sloan
|
Why ‘the future of AI is the future of work’
|
https://mitsloan.mit.edu
|
[
"David Autor",
"David A. Mindell",
"Elisabeth B. Reynolds"
] |
In a new book about how technology will affect workers, MIT experts explain how artificial intelligence is far from replacing humans — but still ...
|
facebook
X
linkedin
email
print open share links close share links
Amid widespread anxiety about automation and machines displacing workers, the idea that technological advances aren’t necessarily driving us toward a jobless future is good news.
At the same time, “many in our country are failing to thrive in a labor market that generates plenty of jobs but little economic security,” MIT professors David Autor and David Mindell and principal research scientist Elisabeth Reynolds write in their new book “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines . ”
Work smart with our Thinking Forward newsletter Insights from MIT experts, delivered every Tuesday morning. Email: Leave this field blank
The authors lay out findings from their work chairing the MIT Task Force on the Work of the Future, which MIT president L. Rafael Reif commissioned in 2018. The task force was charged with understanding the relationships between emerging technologies and work, helping shape realistic expectations of technology, and exploring strategies for a future of shared prosperity. Autor, Mindell, and Reynolds worked with 20 faculty members and 20 graduate students who contributed research.
Beyond looking at labor markets and job growth and how technologies and innovation affect workers, the task force makes several recommendations for how employers, schools, and the government should think about the way forward. These include investing and innovating in skills and training, improving job quality, including modernizing unemployment insurance and labor laws, and enhancing and shaping innovation by increasing federal research and development spending, rebalancing taxes on capital and labor, and applying corporate income taxes equally.
The first step toward preparing for the future is understanding emerging technologies. In the following excerpt, Autor, an economist, Mindell, a professor of aeronautics, and Reynolds, now the special assistant to the president for manufacturing and economic development, look at artificial intelligence, which is at the heart of both concern and excitement about the future of work. Understanding its capabilities and limitations is essential — especially if, as the authors write, “The future of AI is the future of work.”
+++
To address the time to develop and deploy AI and robotic applications, it is worth considering the nature of technological change over time. When people think of new technologies, they often think of Moore’s Law, the apparently miraculous doubling of power of microprocessors, or phenomena like the astonishing proliferation of smartphones and apps in the past decades, and their profound social implications. It has become common practice among techno-pundits to describe these changes as “accelerating,” though with little agreement on the measures.
But when researchers look at historical patterns, they often find long gestation periods before these apparent accelerations, often three or four decades. Interchangeable parts production enabled the massive gun manufacturing of the Civil War, for example, but it was the culmination of four decades of development and experimentation. After that war, four more decades would pass before those manufacturing techniques matured to enable the innovations of assembly-line production. The Wright Brothers first flew in 1903, but despite the military application of World War I, it was the 1930s before aviation saw the beginnings of profitable commercial transport, and another few decades before aviation matured to the point that ordinary people could fly regularly and safely. Moreover, the expected natural evolution toward supersonic passenger flight hardly materialized, while the technology evolved toward automation, efficiency, and safety at subsonic speeds — dramatic progress, but along other axes than the raw measure of speed.
The ability to adapt to novel situations is still a challenge for AI and robotics and a key reason why companies continue to rely on human workers for a variety of tasks. Autor, Mindell, and Reynolds Authors, "Work of the Future" Share
More recently, the basic technologies of the internet began in the 1960s and 1970s, then exploded into the commercial world in the mid-1990s. Even so, it is only in the past decade that most businesses have truly embraced networked computing as a transformation of their businesses and processes. Task Force member Erik Brynjolfsson calls this phenomenon a “J-curve,” suggesting that the path of technological acceptance is slow and incremental at first, then accelerates to break through into broad acceptance, at least for general-purpose technologies like computing. A timeline of this sort reflects a combination of perfecting and maturing new technologies, the costs of integration and managerial adoption, and then fundamental transformations.
While approximate, four decades is a useful time period to keep in mind as we evaluate the relationship of technological change to the future of work. As the science fiction writer William Gibson famously said, “The future is already here, it’s just not evenly distributed.” Gibson’s idea profoundly links the slow evolution of mass adoption to what we see in the world today. Rather than simply making predictions, with their inevitable bias and poor results, we can look for places in today’s world that are leading technological change and extrapolate to broader adoption. Today’s automated warehouses likely offer a good glimpse of the future, though they will take time for widespread adoption (and likely will not be representative of all warehouses). The same can be said for today’s most automated manufacturing lines, and for the advanced production of high-value parts. Autonomous cars are already 15 years into their development cycle but just beginning to achieve initial deployment. We can look at those initial deployments for clues about their likely adoption at scale. Therefore, rather than do research on the future, the task force took a rigorous, empirical look at technology and work today to make some educated extrapolations.
AI today, and the general intelligence of work
Most of the AI systems deployed today, while novel and impressive, still fall into the category of what task force member, AI pioneer, and director of MIT’s Computer Science and Artificial Intelligence Laboratory Daniela Rus calls “specialized AI.” That is, they are systems that can solve a limited number of specific problems. They look at vast amounts of data, extract patterns, and make predictions to guide future actions. “Narrow AI solutions exist for a wide range of specific problems,” write Rus, MIT Sloan School professor Thomas Malone, and Robert Laubacher of the MIT Center for Collective Intelligence, “and can do a lot to improve efficiency and productivity within the work world.” Such systems include IBM’s Watson system, which beat human players on the American TV game show “Jeopardy!” and its descendants in health care, or Google’s AlphaGo program, which also bests human players in the game of Go. The systems we explore in insurance and health care all belong to this class of narrow AI, though they vary in different classes of machine learning, computer vision, natural language processing, or others. Other systems in use today also include more traditional “classic AI” systems, which represent and reason about the world with formalized logic. AI is no single thing but rather a variety of different AIs, in the plural, each with different characteristics, that do not necessarily replicate human intelligence.
Specialized AI systems, through their reliance on largely human-generated data, excel at producing behaviors that mimic human behavior on well-known tasks. They also incorporate human biases. They still have problems with robustness, the ability to perform consistently under changing circumstances (including intentionally introduced noise in the data), and trust, the human belief that an assigned task will be performed correctly every single time. “Because of their lack of robustness,” write Malone, Rus, and Laubacher, “many deep neural nets work ‘most of the time’ which is not acceptable in critical applications.” The trust problem is exacerbated by the problem of explainability because today’s specialized AI systems are not able to reveal to humans how they reach decisions.
The ability to adapt to entirely novel situations is still an enormous challenge for AI and robotics and a key reason why companies continue to rely on human workers for a variety of tasks. Humans still excel at social interaction, unpredictable physical skills, common sense, and, of course, general intelligence.
From a work perspective, specialized AI systems tend to be task-oriented; that is, they execute limited sets of tasks, more than the full set of activities constituting an occupation. Still, all occupations have some exposure. For example, reading radiographs is a key part of radiologists’ jobs, but just one of the dozens of tasks they perform. AI in this case can allow doctors to spend more time on other tasks, such as conducting physical examinations or developing customized treatment plans. In aviation, humans have long relied on automatic pilots to augment their manual control of the plane; these systems have become so sophisticated at automating major phases of flight, however, that pilots can lose their manual touch for the controls, leading in extreme cases to fatal accidents. AI systems have not yet been certified to fly commercial aircraft.
Artificial general intelligence, the idea of a truly artificial human-like brain, remains a topic of deep research interest but a goal that experts agree is far in the future. A current point of debate around AGI highlights its relevance for work. MIT professor emeritus, robotics pioneer, and Task Force Research Advisory Board member Rodney Brooks argues that the traditional “Turing test” for AI should be updated. The old standard was a computer behind a wall with which a human could hold a textual conversation and find it indistinguishable from another person. This goal was achieved long ago with simple chatbots, which few argue represent AGI.
In a world of robotics, as the digital world increasingly mixes with the physical world, Brooks argues for a new standard for AGI: the ability to do complex work tasks that require other types of interaction with the world. One example might be the work of a home health aide. These tasks include providing physical assistance to a fragile human, observing their behavior, and communicating with family and doctors. Brooks’ idea, whether embodied in this particular job, a warehouse worker’s job, or other kinds of work, captures the sense that today’s intelligence challenges are problems of physical dexterity, social interaction, and judgment as much as they are of symbolic data processing. These dimensions remain out of reach for current AI, which has significant implications for work. Pushing Brooks’ idea further, we might say that the future of AI is the future of work.
Excerpted from The Work of the Future: Building Better Jobs in an Age of Intelligent Machines by David Autor, David A. Mindell and Elisabeth B. Reynolds. Reprinted with permission from the MIT PRESS. Copyright 2022.
Read next: Machine learning, explained
| 2022-01-31T00:00:00 |
2022/01/31
|
https://mitsloan.mit.edu/ideas-made-to-matter/why-future-ai-future-work
|
[
{
"date": "2022/01/31",
"position": 71,
"query": "future of work AI"
},
{
"date": "2022/01/31",
"position": 45,
"query": "future of work AI"
},
{
"date": "2022/01/31",
"position": 43,
"query": "future of work AI"
},
{
"date": "2022/01/31",
"position": 45,
"query": "future of work AI"
}
] |
Explore the present and future of AI in journalism - Eidosmedia.com
|
Explore the present and future of AI in journalism
|
https://www.eidosmedia.com
|
[
"Eidosmedia S.P.A."
] |
We look at how artificial intelligence and machine learning can be used to improve accuracy and efficiency in journalism — and where AI in ...
|
Whether it’s a retailer’s mobile app providing shopping recommendations based on past purchases or a self-driving car, artificial intelligence (AI) has become ubiquitous in modern society. And this newfound reliance on the scalability, accuracy, and personalization AI enables is only projected to grow — TechJury reports the global AI market is expected to reach $60 billion by 2025.
For journalism, a field that relies on the swift dissemination of accurate information, AI and machine learning (ML) present valuable opportunities to make reporting more efficient. In response to this potential, the London School of Economics’ journalism think-tank, Polis, has begun a project called JournalismAI — “a global initiative that aims to inform media organizations about the potential offered by AI-powered technologies.” In its AI Starter Pack, JournalismAI attempts to answer the most frequently asked questions about AI and ML in journalism.
What is artificial intelligence?
JournalismAI defines artificial intelligence as “an umbrella term to refer to the use of algorithms and automation by news organizations, usually to make journalists’ work more efficient or to deliver more relevant content to audiences.” More generally, AI refers to intelligent machines that can, at least theoretically, “think like humans.”
What is machine learning?
JournalismAI classifies machine learning as a subset of AI developed to process data and learn patterns. Powered by this information, ML-trained systems can perform tasks and answer questions without specific instruction from a programmer.
AI in the Newsroom
Initially, the newsroom may be the last place you would expect to see AI or ML. However, it’s already in play at media organizations globally. To better understand how AI is currently being used in news media, one of journalism’s leading nonprofits, The Knight Foundation, recently analyzed 130 AI-powered projects. Here’s what they found:
47% of the surveyed projects used AI to augment reporting capacity. From social media to document dumps, it’s impossible for reporters to keep up with every possible source for their next story. Augmented reporting enlists AI and machine learning to process large volumes of data and identify potential news stories.
From social media to document dumps, it’s impossible for reporters to keep up with every possible source for their next story. Augmented reporting enlists AI and machine learning to process large volumes of data and identify potential news stories. 27% used AI to reduce variable costs in journalism. AI-powered tools that automate tedious tasks like tagging and transcription save valuable time — and money.
AI-powered tools that automate tedious tasks like tagging and transcription save valuable time — and money. 12% used AI to optimize revenue streams. For publications still feeling the loss of print media, the extra revenue AI can generate through “dynamic paywalls, recommendation engines and the digitization of a news organization’s archives” is an significant boon.
The Knight Foundation also looked at the places AI was most commonly implemented in the news pipeline. They found 67% used AI for newsgathering, automatic story generation, and news production, but only 12% leveraged AI for product development, subscriber management, and paywall optimization. This indicates an opportunity for further automation and efficiency in the industry — “when we talk about AI in newsrooms,” says the Foundation, “we seem to lean heavily on the newsgathering part of the process and maybe do not pay as much attention to the product or the business side of the ecosystem.”
AI Use Case: The Financial Times
To better understand the impact of AI, Forbes compiled a list of the most popular applications of AI in journalism in 2020. The Financial Times (FT) was referenced multiple times as an example of successful AI implementation, so let’s take a closer look at the different ways they put this burgeoning technology to use.
Trend spotting
When it comes to cutting through the noise to find engaging, topical stories, AI can’t be beat. FT specifically tailors its algorithms to spot market trends, in turn informing content creation and ensuring the publication keeps its finger on the pulse of the economy.
Reducing bias
For any reputable publication, journalistic bias is at the top of the mind — but it can be a difficult thing to quantify. That’s why FT implemented Janetbot (named after Janet Yellen, former chair of the U.S. Federal Reserve) to monitor the ratio of male to female faces appearing in the publication.
Proofing
There’s no room for inaccuracy in journalism, and AI is a great defense against the dreaded error. FT not only uses AI to “spot and correct errors,” the algorithm also tracks reader engagement and feedback.
Subscriptions
FT began charging for online content back in 2001, but in recent years switched to a subscription model — AdWeek reports digital subscriptions to FT increased 6% year-over-year in 2020. AI not only optimizes the subscription model by delivering a more personalized user experience, it can also help create more of the content subscribers want to see.
The Future of AI
As we’ve learned, the current uses of AI in journalism run the gamut from automating transcriptions to identifying trending topics — but what about the actual writing? It might seem like sacrilege to have a machine write an article, but so-called “robo-journalism” is on the rise, and has its merits.
Take What’s New In Publishing’s example of Swedish news publisher MittMedia. When Head of Content Development Li L’Estrade discovered real-estate articles were performing especially well, she set out to publish more content about properties. But with the sheer volume of houses being bought and sold, it wasn’t sustainable for MittMedia reporters to produce content on every listing. So they developed Homeowners Bot, an AI system capable of analyzing properties and writing short descriptions of them. As a result of implementing Homeowners Bot, MittMedia generates 480 articles on home sales per week and has converted almost 1,000 paying subscribers.
Reuters suspects robo-journalism will continue to gain traction as the technology becomes more fluent. “Every year sees more spectacular progress in the world of Natural Language Processing and Generation. In 2020 OpenAI came up with its GPT-3 model, which learns from existing text and can automatically provide different ways of finishing a sentence (think predictive text but for long-form articles). Now Deep Mind, which is owned by Google, has come up with an even larger and more powerful model and these probabilistic approaches are making an impact in the real world.”
But journalists don’t have to worry about robots taking their jobs anytime soon. The main benefit of robo-journalism is that it takes care of busywork, allowing journalists to focus their time on more thought-provoking pieces. Lisa Gibbs, Director of News Partnerships at The Associated Press sums it up best: “The work of journalism is creative, it’s about curiosity, it’s about storytelling, it’s about digging and holding governments accountable, it’s critical thinking, it’s judgment — and that is where we want our journalists spending their energy.”
Investment research publishing is another area where ‘embedded intelligence’ is playing an increasingly important role. Find out more.
| 2022-02-07T00:00:00 |
https://www.eidosmedia.com/updater/tecnnology/AI-in-Journalism
|
[
{
"date": "2022/02/07",
"position": 86,
"query": "artificial intelligence journalism"
},
{
"date": "2022/02/07",
"position": 86,
"query": "artificial intelligence journalism"
}
] |
|
Antecedents and outcomes of artificial intelligence adoption and ...
|
Antecedents and outcomes of artificial intelligence adoption and application in the workplace: the socio-technical system theory perspective
|
https://www.emerald.com
|
[] |
The use of artificial intelligence (AI) in the workplace is on the rise. To help advance research in this area, the authors synthesise the ...
|
The use of artificial intelligence (AI) in the workplace is on the rise. To help advance research in this area, the authors synthesise the academic research and develop research propositions on the antecedents and consequences of AI adoption and application in the workplace to guide future research. The authors also present AI research in the socio-technical system context to provide a springboard for new research to fill the knowledge gap of the adoption and application of AI in the workplace.
| 2022-02-08T00:00:00 |
https://www.emerald.com/insight/content/doi/10.1108/itp-04-2021-0254/full/html
|
[
{
"date": "2022/02/08",
"position": 53,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 69,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 73,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 54,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 53,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 62,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 62,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 62,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 55,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 55,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 53,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 55,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 55,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 55,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 53,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 46,
"query": "workplace AI adoption"
},
{
"date": "2022/02/08",
"position": 64,
"query": "workplace AI adoption"
}
] |
|
What Every Executive Needs to Know About AI to Build an AI-driven ...
|
What Every Executive Needs to Know About AI to Build an AI-driven Company: Artificial Intelligence for Business Leaders
|
https://nexocode.com
|
[] |
Business leaders need to fully understand and appreciate how AI can help them. A CEO doesn't really need to be an AI expert. Still, a certain ...
|
If you’re an executive, it’s essential to stay ahead of the curve on new technologies that can impact your business. One such technology is artificial intelligence (AI). Many people are getting excited about the rapid development of artificial intelligence and its potential to change how we live, work, and play. But what does every executive need to know about AI to build an AI-driven company? What’s the baseline knowledge of the concepts and challenges of AI for executives? This article focuses on the key concepts and considerations for business leaders considering moving towards AI adoption.
Business leaders need to fully understand and appreciate how AI can help them. A CEO doesn’t really need to be an AI expert. Still, a certain level of knowledge helps to feel confident among developers and understand the key concepts for business decision-making.
Get Your Terms Right (AI vs. ML vs. DL vs. Traditional Software)
To confidently navigate the tech landscape connected with AI and ML, you must understand the key terms.
You need to be able to differentiate between machine learning and artificial intelligence. You may have heard different definitions of these terms depending on whom you asked.
Machine learning is just a subfield of artificial intelligence at a most basic level. In business contexts, the terms are often used interchangeably to refer to machines that learn from data independently. In marketing, the term AI has been so violently abused it’s lost its original meaning – AI is a technology used in vacuum cleaners, TVs, and toothbrushes.
Let’s set the record straight.
Traditional Software
Traditional software is built around deduction. People are responsible for coming up with rules and coding them in the system. Then, these rules are applied to data.
traditional software vs. machine learning technology
Artificial Intelligence
Artificial intelligence is usually used when referring to software that can solve problems by itself. The term is very robust and covers abstract cognitive solutions and various machine learning approaches.
Machine Learning
As the name suggests, machine learning means that machines are used to learn from data and derive insights, conclusions, or actions. ML is a form of data analysis that automates the analytical process, making it possible to learn from data and make predictions on new data. Machine learning involves induction. A machine learning algorithm is fed with examples (hence the term learning) to discover rules automatically. These rules are then applied to new data. For example, you can provide a machine with hundreds of images of cats to help it develop its own rules to identify cats in the future.
In supervised learning, a computer system is given a set of labeled training data, which it uses to learn how to make predictions about new data. Unsupervised learning algorithms are given only data without labels, and their goal is to find patterns and groupings in the data.
Deep Learning
Deep learning is a subset of machine learning. It uses algorithms capable of learning from data without human intervention or assistance and drawing conclusions on their own. These techniques are usually more complex and have had a lot of success in image recognition and natural language processing.
Reinforcement Learning Models
Reinforcement learning is an area of machine learning that deals with how agents can learn to behave in specific ways when interacting with their environment. The aim is for the agent to receive feedback on its actions, which will help it make better decisions in the future.
Convolutional Neural Networks (CNN)
A convolutional neural network (CNN), also known as ConvNet, is a deep learning algorithm used for object detection and identification, facial recognition, and character recognition.
With the basic terms out of the way, let’s discuss what you need to know when adopting AI technology.
Get Your Foundational Technical Knowledge on Artificial Intelligence Technology
Get a Solid Understanding of Common AI Opportunities
To adopt AI technology, first, you need to understand its possibilities to identify opportunities beneficial for your particular business. AI can be applied to various typical problems businesses face. Some common AI applications include:
image recognition and analysis for automated computer vision software,
natural language processing tools that can understand human language to extract data or generate new text-based insights,
voice/sound analysis and generation
fraud detection and anomaly detection to discover events that could be fraudulent or unusual,
recommendation engines for personalization,
predictive analytics for forecasting and building insights,
advanced modeling for massive amounts of parameters.
Data is Paramount
Data is the bread and butter of AI. All business decisions are based on data, so executives need to have at least a basic understanding of how data works. Data can be structured or unstructured. Structured data is when information is organized in a specific way, like in a table or spreadsheet or a series of labeled images in the same format. Unstructured data is data that’s not in a defined structure, like text or voice. In many cases, businesses have more unstructured than structured data.
Harness the full potential of AI for your business Sign up for newsletter
AI technologies take lots of well-structured data to work correctly. AI systems like neural networks make better decisions when vast amounts of historical data are available. The primary benefit of advanced ML and deep learning models is their ability to process and get insights from unstructured data.
Do you have enough data? Where will this data come from? Is the company ready to provide it? Providing the data allows them to produce models that organizations must constantly refine as new data comes into the organization.
Many businesses use infrastructure built by multiple teams and developed over many years. This typically leads to a fragmented information landscape, with data stored in different systems that are not connected. De-siloing is a foundational practice for modern, digital-first organizations. It takes a mixture of leadership and investment in technology to break some organizational and technological barriers.
The role of a future-minded CEO is instrumental in the process of AI adoption – leading from above, enlisting key allies in the organization in a joint effort to unify the organization’s data architecture. To learn more about data strategies for AI-based products, head over to our article on AI Data Needs.
Coding From Scratch Is Not Always the Best Option
Whether deciding on a ready product or building one from scratch through outsourcing or in-house development, the success of any project is contingent on the team’s expertise, which is also true for AI development. Depending on the scale, goals, features, and budgetary constraints of the project, a company must choose the most convenient and efficient model for software development.
Building From Scratch
Coding from scratch takes longer, especially with the emerging crop of developer toolkits that accelerate productivity, carving weeks or even months off development schedules. Building a custom AI solution is often considered the best way to get started with AI and turn it into business value for your business.
Ready-Made Solutions
There are two main ways to approach AI development: through custom AI software development or with ready-to-use AI products. Many ready-to-use AI products can be easily implemented in the organization and solve your problem without unnecessary development costs on your side.
However, for some businesses, it may be better to focus on custom AI solutions while others might find their needs could be met by one of the prebuilt solutions available today.
For a more thorough comparison of ready-made AI solutions and using custom development services, read another post on our blog.
Now, let’s focus on the factors which make your business a likely use case for AI and machine learning technology.
When an ML Solution Will Benefit Your Business
A couple of scenarios make an ML solution more likely to elevate your business, whether it’s ready-made or self-developed.
You Already Have Lots of Well-Structured Data
If you have lots of clean, well-structured data, the odds are high your business will benefit from implementing AI. Some of the most promising use cases for AI tools include natural language processing, predictive analytics, precision medicine, and clinical decision support.
Reliance on data in decision-making processes is a good sign that you would derive value from an ML solution. ML and AI algorithms allow businesses to optimize and unearth new statistical patterns and predictive analytics.
You Have Lots of Data and Apply Rules
Suppose hand-crafted rules or heuristics are already applied on various datasets in your organization. In that case, machine learning will just kick you into the fifth gear, helping you find patterns and complex rules based on existing data.
For example, if you are running an e-commerce store, you can recommend new products to them based on their previous purchases and browsing history. With an ML solution in place, you can improve the existing hard-coded rules and automate the manual labor it requires.
When an ML Solution Won’t Benefit Your Business
Leveraging ML may not always be the best option for your business. It could be a terrible fit in a couple of scenarios.
The Repetitive Tasks You Want to Automate Are Not as Repetitive
Specific tasks only appear “repetitive” instead are anything but. It turns out they can’t be fully defined and automated. This is very typical of sales processes, which involve writing sales emails and handling support cases. This seems like a great use case for an ML solution, but it won’t significantly improve these business processes or completely replace them.
Many human processes rely on empathy, creativity, and improvisation, which can’t be coded as part of the algorithm.
There’s Not Enough Data
Launching an AI-driven app without a solid amount of data to drive it is a common mistake. It’s almost always better to focus on gathering and organizing the data and gaining insights manually before considering machine learning. There is little benefit to implementing ML preemptively, and expect it to benefit the business before gathering data.
Use cases for ML can be found in almost every business, but this is always a balancing act between investment and return. In a small business with little data, AI processes won’t make much difference, and analyzing the data manually might still be a better option.
The Common Problems of AI Development
AI development is associated with potential pitfalls and problems which you should be aware of before moving forward:
You Lack AI Skills Within Your Team
Are you planning to launch an ML solution? Be sure to have the necessary AI expertise in your team. If you decide to go down the path of developing your ML solution, it’s essential to have a team that knows its way around this technology. It requires various skillsets and expertise, from machine learning experts and data scientists to software developers.
If you don’t want to build these skills in-house, make sure to hire experienced partners.
You Require Significant Investment
From an initial project estimate to the final delivery, you will need a good amount of capital from hiring the right people, purchasing software development services, and paying for software licenses. That’s why it might be worth considering collaborating with an experienced company instead (especially if you are not experienced enough in this field yourself). To learn more about artificial intelligence development costs, check out this post.
The cost of future maintenance increases proportionally with its complexity, and the computation needs it will produce.
People Don’t Trust Your Model
It’s the CEO’s role to convince the team and explain to your customers why they should trust your model. Even if it consistently generates accurate predictions, you should expect pushback.
To many people, the workings of ML are nothing short of magic – and that’s normal. It’s a good idea to understand the critical requirements regarding explainability and interpretability to tackle such concerns. It might be a legal requirement in areas such as pharmacology or credit scoring.
The AI Solution is Unmaintainable
It’s important to understand that development is an ongoing, iterative process, meaning your solution will need to be constantly updated and fixed. It’s no different with AI solutions. Your AI model might work today, but it requires proper maintenance to ensure it is reliable tomorrow and adapts to your changing needs.
Having an experienced AI team on board might cost you extra, but it is an excellent way to make sure the model evolves to fit your needs.
A Ready-Made Solution Might Be Just Fine
Custom development makes sense for many scenarios. However, choosing a ready-made, off-the-shelf solution might be a more sensible and efficient strategy for specific use cases. There is an ever-growing number of ready-to-use AI products, and choosing one will shorten the time to market.
For a more in-depth take on the develop vs. buy dilemma, read another article on our blog here.
Your Model Is Biased
No matter the processing power, computers are fallible if the data machine learning model was trained using biased data. Such was the case of the COMPAS system, a piece of software helping determine which prisoners could be released earlier. The system was found to have a significant racial bias – tagging Black defendants as two times more likely to commit recidivism.
For example, you can build an image recognition model using thousands of images of cats. However, the software will still be unable to identify a can when it’s wet – because it wasn’t fed with images of wet cats.
Should You Outsource or Build Your Own ML Team?
You need to make the primary choice between building your own ML team or hiring a consultancy. Creating your own team or a whole artificial intelligence laboratory can take many years, and it’s probably only the right choice for your corporate strategy if you don’t need to see results urgently and if you expect ML to be the key differentiating factor between you and your competitors. Otherwise, a good partner skilled in developing advanced software solutions will also be able to complement your in-house team or take over the development completely and guide you through the process of building the technical environment and setting up a full-fledged AI system within your company.
AI for Executives - Last Words
Tech-minded CEOs tend to force ML where it can’t add value to stay modern and relevant. Others fail to take advantage of it where it would add value. You need to understand why you need an AI solution in the first place. Are you trying to solve a problem, planning to beat a competitor, or only exploring the potential use of the technology in the business? Understanding what’s possible and what’s not possible will allow you to set your expectations about AI right and reduce the pushback from non-believers within your organization.
Even if investing in AI is not on your agenda today, it’s definitely something worth considering. If you’re looking to develop your own AI product or trying to find a provider of AI software development services, our team of experts will discuss your project.
Our AI Design Sprint is a low-investment workshop that aims to kickstart any AI implementation project and give strategic benefits. We apply a set of tools for each step of the design-thinking process to help our clients leverage the power of AI and machine learning algorithms and turn these technologies into a tangible competitive advantage. Get in touch with us to start your AI adoption and digital transformation journey.
| 2022-02-10T00:00:00 |
2022/02/10
|
https://nexocode.com/blog/posts/artificial-intelligence-for-business-leaders-and-executives/
|
[
{
"date": "2022/02/10",
"position": 96,
"query": "artificial intelligence business leaders"
},
{
"date": "2022/02/10",
"position": 91,
"query": "artificial intelligence business leaders"
},
{
"date": "2022/02/10",
"position": 93,
"query": "artificial intelligence business leaders"
},
{
"date": "2022/02/10",
"position": 90,
"query": "artificial intelligence business leaders"
},
{
"date": "2022/02/10",
"position": 95,
"query": "artificial intelligence business leaders"
},
{
"date": "2022/02/10",
"position": 95,
"query": "artificial intelligence business leaders"
},
{
"date": "2022/02/10",
"position": 95,
"query": "artificial intelligence business leaders"
},
{
"date": "2022/02/10",
"position": 95,
"query": "artificial intelligence business leaders"
},
{
"date": "2022/02/10",
"position": 95,
"query": "artificial intelligence business leaders"
},
{
"date": "2022/02/10",
"position": 95,
"query": "artificial intelligence business leaders"
}
] |
The Role of Technological Job Displacement in the Future of Work
|
The Role of Technological Job Displacement in the Future of Work
|
https://blogs.cdc.gov
|
[
"Chia-Chia Chang",
"Mph",
"Mba",
"Sara L. Tamers",
"Phd",
"Naomi Swanson"
] |
Indeed, despite new jobs being created, some estimates suggest that almost half of workers have automation-susceptible jobs (1, 2), and not all ...
|
The future of work holds many possibilities for technological advancements, which may alter the number, quality, and stability of jobs; create new jobs that vary in skill and wage level; and fundamentally change entire industries. Such developments, including digitalization, robotics, artificial intelligence, and advanced computing, have the potential to lead to automation of unsafe tasks or reduction of hazards. While these innovations are often perceived to be favorable and may be linked to economic growth and productivity, they are also tied to unfavorable outcomes, such as technological job displacement—the elimination of jobs when human workers are replaced by technology. Indeed, despite new jobs being created, some estimates suggest that almost half of workers have automation-susceptible jobs (1, 2), and not all displaced workers may find new jobs. As such, it is critical to consider trends in technological job displacement and address resulting impacts on the safety, health, and well-being of workers.
The distribution of technological job displacement is difficult to forecast and varies by job, industry, occupation, and worker demographics. This is because tasks that are repetitive and require less interaction—making them more easily, efficiently, or safely accomplished by computers or algorithms (1)—may affect certain worker groups more than others (i.e., those in high-, middle, versus low-skill jobs). This is referred to as occupational polarization, an outcome and critical concern of technological job displacement. For instance, medium-skill jobs such as those in production, office and administration, and sales, are largely characterized by automation-amenable tasks (3-5). Conversely, occupations that are interactive or non-routine or require higher order thinking are less likely to be completely automated (6). These include both low-skill jobs (e.g., child and elder care, service industry, gardening, cleaning, transportation, and food-focused) (3-5, 7-9), and high-skill jobs (e.g., managers, technicians, accounting, and paralegal work) (3-6, 8, 10). Such trends may disproportionately impact women, younger workers, and immigrants (11, 12); limit career opportunities (13); provide fewer occupational safety and health (OSH) protections; and foster workplaces where workers who fear technological job displacement have less bargaining power, making them less likely to report hazardous working conditions.
Just as it is difficult to forecast the distribution of technological job displacement, the magnitude and pace of technological change leading to displacement is also challenging to predict. While fundamental changes to work that result from new technologies have historically created new jobs and new industries (14-18), these can be slowed by economic, legal, or societal factors, with both positive and negative implications. On one hand, if technological advancements decrease production costs and thus consumer costs, consumer demand for certain products and services may change, leading to increases in labor demand and related economic growth. Workers who gain new skills can become more competitive in the new labor market (18), coupled with benefits experienced due to changes in work arrangements (e.g., online platforms fueling the “gig” economy) and organizational design. On the other hand, productivity-enhancing technologies may result in inequitable benefits to society. The current debates and concerns regarding fully autonomous vehicles being used to replace human drivers (including those who do so for a living) is an example of the challenges that new technologies may face, including public acceptance and understanding, and the development of risk and liability standards (19).
In the center of all this uncertainty, human workers continue to do their jobs, weighing choices which are not new or unique to technological job displacement, but are more at the forefront of public discourse. Studies have found that fears about limited employment opportunities, perceptions of job insecurity, and anxiety about the need to acquire new skills have exacerbated, leading to public health crises such as widespread increases in depression, suicide, and alcohol and drug abuse (including opioid-related deaths) (12, 13, 20-23). At the same time, the accelerated use of new technologies, recent shifts to increased remote work, and alterations in labor markets experienced globally (9, 24) have offered new opportunities while further impacting worker well-being in positive, negative, and still nuanced ways (25).
Would you like to learn more about the OSH implications of technological job displacement? Join us on Thursday, February 24, 3-4pm EST for a webinar: The Role of Technological Job Displacement in the Future of Work, featuring Dr. Naomi Swanson from CDC/NIOSH and Ms. Shannon Meade from the Emma Coalition and the Workplace Policy Institute. Register here to attend this free webinar presented by the NIOSH Future of Work Initiative.
Missed previous webinars in the series? You can watch them here.
To learn more about the NIOSH Future of Work Initiative, please visit its website.
NIOSH has identified technological job displacement objectives for the future. Tell us what technological job displacement needs you have at your workplace in the comment section below.
Chia-Chia Chang, MPH, MBA is a Coordinator in the NIOSH Office Total Worker Health® and the NIOSH Healthy Work Design and Well-Being Cross-Sector Program.
Sara L. Tamers, PhD, MPH is Coordinator of the NIOSH Future of Work Initiative, Coordinator of the Total Worker Health® Program, and Assistant Coordinator of the Healthy Work Design and Well-Being Program.
Naomi Swanson, PhD, is a Senior Science Advisor at NIOSH.
References
Frey CB, Osborne MA. The future of employment: How susceptible are jobs to computerisation? Technological Forecasting Social Change. 2017;114:254-280. McKinsey Global Institute. A Future That Works: Automation, Employment and Productivity: https://www.mckinsey.com/featured-insights/digital-disruption/harnessing-automation-for-a-future-that-works. 2017. Manyika J, Lund S, Chui M, et al. What the future of work will mean for jobs, skills, and wages: Jobs lost, jobs gained. McKinskey Global Institute. 2017. Vermeulen B, Kesselhut J, Pyka A, Saviotti PP. The impact of automation on employment: just the usual structural change? Sustainability. 2018;10:1661. Hirschi A. The fourth industrial revolution: Issues and implications for career research and practice. The Career Development Quarterly. 2018;66:192-204. Arntz M, Greggory T, Zierahn U. The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis. In: Organisation of Economic Cooperation and Development S, Employment and Migration, ed. Organisation of Economic Cooperation and Development, Social, Employment and Migration Working Papers; 2016. World Bank. 2019. World Development Report 2019: The Changing Nature of Work. Washington, DC: World Bank. https://www.worldbank.org/en/publication/wdr2019.
| 2022-02-15T00:00:00 |
2022/02/15
|
https://blogs.cdc.gov/niosh-science-blog/2022/02/15/tjd-fow/
|
[
{
"date": "2022/02/15",
"position": 65,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 85,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 75,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 19,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 65,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 16,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 73,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 73,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 68,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 43,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 65,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 65,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 64,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 17,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 63,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 19,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 19,
"query": "automation job displacement"
},
{
"date": "2022/02/15",
"position": 59,
"query": "robotics job displacement"
},
{
"date": "2022/02/15",
"position": 55,
"query": "robotics job displacement"
}
] |
Robotics and Unemployment: a Double-Edged Sword - Augmentus
|
Robotics and Unemployment: a Double-Edged Sword
|
https://www.augmentus.tech
|
[] |
Robotics causing unemployment has been increasingly addressed as more workers are found to be displaced from their jobs.
|
Robotics has undoubtedly penetrated many industries in recent years. However, online sources provide conflicting information on whether this has caused job creation or destruction. Robotics causing unemployment has been increasingly addressed as more workers are found to be displaced from their jobs. The term for this is officially called ‘technological unemployment’. This begs the question: does this increase in automation only result in negative impacts?
Robotics and Unemployment: a Double-Edged Sword
Displacement Effects of Robotics
The labour market primarily consists of 2 groups of workers, skilled and unskilled or low-skilled. Low-skilled workers carry out repetitive tasks, making them easily replaceable. These workers are often found in sunset industries. On the other hand, skilled workers might perform more abstract tasks – mainly in sunrise industries, that are difficult to automate. A study suggests that emerging technologies will displace 4.3 million US workers by 2027 (Oxford Economics, 2017), most of whom will be from sunset industries. Of course, such statistics spark concern for robotics causing unemployment.
Figure 1: Automated Welding Arm Image by Adobe Stock
An Economic Perspective on Technological Unemployment
The displacement of workers is due to “labour saving”. In a literal sense, labour saving means to save on labour by pumping financial resources into capital, otherwise known as machinery. The decision to use machinery over labour stems from the former yielding greater utility at a lower cost. Workers’ physiological state would deteriorate after several hours of work. On the other hand, robotic arms used would not face this issue and could even execute tasks at a greater speed than human workers. With the benefits of increasing production volume and quality, it is only logical for profit-maximising firms to arrive at the decision to cut costs on labour and reallocate those funds into machinery.
Figure 2: Mr Bucket Losing His Job from Charlie and The Chocolate Factory
Productive Effect of Robotics
With all the negativity surrounding automation, it is easy to neglect the productive effects presented. In spite of the data presented, automation ultimately strives to enhance productivity, not rob workers of their jobs.
Job Enhancement with Robotics
In a previous blog post, it was mentioned that workers can be removed from harsh working environments when robotic arms are used. This might present itself as a stepping stone to how robots are rendering humans obsolete. However, this is not necessarily true. These workers now play a different role in ensuring that the robotic arms function smoothly and perform the tasks as programmed. There is minimal loss of job, but rather a reskilling of the worker to an oversight role. It will take time for workers to familiarise themselves, but it is a small sacrifice in the face of impending unemployment. Hence, the reality is that the job scope of many occupations has simply been altered. Estimates show that 95% of workers under threat are able to find new job opportunities with sufficient reskilling.
Figure 3: Employees as Oversight Role for Robots Image by Shutterstock
Job Creation with Robotics
Utilising robots to carry out repetitive tasks leaves room for humans to pursue more meaningful work. For instance, employees can prioritise tasks of greater concern and increase workplace efficiency. Furthermore, the lack of autonomous thinking ability of a robot allows for humans to take greater control. This enhances the capacity of humans as an autonomous agency. Thus, creating more higher-value jobs with the main role in ideation and decision-making.
Figure 4: Employees in Decision-Making Role by Shutterstock
Conclusion
Robots are Still Robots
A report by the United Nations Department of Economic and Social Affairs (UN DESA) Development Policy and Analysis Division (DPAD) stated that many claims of robots being able to replace up to 80% of jobs are unrealistic and there are several reasons why. Firstly, robots only execute specific tasks. For example, a pick and place robot in a factory or even the surface treatment application example provided earlier. These robots do not have the ability to execute other tasks apart from their programmed functions. Hence, it is unrealistic to replace an entire occupation with robots. Additionally, they do not have the capacity for feeling or intuition, making them substantially interior to humans (Diamond, 2018). Such skills are a large component of 21st-century jobs. Therefore, robots lacking such skills will diminish the scale of unemployment.
Ethical Implications
Secondly, there are ethical implications to robots taking over jobs completely. When a negative outcome arises because of a robot making an important decision, there is no clear answer as to who is responsible. Extensive legal battles, easily avoided by disallowing the use of robots in decision-making jobs, have to be carried out to resolve the situation (United Nations, n.d.). Consequently, in order to prevent this waste of resources, the complete acquisition of control by robots in the workplace is not a feasible option. In addition, there will always be a secondary layer of human judgement when a decision is to be made by robots so that there is a check and balance in place. Thus, eliminating the first layer of robots making a decision will be more efficient.
Final Thoughts on Robotics causing Unemployment
There is still a long way to go before robots completely take over our jobs. Furthermore, the chances of workers being displaced will be significantly reduced with constant upskilling. As long as humans are able to adapt and predict what is needed to stay relevant, robots will act solely as a tool for enhancing workplace efficiency.
Fun fact: At the end of the day, in the movie “Charlie and The Chocolate Factory” Mr Bucket ended up getting a new job in the same company as an automation technician for the robotic arm that once replaced him.
About Augmentus
Augmentus pioneers industry-leading robotic technologies that enable easy and rapid robotic automation, enabling anyone, even those with no robotic experience, to program dynamic industrial robots in minutes. Our proprietary technology incorporates algorithms to enable fully automated robot path generation and an intuitive graphical interface that eliminates the need for coding and CAD files in robot teaching. Companies using Augmentus have experienced up to 70% cost reduction and 17 times faster deployments across a wide variety of applications, such as spraying, palletizing, welding, and inspections. Augmentus ushers in a new era of human-machine interface, democratizing robotic automation.
References
| 2022-02-24T00:00:00 |
2022/02/24
|
https://www.augmentus.tech/blog/robotics-and-unemployment-a-double-edged-sword/
|
[
{
"date": "2022/02/24",
"position": 98,
"query": "robotics job displacement"
}
] |
A Future With AI and ML: The Power of Workforce Education - MeriTalk
|
A Future With AI and ML: The Power of Workforce Education
|
https://www.meritalk.com
|
[
"Keith Nakasone",
"About Keith Nakasone",
"Keith Nakasone Is A Federal Strategist At Vmware."
] |
In today's digital age, it's clear that artificial intelligence (AI) and machine learning (ML) will be a part of all digital transformation ...
|
In today’s digital age, it’s clear that artificial intelligence (AI) and machine learning (ML) will be a part of all digital transformation journeys – including those of government.
In fact, the Federal government was projected to spend $3 billion on AI and ML technologies in 2021 and planned to invest more than $6 billion in AI-related research and development projects, according to a report by Bloomberg Government. We are even seeing the General Services Administration’s AI Center of Excellence and the Pentagon’s Joint AI Center (JAIC) speed the adoption of AI technologies by civilian and defense agencies.
However, a critical part of the implementation strategy for these technologies is frequently overlooked: workforce training and education. With new technology comes the transformation of employee roles, which requires new skills and training.
AI/ML in Government
AI and ML have the power to help enable government agencies to be more effective and efficient.
The technologies can be used to automate processes, such as the management and optimization of cloud usage. The ability to scale capacity and workloads in response to changes in demand is a significant advantage of cloud computing. AI/ML simplifies the adoption of multi-cloud strategies, empowering more than one public or private cloud. A multi-cloud approach gives agencies more flexibility on which cloud services to use and the ability to innovate across clouds.
AI and ML can also be used to enhance the role of workers. IDC researchers predict that 85 percent of enterprises will combine human expertise with AI, ML, natural language processing and pattern recognition to augment foresight, making workers 25 percent more productive and effective by 2026.
In addition, automation technologies and predictive analytics are increasingly viewed as a matter of national security. The Senate’s FY2022 appropriations framework, released in October 2021, includes $500 million for AI programs across all military branches, plus $100 million for the Department of Defense to help recruit, retain and develop talent to advance use of AI.
Communication is Key
When it comes to implementing technologies like AI and ML, workforce training and education is critical. As with any new technologies, people need training to understand the power of AI and ML and rapidly adopt the technology for low level repeatable tasks.
To cut through assuage worker fears, government IT leaders must include people, processes, and technology when considering innovation across the agency. By communicating across all levels, leaders can more effectively help employees understand how their day-to-day tasks will be impacted by new technologies, and while having the workforce focus on more complex tasks.
Before and throughout the rollout of new technologies, IT leaders must ask themselves questions like: “how can we communicate the benefit of this technology” and “how does it improve the employee experience?” This will not only help people understand what these new technologies mean for them and their jobs – it will speed up the adoption of AI and ML in government so that agencies can more quickly deliver their benefits to taxpayers.
It’s Time Federal IT Leaders Pave the Way
Artificial intelligence and machine learning are poised to transform how the government operates and delivers services to citizens. But it won’t happen if IT leaders don’t take steps now to pave the way. The Senate’s recent passage of the AI Training Act, a bipartisan bill aimed to strengthen Federal employees’ AI knowledge and skills, is a significant step forward to help the public sector discern which systems are helpful and understand basic AI/ML functionality.
Now is the time to make sustained investments to create public sector models that promote innovation and support the upskilling and reskilling of workers across levels to understand the technology and use it efficiently. Improving outcomes for future acquisition solutions requires collaboration and communication between the private and public sectors. With the right communication in place, government employees can more quickly adopt AI and ML to enhance their roles and deliver better services to citizens, faster.
| 2022-03-09T00:00:00 |
https://www.meritalk.com/a-future-with-ai-and-ml-the-power-of-workforce-education/
|
[
{
"date": "2022/03/09",
"position": 83,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 81,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 84,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 85,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 85,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 85,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 83,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 82,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 84,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 83,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 84,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 87,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 85,
"query": "machine learning workforce"
},
{
"date": "2022/03/09",
"position": 89,
"query": "machine learning workforce"
}
] |
|
3 Ways AI and Machine Learning Will Enhance HR and the Workforce
|
3 Ways AI and Machine Learning Will Enhance HR and the Workforce
|
https://evolve.asuresoftware.com
|
[] |
AI and machine learning will transform human capital management · Develop new skills · Improve the employee experience · Provide support for ...
|
3 Ways AI and Machine Learning Will Enhance HR and the Workforce
Artificial Intelligence (AI) and machine learning (ML) are hot topics in HR right now. The COVID-19 pandemic has ushered in a new landscape—one that includes a hybrid workforce; virtual recruitment, hiring, and onboarding; an increased focus on diversity, equity, and inclusion; and a never-ending battle to retain skilled talent by keeping them engaged and supporting their wellbeing. Businesses are increasingly leveraging AI and ML technologies to stay ahead and competitive.
If you’d like to speak to an HR representative about how to utilize AI with your business, contact us.
Many recent surveys show that today’s business leaders recognize AI’s ability to improve key HR functions including talent acquisition, training, and development. That’s why many businesses have already adopted AI and many more plan to follow suit in the next few years. AI and ML are enabling workers to focus more time and effort on high value tasks while automating repetitive tasks. According to a recent study, 72% of business leaders said AI can enable workers to focus on meaningful work while 34% said AI can help free up time for more valuable work.
Specifically for human resources functions, AI and ML solutions are strengthening recruiting and hiring processes by enabling them to target and personalize outreach to find best-fit candidates faster. Business leaders are also leveraging deeper insights to promote company values in authentic, engaging ways. According to a recent Gartner study, 17% of businesses use AI-based solutions in HR and another 30% plan to do so in 2022. Let’s take a look at the benefits of AI and machine learning technologies for human resources and the workforce.
What is AI? What is machine learning?
Artificial intelligence (AI) is the larger category of technologies that enable a computer using deep learning or neural networks to become capable of intelligent behavior. Machine learning (ML) is a more specific type of AI that describes a technology that completes human tasks and can sometimes even learn from the results. ML is the type of technology that applies to most human resources use cases such as applicant tracking and training.
However, machine learning needs reliable data—and lots of it—to get the job done. Computers learn from the data and improve through experience; algorithms find patterns and trends in the data to make decisions. According to a recent Forbes contributor, “Applied correctly, meaning with the right human touch from an HR perspective, AI and in particular a subset called machine learning can support human decision making.”
AI and machine learning will transform human capital management
Since HR gathers a lot of data on employees, it makes the function a great place for machine learning to handle repetitive but essential HR tasks. Machine learning analyzes large volumes of employee data to identify important trends and opportunities. According to a report from IBM, HR departments are successfully leveraging AI and machine learning to:
Solve business challenges
Develop new skills
Improve the employee experience
Provide support for decision making
Use HR budgets more efficiently
How AI and machine learning improve recruitment
Finding and hiring the best-fit talent for your organization requires a large investment of time and resources from your HR team. LinkedIn data shows that recruiters can spend up to 23 hours reviewing resumes for every one successful hire. Now, AI-powered solutions can streamline the resume process by automatically screening resumes for specific skills and experiences that match with your job listings.
When used properly, machine learning technologies can save time by using predictive analysis to make the recruitment process more reliable and accurate. It helps speed up the process and eliminate human bias that could interfere with your company’s ability to recruit and hire skilled candidates.
AI and machine learning solutions do more than just help HR save time. To attract the best talent, HR must deliver great employee and candidate experiences. According to a recent article in HR Executive, here’s how AI and machine learning strengthen your process:
Make help available 24/7 . Chatbots simulate person-to-person conversations and are available anytime, anywhere, and on any device. Chatbots provide immediate responses to candidates or employees who have pressing hiring questions or concerns about benefits.
Automate workflow. AI solutions support your workforce by automating transactional or repetitive work. Automated workflow ensures time-consuming tasks like scheduling interviews are completed, follow-up notes are sent, and training certifications are kept up-to-date.
Send personalized communications. People have become accustomed to personalized experiences and fast response times in their daily lives. They expect it from their employers too. That’s why more businesses are using AI to deliver real-time access to HR resources, provide alerts, and deliver personalized training experiences and career path recommendations.
How AI can improve retention and employee engagement
Retaining talent is also an essential function of HR, and it’s important to the health and success of your business. HR can use AI solutions to better predict, understand, and manage attrition rates with valuable insights into the reasons behind staff turnover.
Machine learning is also proving to be valuable to businesses as they train new hires and existing staff. As organizations provide upskilling and advancement opportunities to their workforce, these technologies help guide and assist HR staff to identify individual skills needs as they embolden their workforce to be agile and future ready. Additionally, training can be customized so that employees learn at their own pace, based on individual needs making
the need for traditional one-size-fits-all sessions obsolete.
Future-proof your workforce
A recent HR Executive article concludes, “Successful adoption of AI enables HR teams to spend more time on the ‘human’ part of human resources—listening to employees’ voices and supporting their wellbeing—a winning situation for everyone.” If you’d like to learn how AI solutions can help support your workforce, Asure offers software and HR services, including fully certified HR professionals, to help with need assessments, employee engagement programs, training and development, and more. With a team of HR experts on your side, you’ll have access to the resources and manpower needed to successfully build a personalized employee experience that attracts and retains the talent you need to grow your business.
| 2022-03-24T00:00:00 |
https://evolve.asuresoftware.com/blog/ai-machine-learning-in-hr/
|
[
{
"date": "2022/03/24",
"position": 90,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 91,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 86,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 93,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 93,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 94,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 88,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 87,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 89,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 88,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 86,
"query": "machine learning workforce"
}
] |
|
3 Ways AI and Machine Learning Will Enhance HR and the Workforce
|
3 Ways AI and Machine Learning Will Enhance HR and the Workforce
|
https://www.asuresoftware.com
|
[] |
How AI and machine learning improve recruitment · Make help available 24/7. Chatbots simulate person-to-person conversations and are available ...
|
Artificial Intelligence (AI) and machine learning (ML) are hot topics in HR right now. The COVID-19 pandemic has ushered in a new landscape—one that includes a hybrid workforce; virtual recruitment, hiring, and onboarding; an increased focus on diversity, equity, and inclusion; and a never-ending battle to retain skilled talent by keeping them engaged and supporting their wellbeing. Businesses are increasingly leveraging AI and ML technologies to stay ahead and competitive.
If you’d like to speak to an HR representative about how to utilize AI with your business, contact us.
Many recent surveys show that today’s business leaders recognize AI’s ability to improve key HR functions including talent acquisition, training, and development. That’s why many businesses have already adopted AI and many more plan to follow suit in the next few years. AI and ML are enabling workers to focus more time and effort on high value tasks while automating repetitive tasks. According to a recent study, 72% of business leaders said AI can enable workers to focus on meaningful work while 34% said AI can help free up time for more valuable work.
Specifically for human resources functions, AI and ML solutions are strengthening recruiting and hiring processes by enabling them to target and personalize outreach to find best-fit candidates faster. Business leaders are also leveraging deeper insights to promote company values in authentic, engaging ways. According to a recent Gartner study, 17% of businesses use AI-based solutions in HR and another 30% plan to do so in 2022. Let’s take a look at the benefits of AI and machine learning technologies for human resources and the workforce.
What is AI? What is machine learning?
Artificial intelligence (AI) is the larger category of technologies that enable a computer using deep learning or neural networks to become capable of intelligent behavior. Machine learning (ML) is a more specific type of AI that describes a technology that completes human tasks and can sometimes even learn from the results. ML is the type of technology that applies to most human resources use cases such as applicant tracking and training.
However, machine learning needs reliable data—and lots of it—to get the job done. Computers learn from the data and improve through experience; algorithms find patterns and trends in the data to make decisions. According to a recent Forbes contributor, “Applied correctly, meaning with the right human touch from an HR perspective, AI and in particular a subset called machine learning can support human decision making.”
AI and machine learning will transform human capital management
Since HR gathers a lot of data on employees, it makes the function a great place for machine learning to handle repetitive but essential HR tasks. Machine learning analyzes large volumes of employee data to identify important trends and opportunities. According to a report from IBM, HR departments are successfully leveraging AI and machine learning to:
Solve business challenges
Develop new skills
Improve the employee experience
Provide support for decision making
Use HR budgets more efficiently
How AI and machine learning improve recruitment
Finding and hiring the best-fit talent for your organization requires a large investment of time and resources from your HR team. LinkedIn data shows that recruiters can spend up to 23 hours reviewing resumes for every one successful hire. Now, AI-powered solutions can streamline the resume process by automatically screening resumes for specific skills and experiences that match with your job listings.
When used properly, machine learning technologies can save time by using predictive analysis to make the recruitment process more reliable and accurate. It helps speed up the process and eliminate human bias that could interfere with your company’s ability to recruit and hire skilled candidates.
AI and machine learning solutions do more than just help HR save time. To attract the best talent, HR must deliver great employee and candidate experiences. According to a recent article in HR Executive, here’s how AI and machine learning strengthen your process:
Make help available 24/7 . Chatbots simulate person-to-person conversations and are available anytime, anywhere, and on any device. Chatbots provide immediate responses to candidates or employees who have pressing hiring questions or concerns about benefits.
Automate workflow. AI solutions support your workforce by automating transactional or repetitive work. Automated workflow ensures time-consuming tasks like scheduling interviews are completed, follow-up notes are sent, and training certifications are kept up-to-date.
Send personalized communications. People have become accustomed to personalized experiences and fast response times in their daily lives. They expect it from their employers too. That’s why more businesses are using AI to deliver real-time access to HR resources, provide alerts, and deliver personalized training experiences and career path recommendations.
How AI can improve retention and employee engagement
Retaining talent is also an essential function of HR, and it’s important to the health and success of your business. HR can use AI solutions to better predict, understand, and manage attrition rates with valuable insights into the reasons behind staff turnover.
Machine learning is also proving to be valuable to businesses as they train new hires and existing staff. As organizations provide upskilling and advancement opportunities to their workforce, these technologies help guide and assist HR staff to identify individual skills needs as they embolden their workforce to be agile and future ready. Additionally, training can be customized so that employees learn at their own pace, based on individual needs making
the need for traditional one-size-fits-all sessions obsolete.
Future-proof your workforce
A recent HR Executive article concludes, “Successful adoption of AI enables HR teams to spend more time on the ‘human’ part of human resources—listening to employees’ voices and supporting their wellbeing—a winning situation for everyone.” If you’d like to learn how AI solutions can help support your workforce, Asure offers software and HR services, including fully certified HR professionals, to help with need assessments, employee engagement programs, training and development, and more. With a team of HR experts on your side, you’ll have access to the resources and manpower needed to successfully build a personalized employee experience that attracts and retains the talent you need to grow your business.
| 2022-03-24T00:00:00 |
https://www.asuresoftware.com/blog/ai-machine-learning-in-hr/
|
[
{
"date": "2022/03/24",
"position": 35,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 35,
"query": "machine learning workforce"
},
{
"date": "2022/03/24",
"position": 35,
"query": "machine learning workforce"
}
] |
|
30 Automation Statistics for The New Decade - KommandoTech
|
30 Automation Statistics for The New Decade
|
https://kommandotech.com
|
[] |
The impact of automation on employment · 1. One in four jobs in the United States will face a high risk of automation job displacement by 2030.
|
The impact of automation on employment
This section of our job automation statistics is focused on the direct impact that automation will have on the employment sector now and in the future. While there have definitely been jobs lost to automation since 1980, things aren’t nearly as bad as they seem.
1. One in four jobs in the United States will face a high risk of automation job displacement by 2030.
(Brookings)
2. 70% of routine physical and cognitive tasks are jobs at risk of automation in the United States.
(Brookings)
3. 24% of US jobs requiring a bachelor’s degree, and 55% of those with lower requirements, have some kind of job automation potential.
(Brookings)
4. The youngest workers (aged 16 to 24) face the highest average risk of automation exposure (49%) in the United States.
(Brookings)
5. 24% of jobs done by men and 17% of those done by women are at a high risk of becoming automated.
(Brookings)
6. 1.5 million or 7.4% of jobs in England are currently at risk of becoming obsolete due to automation.
(Office for National Statistics)
7. By 2030, 44% of low education workers will be at risk of technological unemployment.
(PwC)
8. Transportation, storage and manufacturing sectors will face the highest number of jobs lost to automation.
(PwC)
9. The education and social work sectors will have the least amount of jobs eliminated by automation.
(PwC)
Automation concerns
Many people today are talking about how technology is destroying jobs. Automation and job loss are hot topics with people who aren’t tech enthusiasts. Even among those who are, concerns have been voiced about job automation. Our next set of automation statistics is all about these concerns.
10. 37% of people are worried about automation replacing their jobs.
(PwC)
11. More than 70% of people would consider brain and body augmentations if it led to better job prospects in the future.
(PwC)
12. 56% of people believe that governments should do whatever it takes to protect jobs from automation.
(PwC)
Automation in manufacturing
Robot jobs are a thing. Automation statistics from 2018 and beyond show that we are definitely moving towards a world with more automated companies than we could ever conceive just a few decades ago. Here are some manufacturing automation statistics for your reading pleasure.
13. By 2022 42% of total task hours will be completed by machines.
(World Economic Forum)
14. The global sales value of service robots in 2019 was $12.9 billion.
(International Federation of Robotics)
15. Over 2 million new industrial robots will enter service between 2018 and 2021.
(International Federation of Robotics)
16. The number of industrial robots worldwide is growing by around 14% annually.
(International Federation of Robotics)
17. The global medical robot market will grow to $6.5 billion by 2024.
(Market Research Engine)
18. By 2030, 20 million or 8.5% of the global manufacturing workforce will face job loss due to automation.
(Oxford Economics)
Benefits of automation
So, we’ve seen a lot of stats about jobs replaced by automation or those that will be displaced or changed by robots and AI entering the workforce. But what about automation statistics on jobs created by automation? Well, we’ve got some of those, too. Let’s dig in.
19. Nearly 70% of workers believe automation will bring opportunities to qualify for higher skilled work.
(International Federation of Robotics)
20. 57% of employers want to use automation in order to improve human performance and productivity.
(Willis Towers Watson)
21. 24% of employers would employ the automation of jobs in order to reduce operating costs.
(Willis Towers Watson)
22. One third (33%) of new jobs in the United States are created for occupations that didn’t exist 25 years ago.
(McKinsey & Company)
Automation through AI and AI industry statistics
The last section of our automation stats is all about artificial intelligence, adoption, and the impact of AI tech. Is AI taking jobs faster than it’s creating them? Are automation statistics from 2019 going to be relevant in five or ten years? Let’s find out.
23. The global AI market is set to reach $190.6 billion by 2025.
(MarketsandMarkets)
24. AI implementation will boost the global economy by up to $15 trillion by 2030.
(PwC)
25. According to AI statistics, by 2022 the industry will create 133 million new jobs and take over 75 million existing ones.
(World Economic Forum)
26. By 2035, AI has the potential to boost labor productivity by 40%.
(Accenture)
27. The chatbot market size will be over $1.34 billion by 2024.
(Global Market Insights)
28. 84% of company representatives feel that AI can bring competitive advantages in their industry.
(Statista)
29. AI-powered autonomous vehicles could save 300,000 American lives per decade.
(Digital Information World)
30. 73% of workers believe that technology cannot fully replace the human mind.
(PwC)
| 2022-03-28T00:00:00 |
https://kommandotech.com/statistics/automation-statistics/
|
[
{
"date": "2022/03/28",
"position": 47,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 46,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 47,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 47,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 60,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 48,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 60,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 62,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 62,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 46,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 46,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 47,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 48,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 50,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 48,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 54,
"query": "job automation statistics"
},
{
"date": "2022/03/28",
"position": 55,
"query": "job automation statistics"
}
] |
|
On the Impact of Digitalization and Artificial Intelligence ... - Frontiers
|
On the Impact of Digitalization and Artificial Intelligence on Employers' Flexibility Requirements in Occupations—Empirical Evidence for Germany
|
https://www.frontiersin.org
|
[
"Warning",
"Department Forecasts",
"Macroeconomic Analyses",
"Institute For Employment Research",
"Iab",
"Weber",
"Püffel",
"Universität Regensburg"
] |
We are the first to conduct an empirical analysis of employers' increasing flexibility requirements in the course of advancing digitalization.
|
Artificial intelligence (AI) has a high application potential in many areas of the economy, and its use is expected to accelerate strongly in the coming years. This is linked with changes in working conditions that may be substantial and entail serious health risks for employees. With our paper we are the first to conduct an empirical analysis of employers' increasing flexibility requirements in the course of advancing digitalization, based on a representative business survey, the IAB Job Vacancy Survey. We combine establishment-level data from the survey and occupation-specific characteristics from other sources and apply non-linear random effects estimations. According to employers' assessments, office and secretarial occupations are undergoing the largest changes in terms of flexibility requirements, followed by other occupations that are highly relevant in the context of AI: occupations in company organization and strategy, vehicle/aerospace/shipbuilding technicians and occupations in insurance and financial services. The increasing requirements we observe most frequently are those concerning demands on employees' self-organization, although short-term working-time flexibility and workplace flexibility also play an important role. The estimation results show that the occupational characteristics, independently of the individual employer, play a major role for increasing flexibility requirements. For example, occupations with a larger share of routine cognitive activities (which in the literature are usually more closely associated with artificial intelligence than others) reveal a significantly higher probability of increasing flexibility demands, specifically with regard to the employees' self-organization. This supports the argument that AI changes above all work content and work processes. For the average age of the workforce and the unemployment rate in an occupation we find significantly negative effects. At the establishment level the share of female employees plays a significant negative role. Our findings provide clear indications for targeted action in labor market and education policy in order to minimize the risks and to strengthen the chances of an increasing application of AI technologies.
Introduction
Increasing digitalization, including the development and use of artificial intelligence (AI), has substantially changed working conditions in establishments and administrations. This is one of the main results obtained in the empirical analyses conducted by Warning and Weber (2018) and Warning et al. (2020) on the basis of data from a representative German employer survey. The analyses show, among other things, that employers with digitalization activities—including the application of artificial intelligence—specify higher flexibility requirements with respect to place of work, working time, and self-organization for their newly hired employees significantly more frequently compared to employers without digitalization activities.
As far as we know, that study was one of the first to deal with changes in qualitative working conditions in the course of digitalization. To date, most analyses from labor market research focus on the quantitative effects, and the debate surrounding whether digitalization and its components creates or suppresses employment remains in the foreground (DeCanio, 2016; Arntz et al., 2017, 2020; Acemoglu and Restrepo, 2020a).
Yet, serious research from both labor and health economics and sociology point to the possible negative effects of precisely that type of qualitative changes reported by Warning and Weber (2018) and Warning et al. (2020). According to that research, changing requirements of employers with regard to working place, working time and work organization are not regarded as positive by all employees, and digitalization causes a significant proportion of individual psychological stress (Diebig et al., 2020; Hartwig et al., 2020). In Germany almost half of all employees (46%) associate digitalization with an increasing workload, while only 9% experience a reduction of their workload (Institut DGB Index Gute Arbeit, 2016).
Health insurance providers, in turn, report an increase in illnesses related to such increasing workloads, deadlines and time pressures, as well as changing working hours, and warn of the negative health effects of digitalization, see for Germany Marschall et al. (2017). The increase in stress-related illnesses is not only associated with lost hours of work and a strain on health and social security funds, employers must also expect significant reductions in the performance of those who continue to work despite illness (Diebig et al., 2020).
Sociological research intensively discusses the possible effects of increasing flexibility in working-time. It can entail considerable negative aspects for workers if they face the challenge of reconciling changing working times with other areas of their life, which is not always possible without conflict and is not always cost neutral (Allen et al., 2000; Ford et al., 2007; Dettmers et al., 2013; Brough et al., 2020). Of course, other individuals benefit from more time flexibility in their jobs in terms of work-life balance, particularly when increasing flexibility goes hand in hand with a high level of individual freedom, rather than increasing control over what employees do minute by minute.
Potential negative effects have been documented in a large number of studies and are likely to be relevant in most areas of digitalization. Not least due to the challenges in the wake of the COVID-19 pandemic, the dynamics of digitalization processes have accelerated enormously and AI is gaining importance in modern economies (Brynjolfsson et al., 2018; Al Momani et al., 2021; Amankwah-Amoah et al., 2021). As is discussed by Warning and Weber (2018), establishments and administrations first develop their internal and external digitalization technologies and networks, whereas artificial intelligence is integrated at a later date, so far in only a minority of establishments. However, its speed of dissemination is strongly increasing and a broader discussion of the effects on employees—besides the question of whether jobs are being created or destroyed—is needed to counteract at an early stage any negative developments that might burden not only individuals, but also businesses and society. In doing so, we consider it highly important to take account of the specificities of occupations, since, as has already been discussed in the literature, the applications of AI may differ considerably between occupations and fields of activity (see section Available Research on AI and the Labor Market), which in turn may have an impact on the respective working conditions.
With our analyses we make a substantive empirical contribution to the discussion surrounding qualitative changes in working conditions in the course of digitalization and the use of AI, with a special focus on the role of occupation-specific characteristics. On the basis of data from a large, representative German employer survey we shed light on employers' changing flexibility demands regarding their employees' place of work, short-term changes in their working time and requirements regarding their self-organization. As far as we know, there is no other representative study available in this context, based on concrete assessments by a large number of employers in all industries and establishment sizes. Germany is a country with a strong digital development and high investments in the development and application of AI (OECD, 2020). Therefore, the results presented here are also highly relevant for other advanced economies and contribute to discussions at the European level dealing with changing working conditions.
Our article is structured as follows: Section Available Research on AI and the Labor Market provides an overview of the research conducted to date on labor market changes related to artificial intelligence, which so far mainly comprises research on potential quantitative effects. Section Method presents the data that we use for our study, explains the transformation of the data into a panel data set and justifies the selection of a non-linear random effects estimator. This part is followed by a description of some of the digital developments in Germany and of the occupations that are relevant in the context of AI applications in section Some Descriptive Results. Section Estimation Results discusses the results of the random effects estimations and the factors that emerge as relevant for employers' increasing requirements regarding their employees' flexibility in terms of their place of work, their working time and their self-organization. We summarize our results in section Discussion and Outlook and provide an outlook for future empirical research on the qualitative labor market effects of AI.
Available Research On AI and The Labor Market
As is the case for digitalization in general, there is no unique definition of AI that expresses the diversity and breadth of both the technology and its potential applications, although we do not yet know all of the potential AI applications. Therefore, labor market researchers currently address above all the possible labor market effects of AI, while the actual labor market effects remain largely unknown, with little empirical work conducted on the topic so far.
Current research deals partly with conceptual boundaries and the ways that AI can be operationalized for empirical research (Ernst et al., 2019; Acemoglu and Restrepo, 2020b; Tolan et al., 2021). Building on or parallel to this, empirical work has also been conducted on the quantitative effects of AI on employment, wages, hires, and fluctuation (Felten et al., 2019; Webb, 2020; Georgieff and Hyee, 2021; Fossen and Sorgner, 2022). These quantitative studies have to make assumptions about how certain capabilities and tasks are changed by the application of AI technologies, which have to be defined initially, for example on the basis of interviews with experts from the AI field. The aim is to assess how the characteristics of occupations change with regard to the tasks to be performed and the skills required and to estimate the quantitative effects resulting from these changes. Research on changing tasks and the shifting importance of specific task types (types of manual and cognitive tasks) is usually a crucial element of these approaches.
For instance, in German labor market research, occupations are distinguished according to five task types (Spitz-Oener, 2006), see Table 1 for a description and examples. Using this concept Genz et al. (2021) discuss the idea of different stages of digital development that include AI in the youngest stage. They find that establishments that are active in this youngest stage (“4.0 adopters”) have a comparatively larger share of employees performing routine cognitive tasks in their job activities (36%), followed by non-routine analytical tasks and non-routine manual tasks. The degree of complexity involved in the job increases with ongoing digitalization, as does demand for IT staff (AI specialists, IT security consultants, cloud engineers) and staff in business services.
TABLE 1
Table 1. Task types of occupations and examples.
From the available studies, it can be deduced that AI is mainly used in occupational fields involving a high proportion of cognitive and analytical tasks. In these fields, based on a large amount of data, AI can strengthen the basis for decision-making by making it possible to systematically monitor and evaluate processes, thereby supporting people in their decision-making. In some areas AI can also take over the control of processes entirely. On the other hand, AI is used less in areas in which people interact strongly, as not all elements of human behavior can be replaced by technological systems.
The OECD recently published an article reviewing what is known about the labor market effects of AI, showing the potential of AI on the one hand and our very limited knowledge about the real labor market effects on the other hand (Lane and Saint-Martin, 2021). This applies in particular to knowledge about changing working conditions and employers' changing demands regarding flexibility, what might be even more important than in previous stages of digitalization. The authors provide an example of this for the case of AI-supported robots: Such robots might take over activities that are dangerous or physically very strenuous for humans, which has clear positive effects on the tasks to be performed, as they become less dangerous and less strenuous. However, if the humans have to adapt their work intensity and rhythm to the robot in a close human-machine-interaction, the work pressure might simultaneously increase and the freedom of action may decrease, leading to increasing stress and growing dissatisfaction, in turn causing (new) psychological stress for the employee. Another open issue in the context of AI is the availability of big data, which enables employers to closely monitor employees' activities and to steer these activities automatically in the short term. This not only raises questions concerning data protection and personal rights, but in practice pressurizes employees to respond at short notice to adaptations intended by the AI system and to avoid any mistakes and misconduct while carrying out work.
Method
Establishment Data From the IAB Job Vacancy Survey
In the study presented here, we examined the role of occupation- and establishment-specific characteristics for increasing flexibility requirements expressed by employers.
We took up some of the findings obtained by Warning and Weber (2018) on significant changes in working conditions and again use the IAB Job Vacancy Survey (JVS) for our new approach. The JVS is a representative employer survey conducted at regular intervals among employers in Germany. Its overall aim is to determine the current demand for labor and to observe staff-search and hiring processes in detail (Davis et al., 2014; Bossler et al., 2020). Every year some 12,000 establishments and administrations of all sizes and from all sectors of the economy complete the written questionnaire in the fourth quarter of the year. (According to the sampling method, the term “establishment” always refers to establishments and public administrations with at least one employee covered by social security contributions.) The information they report on vacancies, employment, and the development of search and hiring processes are extrapolated to all establishments and all new hires in Germany, thereby providing a unique, representative picture of the current labor market development in Germany (on the extrapolation, see Brenzel et al., 2016). The JVS is quality assured in accordance with the regulations laid down by the European Commission concerning the collection, measurement and calculation of job vacancy and employment data that are gathered in this survey and are officially published by Eurostat in the context of labor demand data for the European countries (Eurostat, n. d).
In 2016 we integrated new detailed questions into a special questionnaire section of the JVS. It focused on changing flexibility requirements in occupations by those employers who expect increasing digitalization in the subsequent 5 years, see Figure 1. In the first question (question 36 in the JVS) the participating establishments, or their managers or personnel managers, are asked whether their particular establishment is expecting an increase in digital development over the following 5 years. As in the previous analysis of Warning and Weber, digital development is defined as internal digital networking, networking with customers/suppliers and the use of learning systems. Learning systems as part of artificial intelligence systems are thus included in our study.
FIGURE 1
Figure 1. IAB Job Vacancy Survey 2016, written questionnaire, p. 5.
All establishments that answer the first question with YES (a total of 4,262 establishments) are then asked to report the occupations for which they expect particularly strong changes in employees' qualitative working conditions as a result of increasing digitalization. The questionnaire gives the possibility to state a maximum of three occupations. The changes in working conditions refer to flexibility in terms of workplace, flexibility regarding working time on short notice and demands regarding employees' self-organization. The wording in the special questionnaire section deliberately refers only to (great and small) increasing or unchanging flexibility requirements, because our research focuses only on increases, not decreases.
Restricting the number of occupations that establishments could mention here to a maximum of three was a compromise: On the one hand, we wanted to investigate positive changes in flexibility requirements by individual occupations. On the other hand, an already extensive written survey like the JSV cannot be extended by too many additional questions, as this may lead to a drop in establishments' willingness to participate, thereby endangering the success of the entire survey. However, the restriction to three occupations proved in retrospect to be very meaningful and does not lead to a distortion of the results: The vast majority of those establishments expecting an increase in digitalization provided detailed information on flexibility requirements for one or two occupations. Only rarely did an establishment report three occupations in the questionnaire. Therefore the answers reflect employers' assessments of the occupations that they consider to be most strongly affected, this has to be taken into account when interpreting the survey results.
For the subsequent estimations we calculated three new binary variables from the JVS data. They are independent of each other and are the dependent variables in our models:
1) increasing requirements regarding flexibility in terms of place of work,
2) increasing requirements regarding short-term flexibility in working time and
3) increasing demands regarding self-organization.
Each binary variable took the value 1 if the establishment reported a small or large increase in the flexibility required in the specific occupation. It took the value 0 if the establishment indicated no change or no relevance of this requirement.
In addition to the data on changing requirements by occupation we utilized standard establishment-specific structural data from the JVS. They describe the establishment's individual employment and labor demand situation that might affect the employer's individual decisions regarding the flexibility required of their employees. Specifically, we used information on region and workforce size, the share of academics, the share of employees with vocational qualifications and the share of women. We included data on the establishment's overall labor demand, such as the expected employment development, the number of new hires, job vacancies as a proportion of employment and the fluctuation in the particular economic sector. We also included data on the existence of a works council and collective agreements, as this might hinder or delay the implementation of new technologies and the associated changes in working conditions (Warning and Weber, 2018). Table 2 provides a descriptive overview of all establishment-specific variables used in our models.
TABLE 2
Table 2. Descriptives of the variables used in the estimation models.
Data on Occupation-Specific Characteristics
In order to be able to depict occupation-specific characteristics in the best possible way, we added various occupation-specific variables that are independent of the individual establishments. First, we used information on the shares of five task types in each occupational group (Spitz-Oener, 2006). Data for the year 2016 come from IAB task research, providing the shares of non-routine analytical, non-routine interactive, routine cognitive, non-routine manual, and routine manual activities in each occupation (Dengler et al., 2014). Table 1 provides a description of these types, as well as examples of occupations that have a relatively large share of the respective task type.
Second, we used structural information from the Federal Employment Agency related to the occupational group: the average age of the workforce, the employment growth rate between 2013 and 2016, the labor turnover rate in 2016 and the unemployment rate in 2016. These data allow us to describe general differences between the occupational groups as precisely as possible, thereby minimizing the risk of omitted variables in our estimation models. Table 2 contains a descriptive overview of the occupation-specific variables.
Creation of a Panel Dataset for Random Effects Estimations
The reported occupations were originally coded according to the German Classification of Occupations 2010 at the 4-digit level (Statistical Offices of the Federation and the Länder, n. d). To ensure that the number of cases per occupational unit was sufficiently high for the analyses, we aggregated the original data at the level of 14 occupational groups and finally obtained a dataset containing information on changing requirements in 14 occupational groups from about 4,200 establishments.
In order to take into account heterogeneity effects and to analyze increasing flexibility requirements in the context of occupations, we transformed this original cross-sectional dataset into a panel data structure. This allows the use of a panel data model, we specifically chose the non-linear random effects model (Cameron and Trivedi, 2010; Wooldridge, 2010). A fixed effects model would not yield estimates for the occupation-specific variables which are the focus of our interest (see next paragraph on these variables). Besides that argument, fixed effects models do not function in the specific case of our data structure. This is characterized by the peculiarity that the three binary dependent variables have a relatively high number of zeros and a relatively low number of ones, meaning that there is relatively little variation in the dependent variables by 14 occupational groups and about 4,200 establishments. As a result, the estimation coefficients (see section Estimation Results) are small, but as is shown with the parameter rho in the estimations in Tables 5–7, a standard pooled estimation would lead to inconsistent parameter estimates and a panel data estimation is the preferred approach here.
Some Descriptive Results
Digital Development in German Establishments
The following results are weighted with the standard weighting factors calculated for the data of the IAB Job Vacancy Survey. The figures in Tables 3, 4 thus represent the total numbers of the respective establishments in the economy.
TABLE 3
Table 3. Sectors of the economy with the respective shares of companies that expect increasing digitalization over the next 5 years.
TABLE 4
Table 4. Number of establishments with positive expectations of increasing flexibility requirements in the respective occupation.
A total of 4,262 establishments in the survey expected increasing digitalization in the following 5 years. Altogether, they represent 700,000 establishments in the German economy, which is equivalent to a share of about 32%. The highest shares by economic sector are found in financial and 256 insurance services, at 63%, followed by liberal professions, scientific and technical services at 257 50%, see Table 3. The sectors with the lowest shares of establishments expecting an increase in digitalization include for instance art, entertainment and recreation, and hospitality.
Establishments with more than 250 employees are more likely to expect increasing digitalization than medium-sized and small ones. On the whole our results are similar to those obtained in other studies on the spread of digitalization in Germany (Reimann et al., 2020).
Occupations and Increasing Flexibility Requirements
Table 4 shows a list of the most frequently mentioned occupations and the number of establishments with positive digitalization expectations and positive expectations regarding increasing flexibility requirements in these occupations. Office and secretarial occupations were mentioned most frequently, by about 58,000 establishments and administrations, followed by three occupations that are highly relevant in the context of artificial intelligence: occupations in company organization and strategy (34,000), vehicle/aerospace/shipbuilding technicians (32,000) and occupations in insurance and financial services (32,000).
The table reveals the high relevance of changes in employees' self-organization during the course of digitalization: In all the occupations listed there, this kind of flexibility requirement was mentioned most often by the employers, followed by increasing temporal flexibility and increasing workplace flexibility. As we know, digitalization and in specific the introduction of artificial intelligence systems are closely linked to changes in working structures (Quelle). Our results on the special relevance of increasing demands regarding self-organization underline this statement.
Estimation Results
Occupational Characteristics
Tables 5–10 show the coefficients and marginal effects calculated from our three random effects estimations. In the following we use the marginal effects as the basis for the discussion of our findings, see Table 11 for a comparison between the models. The effects are small in quantitative terms, which is due to the characteristics of the data structure (see section Method). Nevertheless, the effects are highly meaningful, as is confirmed by both the error probabilities and the quality criteria of our estimations.
TABLE 5
Table 5. Estimation results: increasing requirements regarding workplace flexibility.
TABLE 6
Table 6. Estimation results: increasing requirements regarding short-term flexibility in working time.
TABLE 7
Table 7. Estimation results: increasing requirements regarding self-organization.
TABLE 8
Table 8. Marginal effects: increasing requirements regarding workplace flexibility.
TABLE 9
Table 9. Marginal effects: increasing requirements regarding short term flexibility in working time.
TABLE 10
Table 10. Marginal effects: increasing requirements regarding self-organization.
TABLE 11
Table 11. Comparison of the marginal effects of the three estimations.
For all three kinds of flexibility requirements the share of routine cognitive activities is highly significant, with the highest value for increasing demands regarding self-organization. A one-percent increase in the share of routine cognitive activities raises the probability of increasing demands on self-organization by 0.16% points, the probability of increasing short-term working-time flexibility by 0.14% points and of increasing workplace flexibility by about 0.09% points. According to the literature occupations affected strongly by AI applications are often defined by relatively high shares of routine cognitive tasks or non-routine analytical tasks (Genz et al., 2021; Lane and Saint-Martin, 2021). Looking at the shares of routine cognitive activities in the occupational groups in Table 12, our estimates suggest this discussion with regard to occupations with a high share of routine cognitive activities: For instance, in business services and in business management and organization more than half of all tasks are routine cognitive tasks (59 and 56%, respectively). Here increasing digitalization, including the increasing use of AI, is more likely to be associated with employers demanding more flexibility, in particular with regard to self-organization and short-term flexibility in working time.
TABLE 12
Table 12. Shares of task by occupational group 2016, as percentages.
As the marginal effects show, the share of non-routine analytical tasks is negatively significant regarding increasing short-term flexibility in working time, it is not relevant regarding the other two types of flexibility. Looking at the examples of occupations with large shares of such non-routine analytical tasks in Table 12, this result is not surprising in the AI context. If AI is usable at all, it is used more as a supplementary technology. Human beings still have to make decisions and need to understand the AI technology and its applications. Specifically, the work involved in developing and implementing new AI technologies in the establishments may initially be very time-consuming and require a lot of attention from the people involved. It is necessary to understand in detail the interplay between technologies and humans, for which increasing requirements on short-term flexibility in working time, which workers often associate with increasing time pressure, is not a good basis.
Non-routine manual activities show no significant effects on the probability of increasing flexibility requirements. In the context of AI, as a special form of digital development, this result substantiates the discussions about the potential relevance of AI for certain occupations, but not for others.
In all three models, the average age of the employees in the occupational group is negatively and highly significantly related to increasing requirements, with the highest value regarding the demands for self-organization. This result is expectable and reflects the relatively high level of regulation of the German labor market, which protects older employees in many ways. The question also arises of whether older employees who are unwilling or unable to adapt to their employers' changing flexibility requirements are more likely to take up occupations with a lower (or slower) level of digital development or whether they are more frequently forced by their employers to change to other occupational fields or even to change the employer.
The occupation-related employment growth rate between 2013 and 2016, the period directly before the field period of the survey, shows a negative and highly significant value in all three models. An increase in the employment growth rate by 1% reduces the probability of increasing demands on self-organization by 0.7% points. Negative effects are also estimated for the unemployment rate. The fields of the labor market in which digital developments are particularly dynamic and where working conditions may change as a result are more likely to be those in which employers complain of worker and skills shortages. The unemployment rate is correspondingly low and workers' demands for a good work-life balance are likely to be correspondingly high. This is likely to limit employers' possibilities to further increase their flexibility requirements and may even force them to reduce their demands.
The fluctuation rate, i.e., the dynamics of entry and exit from employment in the respective occupational group, exhibits a significant positive effect in all models. High fluctuation means that a relatively large proportion of new employees are recruited relative to the existing workforce. Whereas in the case of the existing workforce, employers are dependent on employees' willingness to change and are not always able to implement changes with the scope and speed desired, in the case of new hires the employers can formulate the precise requirements and conditions that they consider to be in line with the new challenges and opportunities of digitalization. Effects on working conditions and flexibility requirements will therefore be more visible in the more dynamic occupational fields.
Establishment Characteristics
In contrast to the occupational effects, the characteristics of the individual establishments play a minor role in explaining increasing flexibility requirements. The size of an establishment and the region in which it is located are not explanatory. Those operating in an industry with a high labor-turnover rate, and thus having to recruit and train new staff more often, are more likely to define increasing demands on employees' self-organization. This is not the case for the other two types of flexibility.
Positive employment expectations increase the probability of increasing demands for short-term flexible working hours. This is not true of the number of new hires in the previous year or of current vacancies as a proportion of the total workforce, (It should be taken into account that all the establishments in our estimates assume increasing digitalization over the next 5 years, see section Establishment Data From the IAB Job Vacancy Survey of this article).
The skill structure in the establishment shows no significance, except for the proportion of academics in model 1. Differences in skill levels are at least partly captured by the differences in the occupations. In our analyses differences at the occupational level are more relevant than differences at the establishment level.
The proportion of women in the workforce exhibits a significant negative marginal effect in all models. For instance, a one-percent increase in the share of female employees reduces the probability of increasing demands on short term flexibility in working time by 0.012% points. The possibilities for negotiation with female employees regarding increased workplace and short-term working-time flexibility are likely to be fewer than is the case with male employees, at least as far as employees with children or other caring responsibilities are concerned. In many families it is still the mothers who perform the majority of the care work and who have to reconcile this with their employment in terms of space and time. This means that they are tied to existing and stable agreements with their employers to a greater extent, which tends to oppose greater flexibility. The existence of a works council or collective agreements shows no effects in the three estimations.
Discussion and Outlook
Our analyses contribute to the largely unexplored area of research on the qualitative effects of digitalization and the use of AI on working conditions, especially with regard to the demand for increasing flexibility in work assignments. We pay particular attention to the role played by differences between occupations, because, as is discussed in the literature, AI is affecting different occupational fields in different ways. To our knowledge, our study is the first one to present estimation results based on data from a large representative employer survey.
First of all, our study confirms some findings from previous literature on digitalization and AI: occupations for which employers expect the most substantial changes in working conditions as a result of digitalization include office and secretarial occupations as well as occupations in business organization. Occupations in vehicle, aerospace, space, and shipping technology and occupations in tax consulting are also frequently mentioned by the employers in the survey. According to the descriptive results, increasing requirements regarding workplace flexibility play a less significant role than short-term working-time flexibility and specifically the demands on employees' self-organization. These findings indirectly support the discussion surrounding the potential labor market effects of AI, according to which AI primarily changes work content and work processes, which is directly related to aspects of employees' self-organization. According to our results, the flexibility requirements are changing especially in those occupational fields that are undergoing particular strong changes in the context of AI, as discussed for instance by Lane and Saint-Martin (2021).
Using random effects estimations and including numerous establishment- and occupation-specific control variables, we show that it is above all the occupational and less the establishment-specific characteristics that determine the probability of employers demanding increasing flexibility. Increasing demands in terms of flexibility are particularly prevalent in occupational groups that involve a large proportion of routine cognitive activities. These are the fields that are likely to change more strongly with increasing use of AI.
The largest effect of the share of routine cognitive activities in quantitative terms is measured for the probability of increasing demands for employees' self-organization, again supporting arguments, that AI mainly changes work content and work processes. This is particularly important for public employment services: people seeking jobs in occupations with a large proportion of routine cognitive activities can be supported in a targeted manner with regard to their individual abilities and opportunities for a more flexible work engagement than they might be familiar with from previous jobs. This may concern skills in self-organization at work or advice about the advantages and disadvantages of more flexible working time. In fact, policy can focus on very specific areas of the labor market, because possible risks do not affect all occupational fields in which AI is used or might be relevant in future. In our estimations the proportion of manual tasks does not show any significant effect on the flexibility requirements. And occupations involving a large amount of interaction between employees are also less at risk of negative effects. Here, AI is likely to be used somewhat less, since interactions between people are more difficult to replace by machines.
Besides labor market policy also education policy plays a crucial role for the question of whether AI mainly has a negative impact on working conditions or not. Decisive possibilities for policy action are, for instance, the strategic development of the education and vocational training systems and the provision of a child care infrastructure that supports the reconciliation of a more flexible working and private life. For female employees in particular, the increasing use of AI and the associated demand for greater working-time flexibility is likely to be a major challenge and might even become an employment risk if adequate and flexible childcare facilities are not available.
Apart from the share of women, the establishment-specific characteristics play a subordinate role compared to the occupational characteristics. Employers see the challenge of compensating for additional individual burdens on employees in order to maintain the employees' productivity and job satisfaction, especially if the employers are to be increasingly threatened by labor shortages.
Future empirical research on the qualitative labor market effects of digitalization and AI should deal in depth with the role of certain occupations, which requires a larger number of cases in survey-based studies. How does AI change productivity on the one hand and individual stress on the other hand for different employee groups (female/male, young/old, employees with families/without families, etc.) in different occupational fields? Here the gender-related effects should be paid special attention in order to be able to counteract possible replacement effects at an early stage. What options exist for employers to compensate their employees for additional burdens, for example attractive holiday arrangements, further training opportunities, setting up long-term working time accounts with attractive conditions for the employee, through to financial compensation for increasing flexibility in work assignments? What are sustainable good and healthy working conditions that keep the workforce productive and satisfied in times of accelerating digitalization? The employer's perspective is important here for negotiating joint solutions, which makes a combination of both employer surveys and employee surveys highly attractive in this research field. Finally, international comparative analyses could take into account the specifics of different national labor market policies in the context of ongoing digitalization, which in general has been further accelerated by the current COVID-19 pandemic.
Data Availability Statement
Publicly available datasets were analyzed in this study. This data can be found here: The Research Data Centre (FDZ) of the Federal Employment Agency at the Institute for Employment Research. https://fdz.iab.de/en.aspx.
Author Contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Acknowledgments
We thank all participants of the ILO Workshop: Artificial Intelligence and the Future of Work: Humans in Control on October 25/26, 2021 for an inspiring discussion and very helpful comments. We also thank two referees for their questions and constructive tips.
References
Acemoglu, D., and Restrepo, P. (2020a). Robots and jobs: evidence from US labor markets. J. Politic. Econ. 128, 2188–2244. doi: 10.1086/705716 CrossRef Full Text | Google Scholar
Acemoglu, D., and Restrepo, P. (2020b). The wrong kind of AI? artificial intelligence and the future of labour demand. Cambridge J. Reg. Econ. Soc. 13, 25–35. doi: 10.1093/cjres/rsz022 CrossRef Full Text | Google Scholar
Al Momani, K., Nour, A. N., Jamaludin, N., and Zanani Wan Abdullah, W. Z. W. (2021). Fourth Industrial Revolution, Artificial Intelligence, Intellectual Capital, and COVID-19 Pandemic in Applications of Artificial Intelligence in Business, Education and Healthcare. Studies in Computational Intelligence, eds. A. Hamdan, A.E. Hassanien, R. Khamis, B. Alareeni, A. Razzaque and B. Awwad, (Cham: Springer). Google Scholar
Arntz, M., Gregory, T., and Zierahn, U. (2020). Digitization and the Future of Work: Macroeconomic Consequences in Handbook of Labor, Human Resources and Population Economics, ed. K. Zimmermann, (Cham: Springer). Google Scholar
Bossler, M., Gürtzgen, N., Kubis, A., Küfner, B., and Lochner, B. (2020). The IAB job vacancy survey: design and research potential. J. Labour Mark. Res. 54, 13. doi: 10.1186/s12651-020-00278-6 CrossRef Full Text | Google Scholar
Brenzel, H., Czepek, J., Kiesl, H., Kriechel, B., Kubis, A., and Moczall, A. (2016). Revision of the IAB Job Vacancy Survey. Backgrounds, methods and results. IAB-Forschungsbericht. 04/2016. Available online at: https://doku.iab.de/forschungsbericht/2016/fb0416_en.pdf (accessed January 27, 2021). Google Scholar
Brough, P., Timms, C., Chan, X. W., Hawkes, A., and Rasmussen, L. (2020). Work–Life Balance: Definitions, Causes, and Consequences. in Handbook of Socioeconomic Determinants of Occupational Health. Handbook Series in Occupational Health Sciences., ed. T. Theorell, (Cham: Springer), p. 1–15. Google Scholar
Brynjolfsson, E., Mitchell, T., and Rock, D. (2018). What can machines learn and what does it mean for occupations and the economy? AEA Papers Proceed. 108, 43–47. doi: 10.1257/pandp.20181019 CrossRef Full Text | Google Scholar
Cameron, A. C., and Trivedi, P. K. (2010). Microeconometrics Using Stata. College Station: Stata Press, p. 706. Google Scholar
Davis, S., Röttger, C., Warning, A., and Weber, E. (2014). Job Recruitment and Vacancy Durations in Germany. University of Regensburg Working Papers in Business, Economics and Management Information Systems. Available online at: https://epub.uni-regensburg.de/29914/. (accessed January 27, 2021). Google Scholar
Dengler, K., Matthes, B., and Paulus, W. (2014). Occupational Tasks in the German Labour Market—An alternative measurement on the basis of an expert database. FDZ-Methodenreport. Available online at: https://doku.iab.de/fdz/reporte/2014/MR_12-14_EN.pdf (accessed January 27, 2021). Google Scholar
Dettmers, J., Kaiser, S., and Fietze, S. (2013). Theory and practice of flexible work: organizational and individual perspectives. introduction to the special issue. Manage. Revue. 24, 155–161. https://www.jstor.org/stable/23610676 doi: 10.5771/0935-9915-2013-3-155 CrossRef Full Text | Google Scholar
Diebig, M., Müller, A., and Angerer, P. (2020). Impact of the Digitization in the Industry Sector on Work, Employment, and Health in Handbook of Socioeconomic Determinants of Occupational Health. Handbook Series in Occupational Health Sciences, ed. T. Theorell, (Cham: Springer), p. 1–15. Google Scholar
Ernst, E., Merola, R., and Samaan, D. (2019). Economics of artificial intelligence: implications for the future of work. IZA J. Labor Policy. 9, 1. doi: 10.2478/izajolp-2019-0004 CrossRef Full Text | Google Scholar
Eurostat (n. d.). Job Vacancies. Available online at: https://ec.europa.eu/eurostat/web/labour-market/job-vacancies (accessed April 20 2022). Google Scholar
Felten, E., Raj, M., and Seamans, R. (2019). The occupational impact of artificial intelligence on labor: the role of complementary skills and technologies”. NYU Stern School Bus. 19, 605. doi: 10.2139/ssrn.3368605 CrossRef Full Text | Google Scholar
Fossen, F. M., and Sorgner, A. (2022). New digital technologies and heterogeneous wage and employment dynamics in the United States: evidence from individual-level data. Technol. Forecast. Soc. Change. 22, 175. doi: 10.1016/j.techfore.2021.121381 CrossRef Full Text | Google Scholar
Genz, S., Gregory, T., Janser, M., Lehmer, F., and Matthes, B. (2021). How Do Workers Adjust When Firms Adopt New Technologies? ZEW—Centre Euro. Econ. Res. 21, 21–073. doi: 10.2139/ssrn.3949800 CrossRef Full Text | Google Scholar
Georgieff, A., and Hyee, R. (2021). Artificial intelligence and employment: New cross-country evidence. OECD Social, Employment and Migration Working Papers. 265. Paris: OECD Publishing. Google Scholar
Hartwig, M., Wirth, M., and Bonin, D. (2020). Insights about mental health aspects at intralogistics workplaces—a field study. Int. J. Industr. Ergon. 2, 76. doi: 10.1016/j.ergon.2020.102944 CrossRef Full Text | Google Scholar
Institut DGB Index Gute Arbeit (2016). Arbeitshetze und Arbeitsintensivierung bei digitaler Arbeit. So beurteilen die Beschäftigten ihre Arbeitsbedingungen, Ergebnisse einer Sonderauswertung der Repräsentativumfrage DGB-Index Gute Arbeit (Work rush and work intensification in digital work. How the employees evaluate their working conditions. Results of a special evaluation of the representative survey DGB Index Good Work). Available online at: https://index-gute-arbeit.dgb.de/veroeffentlichungen/sonderauswertungen/++co++70aa62ec-2b31-11e7-83c1-525400e5a74a (accessed January 27, 2021). Google Scholar
Lane, M., and Saint-Martin, A. (2021). The impact of Artificial Intelligence on the labour market: What do we know so far? OECD Social, Employment and Migration Working Papers. 256. Paris: OECD Publishing. Google Scholar
Marschall, J., Hildebrandt, S., Sydow, H., and Nolting, H.-D. (2017). Gesundheitsreport 2017 (Health report 2017). Beiträge zur Gesundheitsökonomie und Versorgungsforschung.16. Available online at: https://www.dak.de/dak/download/gesundheitsreport-2017-2108948.pdf (accessed January 27, 2021). Google Scholar
OECD (2020). OECD Digital Economy Outlook 2020. Paris: OECD Publishing. Google Scholar
Reimann, M., Abendroth, A.-K., and Diewald, M. (2020). How Digitalized is Work in Large German Workplaces, and How is Digitalized Work Perceived by Workers? A New Employer-Employee Survey Instrument. IAB-Forschungsbericht. Available online at: https://doku.iab.de/forschungsbericht/2020/fb0820.pdf (accessed January 27, 2021). Google Scholar
Spitz-Oener, A. (2006). Technical change, job tasks, and rising educational demands: looking outside the wage structure. J. Labor Econ. 24, 235–270. doi: 10.1086/499972 CrossRef Full Text | Google Scholar
Statistical Offices of the Federation the Länder (n. d.). Klassifikationsserver. Available online at: https://www.klassifikationsserver.de/ (accessed April 20 2022). Google Scholar
Tolan, S., Pesole, A., Martínez-Plumed, F., Fernández-Macías, E., Hernández-Orallo, J., and Gómez, E. (2021). Measuring the occupational impact of ai: tasks, cognitive abilities and ai benchmarks. J. Artific. Intell. Res. 71, 191–236. doi: 10.1613/jair.1.12647 CrossRef Full Text | Google Scholar
Warning, A., Sellhorn, T., and Kummer, J.-P. (2020). Digitalisierung und Beschäftigung: Empirische Befunde für die Rechts- und Steuerberatung sowie Wirtschaftsprüfung (Digitalization and employment: Empirical findings for legal, tax consulting, and audit firms). Betriebswirtschaftliche Forschung und Praxis. 72, 391–412. doi: 10.1007/s41471-020-00086-1 CrossRef Full Text | Google Scholar
Warning, A., and Weber, E. (2018). Digitalisation, hiring and personnel policy: evidence from a representative business survey. IAB-Discussion Paper. Available online at: https://doku.iab.de/discussionpapers/2018/dp1018.pdf (accessed January 19, 2022). Google Scholar
Webb, M. (2020). The Impact of Artificial Intelligence on the Labor Market. SSRN Electron. J. 20, 2150. doi: 10.2139/ssrn.3482150 CrossRef Full Text | Google Scholar
| 2022-05-03T00:00:00 |
2022/05/03
|
https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.868789/full
|
[
{
"date": "2022/05/02",
"position": 72,
"query": "artificial intelligence employers"
},
{
"date": "2022/05/02",
"position": 88,
"query": "artificial intelligence employers"
},
{
"date": "2022/05/02",
"position": 87,
"query": "artificial intelligence employers"
},
{
"date": "2022/05/02",
"position": 64,
"query": "artificial intelligence employers"
},
{
"date": "2022/05/02",
"position": 60,
"query": "artificial intelligence employers"
},
{
"date": "2022/05/02",
"position": 64,
"query": "artificial intelligence employers"
},
{
"date": "2022/05/02",
"position": 71,
"query": "artificial intelligence employers"
},
{
"date": "2022/05/02",
"position": 72,
"query": "artificial intelligence employers"
},
{
"date": "2022/05/02",
"position": 66,
"query": "artificial intelligence employers"
},
{
"date": "2022/05/02",
"position": 69,
"query": "artificial intelligence employers"
}
] |
Algorithms, Artificial Intelligence, and Disability Discrimination in Hiring
|
Algorithms, Artificial Intelligence, and Disability Discrimination in Hiring
|
https://www.ada.gov
|
[] |
This guidance explains how algorithms and artificial intelligence can lead to disability discrimination in hiring.
|
May 12, 2022 This guidance explains how algorithms and artificial intelligence can lead to disability discrimination in hiring. The Department of Justice enforces disability discrimination laws with respect to state and local government employers. The Equal Employment Opportunity Commission (EEOC) enforces disability discrimination laws with respect to employers in the private sector and the federal government. The obligation to avoid disability discrimination in employment applies to both public and private employers.
Guidance & Resources Read this to get specific guidance about this topic. For a beginner-level introduction to a topic, view Topics
For information about the legal requirements, visit Law, Regulations & Standards
How employers use algorithms and artificial intelligence
Employers, including state and local government employers, increasingly use hiring technologies to help them select new employees.
For example, employers might use technology:
to show job advertisements to targeted groups;
to decide if an applicant meets job qualifications;
to hold online video interviews of applicants;
to use computer-based tests to measure an applicant’s skills or abilities; and
to score applicants’ resumes.
Many hiring technologies are software programs that use algorithms or artificial intelligence. An algorithm is a set of steps for a computer to accomplish a task—for example, searching for certain words in a group of resumes. Artificial intelligence generally means that a computer is completing a task that is usually done by a person—for example, recognizing facial expressions during a video interview.
While these technologies may be useful tools for some employers, they may also result in unlawful discrimination against certain groups of applicants, including people with disabilities.
How the ADA protects against disability discrimination in hiring
The Americans with Disabilities Act (ADA) is a federal law that seeks to remove barriers for people with disabilities in everyday activities, including employment.
**The ADA applies to all parts of employment, including how an employer selects, tests, or promotes employees. An employer who chooses to use a hiring technology must ensure that its use does not cause unlawful discrimination on the basis of disability.
The ADA bars discrimination against people with many different types of disabilities.
Some examples of conditions that may be disabilities include: diabetes, cerebral palsy, deafness, blindness, epilepsy, mobility disabilities, intellectual disabilities, autism, and mental health disabilities. A disability will affect each person differently.
When designing or choosing hiring technologies, employers must consider how their tools could impact different disabilities.
For example, a state transportation agency that designs its hiring technology to avoid discriminating against blind applicants may still violate the ADA if its technology discriminates against applicants with autism or epilepsy.
When employers’ use of hiring technologies may violate the ADA
Employers must avoid using hiring technologies in ways that discriminate against people with disabilities. This includes when an employer uses another company’s discriminatory hiring technologies.
Even where an employer does not mean to discriminate, its use of a hiring technology may still lead to unlawful discrimination. For example, some hiring technologies try to predict who will be a good employee by comparing applicants to current successful employees. Because people with disabilities have historically been excluded from many jobs and may not be a part of the employer’s current staff, this may result in discrimination. Employers must carefully evaluate the information used to build their hiring technologies.
Screening Out People with Disabilities
Employers also violate the ADA if their hiring technologies unfairly screen out a qualified individual with a disability. Employers can use qualification standards that are job-related and consistent with business necessity. But employers must provide requested reasonable accommodations that will allow applicants or employees with disabilities to meet those standards, unless doing so would be an undue hardship. When designing or choosing hiring technologies to assess whether applicants or employees have required skills, employers must evaluate whether those technologies unlawfully screen out individuals with disabilities.
Employers should examine hiring technologies before use, and regularly when in use, to assess whether they screen out individuals with disabilities who can perform the essential functions of the job with or without required reasonable accommodations.
For example, if a county government uses facial and voice analysis technologies to evaluate applicants’ skills and abilities, people with disabilities like autism or speech impairments may be screened out, even if they are qualified for the job.
Some employers try to evaluate their hiring technologies to see how they impact certain groups, like racial minorities. Employers seeking to do the same with respect to people with disabilities must keep in mind that there are many types of disabilities and hiring technologies may impact each in a different way.
How to avoid disability discrimination when using hiring technologies
Testing technologies must evaluate job skills, not disabilities.
Some hiring technologies require an applicant to take a test that includes an algorithm, such as an online interactive game or personality assessment. Under the ADA, employers must ensure that any such tests or games measure only the relevant skills and abilities of an applicant, rather than reflecting the applicant’s impaired sensory, manual, or speaking skills that the tests do not seek to measure.
For example, an applicant to a school district with a vision impairment may get passed over for a staff assistant job because they do poorly on a computer-based test that requires them to see, even though that applicant is able to do the job.
If a test or technology eliminates someone because of disability when that person can actually do the job, an employer must instead use an accessible test that measures the applicant’s job skills, not their disability, or make other adjustments to the hiring process so that a qualified person is not eliminated because of a disability.
In addition, employers must ensure that they do not unlawfully seek medical or disability-related information or conduct medical exams through their use of hiring technologies. For more information about this, see the EEOC’s Enforcement Guidance: Preemployment Disability-Related Questions and Medical Examinations.
Reasonable accommodations for applicants with disabilities.
The ADA requires that employers provide reasonable accommodations to individuals with disabilities, including during the hiring process, unless doing so would create an undue hardship for the employer.
A reasonable accommodation is a change in the way things are usually done to give equal opportunities to a person with a disability in applying for a job, performing a job, or accessing the benefits and privileges of employment.
Examples of accommodations include allowing use of assistive equipment, modifying policies, or making other changes to the way the hiring process or job is performed.
For example, if a city government uses an online interview program that does not work with a blind applicant’s computer screen-reader program, the government must provide a reasonable accommodation for the interview, such as an accessible version of the program, unless it would create an undue hardship for the city government.
Some examples of practices that employers using hiring technologies may need to implement to ensure that applicants receive needed reasonable accommodations include:
telling applicants about the type of technology being used and how the applicants will be evaluated;
providing enough information to applicants so that they may decide whether to seek a reasonable accommodation; and
providing and implementing clear procedures for requesting reasonable accommodations and making sure that asking for one does not hurt the applicant’s chance of getting the job.
What to do if your rights have been violated or you want to find out more
If you believe your employment rights have been violated because of a disability and you want to make a claim of employment discrimination, you can file a “charge of discrimination” with the EEOC. A discrimination charge is a signed statement asserting that an organization engaged in employment discrimination. Information on the EEOC charge process is available here.
If you believe that you or someone else was discriminated against based on a disability because of a state or local government employer’s use of a hiring technology, you can also file a complaint with the Department of Justice. Information on the DOJ complaint process is available here.
For more detail on the topics addressed here and the impact of software, algorithms, and artificial intelligence on employees, please see the EEOC’s technical assistance document, The Americans with Disabilities Act and the Use of Software, Algorithms, and Artificial Intelligence to Assess Job Applicants and Employees. Anyone with questions about the impact of software, algorithms, and artificial intelligence on employees can reach the EEOC at 1-202-921-3191 (voice), 1-800-669-6820 (TTY), or 1-844-234-5122 (ASL Video Phone).
In addition, anyone can call the ADA Information Line at 800-514-0301 (voice) or 1-833-610-1264 (TTY) with questions about their rights or responsibilities under the ADA. ADA Specialists are available to answer questions Monday through Friday.
| 2025-06-13T00:00:00 |
2025/06/13
|
https://www.ada.gov/resources/ai-guidance/
|
[
{
"date": "2022/05/12",
"position": 43,
"query": "artificial intelligence hiring"
},
{
"date": "2022/05/12",
"position": 15,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 16,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 90,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 46,
"query": "artificial intelligence hiring"
},
{
"date": "2022/05/12",
"position": 48,
"query": "artificial intelligence hiring"
},
{
"date": "2022/05/12",
"position": 24,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 35,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 45,
"query": "artificial intelligence hiring"
},
{
"date": "2022/05/12",
"position": 15,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 25,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 38,
"query": "artificial intelligence hiring"
},
{
"date": "2022/05/12",
"position": 43,
"query": "artificial intelligence hiring"
},
{
"date": "2022/05/12",
"position": 47,
"query": "artificial intelligence hiring"
},
{
"date": "2022/05/12",
"position": 23,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 33,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 48,
"query": "artificial intelligence hiring"
},
{
"date": "2022/05/12",
"position": 38,
"query": "artificial intelligence hiring"
},
{
"date": "2022/05/12",
"position": 54,
"query": "artificial intelligence employers"
},
{
"date": "2022/05/12",
"position": 92,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 47,
"query": "artificial intelligence hiring"
},
{
"date": "2022/05/12",
"position": 15,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 15,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 19,
"query": "AI hiring"
},
{
"date": "2022/05/12",
"position": 40,
"query": "artificial intelligence hiring"
}
] |
AI Technology Regulations, Transparency in AI, OSHA's Permanent ...
|
AI Technology Regulations, Transparency in AI, OSHA’s Permanent COVID-19 Standard
|
https://www.ebglaw.com
|
[] |
Regulations and enforcement are tightening for the use of AI technology in employment decisions, on both the state and federal level. What can ...
|
This week, we focus on compliance and transparency when using artificial intelligence (AI) tools in employment decision-making.
The Future of AI Technology Regulations for Employers
Regulations and enforcement are tightening for the use of AI technology in employment decisions, on both the state and federal level. What can employers do to ensure compliance?
Attorneys Nathaniel Glasser and Sahar Shiralian tell us more.
Video: YouTube, Vimeo.
Podcast: Apple Podcasts, Google Podcasts, Overcast, Spotify, Stitcher.
Legal Risks and Remedies for AI’s “Black Box” Problem
When using AI and machine-learning algorithms for workforce decision-making, employers need to understand the basis for automated decisions or recommendations. Join our virtual briefing on June 9 to learn more about strategies for achieving transparency and explainability in AI, assuring regulatory compliance, and avoiding legal liability. Register today.
OSHA’s Permanent COVID-19 Health Care Standard
What challenges are health care providers likely to face as the Occupational Safety and Health Administration prepares its permanent COVID-19 standard for health care workers? Hear more in our recent Diagnosing Health Care podcast episode. Listen now.
Other Highlights
COVID-19 WORKFORCE (re)sources
Click here to see what the federal government and state and local governments have done to address the COVID-19 pandemic this week.
5 Timekeeping Tips from a Former WHD Administrator
HR Dive
Paul DeCamp featured
Court Holds That Judges Can’t Invent Rules Governing Arbitration Waiver and Makes It Harder for Prisoners to Show Ineffective Assistance: SCOTUS Today
SCOTUS Today
Stuart Gerson
What to Do When You Have to Give a Deposition for Your Employer
Commercial Litigation Update
Thomas Kane, Lauren Brophy Cooper
Connecticut Legislature Seeks to Codify Limitations on Noncompetes
Trade Secrets & Employee Mobility
Erik Weibust
About Employment Law This Week
Employment Law This Week® gives a rundown of the top developments in employment and labor law and workforce management in a matter of minutes every #WorkforceWednesday®.
Trouble viewing the video? Please contact [email protected] and mention whether you were at home or working within a corporate network. We'd also love your suggestions for topics and guests!
Email Notifications
Podcast Apps
Never miss an episode! Subscribe to Employment Law This Week on your preferred platform:
Also on Audacy | Audible | Deezer | Goodpods | iHeartRadio | PlayerFM | Pocket Casts | YouTube Music
Spread the Word
Would your colleagues, professional network, or friends benefit from #WorkforceWednesday? Please like and share the edition each week on LinkedIn, Facebook, X, and YouTube, and encourage your connections to subscribe for email notifications.
EMPLOYMENT LAW THIS WEEK® and #WorkforceWednesday® are registered trademarks of Epstein Becker & Green, P.C.
| 2022-05-25T00:00:00 |
https://www.ebglaw.com/insights/podcasts/ai-technology-regulations-transparency-in-ai-oshas-permanent-covid-19-standard
|
[
{
"date": "2022/05/25",
"position": 85,
"query": "AI regulation employment"
}
] |
|
Artificial intelligence in healthcare: Applications, risks, and ethical ...
|
Artificial intelligence in healthcare: Applications, risks, and ethical and societal impacts
|
https://www.europarl.europa.eu
|
[] |
This study offers an overview of how AI can benefit future healthcare, in particular increasing the efficiency of clinicians, improving medical diagnosis and ...
|
In recent years, the use of artificial intelligence (AI) in medicine and healthcare has been praised for the great promise it offers, but has also been at the centre of heated controversy. This study offers an overview of how AI can benefit future healthcare, in particular increasing the efficiency of clinicians, improving medical diagnosis and treatment, and optimising the allocation of human and technical resources. The report identifies and clarifies the main clinical, social and ethical risks posed by AI in healthcare, more specifically: potential errors and patient harm; risk of bias and increased health inequalities; lack of transparency and trust; and vulnerability to hacking and data privacy breaches. The study proposes mitigation measures and policy options to minimise these risks and maximise the benefits of medical AI, including multi-stakeholder engagement through the AI production lifetime, increased transparency and traceability, in-depth clinical validation of AI tools, and AI training and education for both clinicians and citizens.
| 2022-06-01T00:00:00 |
https://www.europarl.europa.eu/thinktank/en/document/EPRS_STU(2022)729512
|
[
{
"date": "2022/06/01",
"position": 29,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/06/01",
"position": 34,
"query": "artificial intelligence healthcare"
}
] |
|
Addressing the Impact of Artificial Intelligence on Journalism
|
Addressing the Impact of Artificial Intelligence on Journalism: the perception of experts, journalists and academics
|
https://revistas.unav.edu
|
[
"Amaya Noain-Sánchez",
"Universidad Rey Juan Carlos"
] |
This study aims to analyse the application of AI in newsrooms, focusing on the impact on news-making processes, media routines and profiles.
|
Beckett, C. (2019). New powers, new responsibilities: A global survey of Journalism and Artificial Intelligence. London: The London School of Economics.
Brennen, J. S., Howard, P. N. & Nielsen, R. S. (2018) An industry-led debate: how UK media cover artificial intelligence. Reuters Institute for the Study of Journalism. Retrieved from https://www.oxfordmartin.ox.ac.uk/publications/an-industry-led-debate-how-uk-media-cover-artificial-intelligence/
Bodó, B., Helberger, N., Eskens, S. & Möller, J. (2019). Interested in diversity: The role of user attitudes, algorithmic feedback loops, and policy in news personalization. Digital Journalism, 7(2). https://www.doi.org/10.1080/21670811.2018.1521292
Bodó, B. (2018). Means, Not an End (of the World)–The Customization of News Personalization by European News Media. SSRN. Retrieved from https://papers.ssrn.com/abstract=3141810
Carlson, M. (2015). The robotic reporter: automated journalism and the redefinition of labor, compositional forms, and journalistic authority. Digital journalism, 3(3), 416-431.
Caswell, D. & Dörr, K. (2018). Automated Journalism 2.0: Event-driven narratives. Journalism Practice, 12(4), 477-496. http://www.doi.org/10.1080/17512786.2 017.1320773
Clerwall, C. (2014). Enter the robot journalist. Journalism Practice, 8(5), 519-531.
Diakopoulos, N. (2014). Algorithmic accountability. Digital Journalism, 3(3), 398-415. https://www.doi.org/10.1080/21670811.2014.976411
Coddington, M. (2015). Clarifying journalism’s quantitative turn: A typology for evaluating data journalism, computational journalism, and computer-assisted reporting. Digital Journalism, 3(3), 331-348. https://www.doi.org/10.1080/21670811.2014.976400
Cohen, S., Hamilton, J. & Turner, F., (2011). Computational journalism. Communications of the ACM, 54(10), 66-71. https://www.doi.org/10.1145/2001269.2001288
Daewon, K. & Seongcheol, K. (2018). Newspaper journalists’ attitudes towards robot journalism. Telematics and Informatics, 35, 340-357.
DeVito, M. A. (2017). From Editors to Algorithms. Digital Journalism, 5(6), 753-773. https://www.doi.org/10.1080/21670811.2016.1178592
Díaz-Campo, J. & Chaparro-Domínguez, M. A. (2020). Periodismo computacional y ética: Análisis de los códigos deontológicos de América Latina, Icono 14, 18(1), 10-32. https://www.doi.org/10.7195/ri14.v18i1.1488
Dörr, K. & Hollnbuchner, K. (2017). Ethical Challenges of Algorithmic Journalism. Digital Journalism, 5(4), 404-419. https://www.doi.org/10.1080/21670811.2016.1167612
Dörr, K. (2016). Mapping the field of algorithmic journalism. Digital Journalism, 4(6), 700-722. https://www.doi.org/10.1080/21670811.2015.1096748
European Broadcasting Union (2019). News report 2019 the next newsroom unlocking the power of AI for public service journalism. EBU. Retrieved from https://www.ebu.ch/publications/strategic/login_only/report/news-report-2019
Fanta, A. (2017). Putting Europe’s robots on the map: Automated journalism in news agencies. University of Oxford; Reuters Institute for the Study of Journalism. Retrieved from http://bit.ly/2m3NFzv
Freixa, P., Pérez-Montoro, M. & Codina, L. (2021). The binomial of interaction and visualization in digital news media: consolidation, standardization and future challenges. Profesional de la información, 30(4). https://www.doi.org/10.3145/epi.2021.jul.01
Flores Vivar, J. M. (2019). Inteligencia artificial y periodismo: diluyendo el impacto de la desinformación y las noticias falsas a través de los bots. Doxa Comunicación, 29, 197-212.
Graefe, A. (2016). Guide to automated journalism. Retrieved from https://www.cjr.org/tow_center_reports/guide_to_automated_journalism.php
Gómez-Diago, G. (2022). Perspectivas para abordar la inteligencia artificial en la enseñanza de periodismo. Una revisión de experiencias investigadoras y docentes. Revista Latina De Comunicación Social, 80, 29-46. https://www.doi.org/10.4185/RLCS-2022-1542
Haass, J (2020). Freedom of the media in artificial intelligence. Retrieved from https://www.osce.org/files/f/documents/4/5/472488.pdf
Hansen, M., Roca-Sales, M., Keegan, J. & King G. (2017). Artificial Intelligence: Practice and Implications for Journalism. Brown Institute for media innovation and the tow center for digital journalism. Columbia Journalism School. https://www.doi.org/10.7916/d8x92prd
Helberger, N. (2019). On the Democratic Role of News Recommenders. Digital Journalism, 7(8), 993-1012. https://www.doi.org/10.1080/21670811.2019.1623700
Ignatidou, S. (2019). AI-driven Personalization in Digital Media. London: Chatham House.
Latar, N. L. & Nordfors, D. (2009). Digital Identities and Journalism Content-How Artificial Intelligence and Journalism May Co-Develop and Why Society Should Care. Innovation Journalism, 6(7), 3-47.
Lecompte, C. (2015). Automation in the newsroom. Nieman Reports, 69(3), 32-45. Retrieved from http://niemanreports.org/wp-content/uploads/2015/08/NRsummer2015.pdf
Lindén, C. (2017). Algorithms for journalism: the future of news work. The Journal of Media Innovation, 4(1), 60-76. https://www.doi.org/10.5617/jmi.v4i1.2420
Loosen, W. (2018). Four forms of datafied journalism. Journalism’s response to the datafication of society, 18. Communicative Figurations Working Paper.
Manfredi Sánchez, J. L. & Ufarte Ruiz, M. J. (2020). Inteligencia artificial y periodismo: una herramienta contra la desinformación. Revista CIDOB d’Afers Internacionals, 124, 49-72. https://www.doi.org/10.24241/rcai.2020.124.1.49
Marconi, F. (2020). Newsmakers: Artificial Intelligence and the Future of Journalism. New York, NY: Columbia University Press.
Marconi, F. & Siegman, A. (2017). The future of augmented journalism: a guide for newsrooms in the age of smart machines. About AP insights. Retrieved from https://insights.ap.org/uploads/images/the-future-of-augmented-journalism_ap-report.pdf
McCarthy, J., Minsky, M., Rochester, N. & Shannon, C. (1955). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Retrieved from http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html
Möller J., Trilling, D., Helberger, N. & van Es, B. (2018). Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity. Information, Communication & Society, (21)7, 959-977. https://www.doi.org/10.1080/1369118X.2018.1444076
Montal, T. & Reich, Z. (2017). I, robot. You, journalist. Who is the author? Digital journalism, 5(7), 829-849. https://www.doi.org/10.1080/21670811.2016.1209083
Monzer, C., Moeller, J., Neys, J. L. D. & Helberger, N. (2018). Who has control and who is responsible? Implications of news personalization from the user perspective. Paper presented to the Annual Conference of the International Communication Association, Communication and Technology Division. Prague.
Napoli, P. (2012). Audience evolution and the future of audience research. International Journal on Media Management, 14(2), 79-97. https://www.doi.org/10.1080/14241277.2012.675753
Newman, N. (2020). Journalism, media and technology: trends and predictions for 2020. London: Reuters Institute for the Study of Journalism & Oxford University. Retrieved from https://reutersinstitute.politics.ox.ac.uk/periodismo-medios-y-tecnologia-tendencias-y-predicciones-para-2020
Newman, N. (2018). Journalism, Media, and Technology trends and predictions 2018. London: Reuters Institute for the Study of Journalism & Oxford University. Retrieved from https://reutersinstitute.politics.ox.ac.uk/our-research/journalism-media-and-technology-trends-and-predictions-2018
Rojas Torrijos, J. L. & Toural Bran, C. (2019). Periodismo deportivo automatizado. Estudio de caso de AnaFut, el bot desarrollado por El Confidencial para la escritura de crónicas de fútbol. Doxa Comunicación, 29, 235-254.
Ruiz, J. J., Vila, P., Corral, D., Pérez, C., Crespo, E., Mayoral, E., Martín, M. A., Cánovas, P., Pérez-Tornero, J. M., Pulido, C., Tejedor, S., Cervi, L., Sanjinés, D., Zhang, W. & Tayie, S. (2019). Detección de noticias a través de aplicaciones de inteligencia artificial: la inteligencia artificial aplicada a informativos 2019-2020. Barcelona: Observatorio para la Innovación de los Informativos en la Sociedad Digital (OI2), RTVE. Retrieved from https://ddd.uab.cat/record/219951
Ruiz, J. J., Vila, P., Corral, D., Pérez, C., Crespo, E., Mayoral, E., Martín, M. A., Cánovas, P., Pérez-Tornero, J. M., Pulido, C., Tejedor, S., Cervi, L., Sanjinés, D., Zhang, W. & Tayie, S. (2020a). Generación automática de textos periodísticos: la inteligencia artificial aplicada a informativos 2019-2020. Barcelona: Observatorio para la Innovación de los Informativos en la Sociedad Digital (OI2), RTVE. Retrieved from http://www.gabinetecomunicacionyeducacion.com/sites/default/files/field/adjuntos/informe_02_generaciontextos_3_baja.pdf
Ruiz, J. J., Vila, P., Corral, D., Pérez, C., Crespo, E., Mayoral, E., Martín, M. A., Cánovas, P., Pérez-Tornero, J. M., Pulido, C., Tejedor, S., Cervi, L., Sanjinés, D., Zhang, W. & Tayie, S. (2020b). Personalización de contenidos en medios audiovisuales: la inteligencia artificial aplicada a informativos 2019-2020. Barcelona: Observatorio para la Innovación de los Informativos en la Sociedad Digital (OI2), RTVE. Retrieved from http://www.gabinetecomunicacionyeducacion.com/sites/default/files/field/adjuntos/informe_3.pdf
Segarra-Saavedra, J., Cristòfol, F. J. & Martínez-Sala, A. M. (2019). Inteligencia artificial (IA) aplicada a la documentación informativa y redacción periodística deportiva. El caso de BeSoccer. Doxa Comunicación, 29, 275-286. https://www.doi.org/10.31921/doxacom.n29a14
Tejedor-Calvo, S., Romero-Rodríguez, L. M., Moncada-Moncada, A. .J., Alencar-Dornelles, M. (2020). Journalism that tells the future: possibilities and journalistic scenarios for augmented reality. Profesional de la información, 29(6). https://www.doi.org/10.3145/epi.2020.nov.02
Papadimitriou, A. (2016). The Future of Communication: Artificial Intelligence and Social Networks. Mälmo: Mälmo University. Retrieved from https://muep.mau.se/bitstream/handle/2043/21302/The%20Future%20of%20 Communication.pdf?sequence=2.
Oremus, W. (2015). No more pencils, no more books. Slate. Retrieved from http://publicservicesalliance.org/wp-content/uploads/2015/10/Adaptive-learning-software-is-replacing-textbooks-and-upending-American-education.-Should-we-welcome-it.pdf
Parasie, S. & Dagiral, E. (2012). Data-driven journalism and the public good: “Computer-assisted-reporters” and “programmer-journalists” in Chicago. New Media & Society, 15(6), 853-871. https://www.doi.org/10.1177/1461444812463345
Tejedor, S. & Vila, P. (2021). Exo Journalism: A Conceptual Approach to a Hybrid Formula between Journalism and Artificial Intelligence. Journal. Media, 2, 830-840. https://www.doi.org/10.3390/journalmedia2040048
Russell, S. & Norvig, P. (2019). Artificial intelligence: A modern approach. Berkeley: Pearson Education.
Salazar, I. (2018). Los robots y la inteligencia artificial. Nuevos retos del periodismo. Doxa Comunicación, 27, 295-315. https://www.doi.org/10.31921/doxacom.n27a15
Sandoval-Martín, T. & La-Rosa, L. (2018). Big Data as a differentiating sociocultural element of data journalism: the perception of data journalists and experts. Communication & Society, 31(4), 193-209.
Stavelin, E. (2014). Computational journalism: when journalism meets programming. Doctoral Thesis. University of Bergen.
The Guardian (2020, September 18) A robot wrote this entire article. Are you scared yet, human? Retrieved from https://www.theguardian.com/commentisfree/2020/sep/08/robot-wrote-this-article-gpt-3
Thurman, N., Dörr, K. & Kunert, J. (2017). When reporters get hands-on with Robo-writing. Digital journalism, 5(10), 1240-1259. https://www.doi.org/10.1080/21670811.2017.1289819
Thurman, N., Moeller, J., Helberger, N. & Trilling, D. (2018). My friends, editors, algorithms, and I: Examining audience attitudes to news selection. Digital Journalism, 7(4). https://www.doi.org/10.1080/21670811.2018.1493936
Túñez-López, M., Toural-Bran, C. & Valdiviezo-Abad , C. (2019). Automatización, bots y algoritmos en la redacción de noticias. Impacto y calidad del periodismo artificial. Revista Latina de Comunicación Social, 74, 1411-1433. https://www.doi.org/10.4185/RLCS-2019-1391
Túñez-López, M., Toural-Bran, C. & Cacheiro-Requeijo, S. (2018). Uso de bots y algoritmos para automatizar la redacción de noticias: percepción y actitudes de los periodistas en España. El profesional de la información, 27(4), 750-758. https://www.doi.org/10.3145/epi.2018.jul.04
Túñez-López, J. M., Fieiras Ceide, C. & Vaz-Álvarez, M. (2021). Impacto de la Inteligencia Artificial en el Periodismo: transformaciones en la empresa, los productos, los contenidos y el perfil profesional. Communication & Society, 34(1), 177-193.
Ufarte Ruiz, M. J. & Manfredi Sánchez, J. L. (2019). Algoritmos y bots aplicados al periodismo. El caso de Narrativa Inteligencia Artificial: estructura, producción y calidad informativa. Doxa Comunicación, 29, 213-233. https://www.doi.org/10.31921/doxacom.n29a11.
Túñez-López, J. M., Fieiras Ceide, C. & Vaz-Álvarez, M. (2021). Impact of Artificial Intelligence on Journalism: transformations in the company, products, contents and professional profile. Communication & Society, 34(1), 177-193.
Ufarte Ruiz, M. J., Túñez López, J. M. & Vaz Álvarez, M. (2019). La aplicación del periodismo artificial en el ámbito internacional: retos y desafíos. In J. L. Manfredi Sánchez, M. J. Ufarte Ruiz & J. M. Herranz de la Cas (Eds.), Periodismo y Ciberseguridad (pp. 67-88). Salamanca: Comunicación Social.
Ufarte Ruiz, M. .J.; Calvo Rubio, L. M. & Murcia Verdú, F. J. (2021). Los desafíos éticos del periodismo en la era de la inteligencia artificial. Estudios sobre el Mensaje Periodístico, 27(2), 673-684. https://www.doi.org/10.5209/esmp.69708
Ufarte Ruiz, M. J., Fieiras-Ceide, C. & Túñez-López, M. (2020). L’ensenyament-aprenentatge del periodisme automatitzat en institucions públiques: estudis, propostes de viabilitat i perspectives d’impacte de la IA. Anà lisi: quaderns de comunicació i cultura, 62, 131-46. https://www.doi.org/10.5565/rev/analisi.3289
Vállez, M. & Codina, L. (2018). Periodismo computacional: evolución, casos y herramientas. El profesional de la información, 27(4), 759-768. https://www.doi.org/10.3145/epi.2018.jul.05
Van Dalen, A. (2012). The algorithms behind the headlines. Journalism Practique, 6(5-6), 648-658. https://www.doi.org/10.1080/17512786.2012.667268
Van den Bulck, H. & Moe, H. (2017). Public service media, universality and personalisation through algorithms: mapping strategies and exploring dilemmas. Media, Culture & Society, 40(6), 875-892. https://www.doi.org/10.1177%2F0163443717734407
Wu, Y. (2019) How Age Affects Journalists’ Adoption of Social Media as an Innovation, Journalism Practice, 13(5), 537-557. https://www.doi.org/10.1080/17512786.2018.1511821
Yanfang, W. (2019). Is Automated Journalistic Writing Less Biased? An Experimental Test of Auto-Written and Human-Written News Stories. Journalism Practique, 14, 1-21. https://www.doi.org10.1080/17512786.2019.1682940
| 2022-06-07T00:00:00 |
https://revistas.unav.edu/index.php/communication-and-society/article/view/41216
|
[
{
"date": "2022/06/07",
"position": 53,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 52,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 49,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 46,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 46,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 79,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 46,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 46,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 46,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 48,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 42,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 42,
"query": "artificial intelligence journalism"
},
{
"date": "2022/06/07",
"position": 39,
"query": "artificial intelligence journalism"
}
] |
|
AI and Job Displacement: Navigating the Ethical Implications of ...
|
AI and Job Displacement: Navigating the Ethical Implications of Automation
|
https://www.hakia.com
|
[] |
Workers in low-skill jobs may find themselves more vulnerable to displacement, while those with higher education or specialized skills are ...
|
The Current Landscape of AI and Automation in the Workforce
As you explore the changing dynamics of the modern workplace, it's vital to grasp how AI and automation are redefining various industries. With rapid advancements in technology, organizations increasingly adopt AI-driven tools and automated systems to enhance productivity and streamline operations. These innovations range from simple task automation to sophisticated algorithms capable of complex decision-making processes. You may notice that sectors like manufacturing, logistics, healthcare, and customer service have aggressively integrated automation technologies. Assembly lines now feature robots performing repetitive tasks, while AI chatbots handle customer inquiries with remarkable efficiency. In healthcare, AI assists in diagnostics and patient monitoring, enabling professionals to focus on more intricate aspects of care. The impact of AI and automation on employment is nuanced. While these technologies may displace certain job functionalities, they also create a demand for new roles that require human oversight, creativity, and interpersonal skills. For instance, as AI takes over routine data entry tasks, there is a greater need for data analysts to interpret and leverage insights drawn from that data. You might also observe that the rise of remote work, accelerated by the COVID-19 pandemic, further intertwines with automation. Enhanced communication tools and project management software leverage AI to facilitate collaboration across distributed teams. This integration shapes a future where human expertise is complemented by automated technologies, ultimately changing job descriptions and skill requirements. Yet, the adoption of AI and automation does not come without its challenges. The potential for job loss raises ethical questions about responsibility, equity, and the future of work. As a stakeholder in this evolving landscape, understanding these dimensions is essential for navigating the implications of technology on your workforce and the broader society. Balancing innovation with ethical considerations becomes key to addressing concerns about job displacement and ensuring that the transition to an automated workforce is both equitable and sustainable.
Historical Context of Technological Advancements and Employment
Throughout history, technological advancements have continually reshaped the employment landscape. The Industrial Revolution serves as a significant reference point, marking a transformative period when machinery began replacing manual labor in various industries. This shift not only increased productivity but also altered the nature of work. Workers migrated from rural areas to urban centers in search of factory jobs, fundamentally changing societal structures and economic models. As you analyze subsequent technological shifts, one cannot ignore the impact of the assembly line and mass production techniques in the early 20th century. These innovations not only improved efficiency but also standardized labor processes, leading to an increase in both job availability and specialization in the workforce. Yet, this progress came at a cost; many skilled artisans found themselves displaced as their trades became obsolete. The latter half of the 20th century introduced further advancements, most notably in computing and information technology. The advent of computers began to automate tasks previously performed by humans, thereby altering roles across various sectors, from administrative positions to manufacturing jobs. As computers gained complexity and connectivity, the need for a skilled workforce adept in navigating these tools became more pronounced, leading to both job creation and displacement. With the dawn of the 21st century, the emergence of robotics and artificial intelligence heralded another significant transition. Industries began integrating AI to enhance efficiency and streamline operations, a move that often meant the replacement of jobs that were routine or repetitive in nature. Professions in sectors such as retail, logistics, and even services saw substantial shifts, as automated systems offered cost-effective alternatives to human labor. As you navigate this historical context, it is essential to consider the socio-economic implications of these technological advancements. Each wave of innovation has prompted a complex interplay between job creation and displacement, demanding that societies adapt and evolve. Understanding this trajectory allows for a deeper insight into the current discourse surrounding AI and the ethical considerations that arise from the automation of jobs. You must remain aware that while new technologies can foster economic growth and create opportunities, they can also lead to significant challenges in workforce transition and worker equity.
Economic Impacts of AI on Job Creation and Displacement
The integration of artificial intelligence into various sectors brings forth a complex landscape of economic impacts, particularly concerning job creation and displacement. As you navigate this terrain, it is important to recognize both the opportunities and challenges posed by AI advancements. AI has the potential to create new jobs that did not previously exist. Emerging technologies often require a workforce skilled in programming, data analysis, and machine learning. You may find that roles in AI ethics, data stewardship, and AI system management are on the rise, driven by the need for oversight and regulation of autonomous systems. Additionally, as companies automate repetitive tasks, they can redirect their human resources toward more strategic initiatives, fostering innovation and new products, thereby creating further employment opportunities. Despite these potential benefits, job displacement remains a significant concern. Automation can lead to the reduction of roles traditionally held by humans, particularly in sectors such as manufacturing, retail, and administrative support. Routine tasks are increasingly being handled by AI systems, which might result in workforce reductions in these areas. As you assess the implications, consider that the transition from manual labor to automation does not happen instantaneously; it can lead to economic disruption and require workers to adapt through retraining and upskilling. Furthermore, the economic disparities that arise from AI implementation can exacerbate existing inequalities. Workers in low-skill jobs may find themselves more vulnerable to displacement, while those with higher education or specialized skills are better positioned to thrive in an AI-driven economy. Society must address these divides to ensure that the workforce can adapt effectively. It is essential to develop policies that support education and training initiatives geared toward emerging technologies, thus fostering an environment where workers can transition into new roles seamlessly. The geographic dynamics of job creation and displacement should also be considered. Urban areas, often hubs for technological development, may experience a surge in AI-related job opportunities. In contrast, rural regions may face higher risks of displacement without access to retraining programs or the ability to attract new industries. Addressing these imbalances through targeted economic policies and investments can help mitigate regional disparities. As you evaluate the economic impacts of AI, acknowledge the dual nature of its influence on job markets. Encouragingly, while some jobs may vanish, others will emerge. The key lies in fostering an adaptive workforce capable of embracing new roles and responsibilities in this evolving landscape. Engaging with policymakers, educational institutions, and industry leaders will be essential in shaping a future that balances the merits of AI with the ethical considerations of job displacement and creation.
Ethical Considerations in Employing AI Technologies
As you navigate the landscape of AI technologies and their impact on the workforce, it is essential to consider the ethical ramifications of automation. The deployment of AI can lead to significant job displacement, and addressing the moral implications that accompany this shift demands careful attention. One key consideration is the responsibility of organizations to mitigate the negative effects of job losses caused by AI integration. You must think about how to balance efficiency and profit margins with the obligation to support affected employees. Providing adequate retraining programs and resources can help individuals transition to new roles in an evolving job market. This commitment acknowledges the human element amidst technological advancement. Transparency in AI implementation is another crucial aspect. You should strive to communicate clearly how AI systems operate and the decision-making processes that influence workforce changes. This transparency fosters trust between employers and employees, enabling a more open dialogue about the strategic direction of the organization and the role of AI in shaping the future of work. Additionally, consider the biases that may be inherent in AI technologies. Algorithms can sometimes perpetuate existing inequalities if not designed thoughtfully. It is your responsibility to ensure that AI systems are developed and implemented in an equitable manner, minimizing discrimination based on gender, race, or socioeconomic status. Engaging diverse teams in AI development can play a significant role in identifying and correcting biases in technology. Furthermore, think about the implications of surveillance and monitoring that often accompany the use of AI in the workplace. While these technologies can enhance productivity, you need to weigh the potential invasion of privacy that may result. Striking a balance between the benefits of monitoring and the respect for individual privacy rights is essential to foster a supportive work environment. Lastly, you should consider the long-term impacts of automation on society as a whole. When assessing the ethical use of AI, it's worth examining its potential to create wealth disparities and exacerbate unemployment rates. Being proactive in developing policies that address these concerns will be crucial for promoting social welfare in an increasingly automated world. Incorporating these ethical considerations into your strategy for deploying AI can help you navigate the complex relationship between technology and workforce dynamics, ultimately leading to a more responsible and inclusive approach to automation.
Societal Implications of Job Displacement Due to Automation
The ramifications of job displacement due to automation extend beyond the individual worker, impacting families, communities, and the broader economy. You must consider how these changes affect social dynamics and economic stability. As sectors increasingly adopt AI and automation technologies, traditional employment roles are at risk, leading to heightened unemployment rates in certain industries, particularly those that are low-skilled and repetitive. This shift presents a challenge not only for displaced workers but for society as a whole. In many cases, the immediate financial strain felt by displaced workers can lead to long-term socio-economic issues. You will observe rising levels of insecurity and instability among families that may struggle to meet basic needs. This stress can contribute to broader mental health issues, leading to an increase in anxiety and depression rates within affected communities. The sense of purpose and identity tied to employment often goes unaddressed, highlighting the need for supportive transition programs. There is also the potential for widening income inequality, as those with the skills to adapt to new roles thrive, while those unable to re-skill fall further behind. You may notice an evolving divide within the labor market, creating a class of workers with high technical skills in demand and another segment struggling with limited opportunities. This disparity not only hinders social cohesion but also poses risks to democratic stability, as economic frustrations may fuel populist movements or social unrest. Moreover, you should be wary of the implications for local economies that rely heavily on specific industries. As jobs disappear, businesses that depended on a stable workforce may collapse, leading to community decline and reduced local investment. This cycle can exacerbate existing regional disparities, making it essential to consider community-centric policies that promote diversification and innovative economic models to reduce dependency on any single industry. Finally, the advent of automation and AI has the potential to reshape societal values and expectations regarding work. If you view work merely as a means of survival, a significant shift in the understanding of meaningful contributions to society might occur. As automation reshapes the landscape, you may find that flexibility in work arrangements, collaboration, and interpersonal contributions gain greater emphasis, leading to a potential redefinition of productivity and fulfillment in society. You are encouraged to engage with these implications thoughtfully, advocating for policies that not only mitigate the adverse effects of job displacement but also promote equitable access to opportunities for all individuals affected by this technological transformation.
Strategies for Workforce Transition and Retraining
The rapid integration of AI and automation into various industries requires a thoughtful approach to workforce transition and retraining. You can adopt several strategies to facilitate this process effectively, helping individuals adapt and thrive in the changing job landscape. First, fostering a culture of lifelong learning within organizations is essential. Encourage employees to engage in continuous professional development and upskilling programs. This could involve offering workshops, online courses, and certifications that align with the evolving needs of the workplace. By investing in consistent training, you create a workforce that is agile and better prepared for new technologies. Next, partnerships with educational institutions can be beneficial. Collaborating with local colleges and universities to design curriculum tailored to emerging technologies will ensure that students are equipped with the necessary skills before entering the workforce. You might also consider internship or apprenticeship programs that allow individuals to gain hands-on experience while still in school, bridging the gap between education and employment. Career counseling and support services play a crucial role in workforce transition. Providing access to career coaches can help employees identify transferable skills and explore new job opportunities. You should implement programs that assist with resume writing, interview preparation, and networking, empowering individuals to navigate their career paths more effectively. Flexibility in job roles is another strategy you can adopt. As automation takes over routine tasks, consider redesigning job descriptions to focus on skills that AI cannot replicate, such as creativity, emotional intelligence, and complex problem-solving. This shift can create new opportunities and inspire employees to pursue roles that require adaptive skills. Furthermore, consider implementing phased transitions for employees at risk of displacement. Gradual shifts to new roles or responsibilities can allow workers to adjust more comfortably to changes in the workplace. Providing ample notice and resources during these transitions demonstrates a commitment to employee welfare and can help mitigate the anxiety surrounding job loss. Lastly, advocacy for policy frameworks supporting retraining efforts can amplify your impact. Engaging with government and industry bodies to promote initiatives that fund retraining programs and job placement services can create a more supportive environment for workers affected by automation. You should also explore tax incentives or grants that encourage companies to invest in employee development. By employing these strategies, you can help ensure that your workforce is better positioned to adapt to the advancements in technology while minimizing the negative impact of AI and automation on employment. A proactive approach not only protects individuals but also contributes to the long-term success of your organization in an increasingly automated world.
Policy Recommendations for Mitigating Job Displacement
To effectively mitigate job displacement caused by AI and automation, a multifaceted approach is necessary. You should consider the following strategies: Promote Lifelong Learning and Skills Development Investing in education systems and workforce training programs that emphasize lifelong learning can help employees adapt to the evolving job market. Encourage businesses to collaborate with educational institutions to ensure that training programs align with industry needs, focusing on skills that complement AI technologies rather than compete with them. Enhance Social Safety Nets Reinforcing social safety nets, such as unemployment insurance and retraining programs, can provide support for displaced workers. Policies should be developed to ensure that workers can access these resources easily and that they are tailored to the needs of specific industries facing automation. Incentivize Businesses for Responsible Automation Encourage companies to adopt responsible automation practices by offering tax incentives or grants for those that invest in employee retraining and development. This can help balance the cost of transitioning to automated systems while prioritizing employee well-being. Foster Innovative Job Creation Develop initiatives that stimulate job creation in sectors that are less likely to be automated, such as healthcare, green technologies, and creative industries. Supporting entrepreneurship and small businesses in these areas can lead to new job opportunities and economic growth. Implement Transition Programs Establish transition programs that assist workers in moving from declining industries to emerging sectors. These could include job placement services, mentorship programs, and short-term financial assistance to support workers while they acquire new skills and search for new employment. Encourage Public-Private Partnerships Facilitate partnerships between government entities and private organizations to address the impacts of automation on employment. Collaborating on research, training programs, and funding initiatives can lead to innovative solutions and more robust support for affected workers. Evaluate and Update Labor Laws Review and revise labor laws to ensure they are compatible with the changing nature of work. This includes considering regulations around gig and freelance work, protecting workers’ rights, and adapting to the needs of a workforce that may increasingly rely on non-traditional employment models. Leverage Technology for Workforce Development Encourage the use of technology to enhance workforce development programs. Online learning platforms and AI-driven assessment tools can provide personalized training solutions, enabling workers to develop the skills needed for emerging roles. By implementing these policy recommendations, you can help create a more resilient workforce that is better prepared to navigate the challenges presented by AI and automation.
Role of Education in Preparing for an Automated Future
As automation technologies advance, the role of education becomes increasingly important in preparing individuals for the workforce of tomorrow. You are not only faced with the challenge of adapting to new tools and systems but also transforming your own skills and mindset to remain competitive in an evolving job market. Educational institutions, from primary to higher education, need to focus on delivering relevant curricula that incorporate both soft and technical skills essential for thriving in an automated environment. To start, fostering critical thinking, creativity, and problem-solving skills becomes vital. These competencies are not easily replicated by machines, making them invaluable in an automated landscape where routine tasks can be performed by AI systems. By engaging in projects, collaborative learning, and real-world problem-solving scenarios, you can cultivate a mindset that enables you to navigate complexities and uncertainties. Furthermore, integrating technology into education plays a significant role in preparing you for an automated future. Familiarity with digital tools, coding languages, and data analysis should be woven into educational programs, ensuring that you can leverage these technologies in your future career. Emphasizing lifelong learning encourages you to stay informed about technological advancements and continually refine your skills. Moreover, vocational training and apprenticeships offer an alternative to traditional education paths, equipping you with hands-on experience in fields likely to grow despite automation. These programs can provide specific technical skills aligned with market demands, ensuring that you are prepared for the types of jobs that will emerge in an automated economy. Finally, you must embrace a mindset of adaptability. The job landscape will continue to shift, and your ability to pivot and learn new skills will determine your success in maintaining employability. Educational systems should promote resilience and a proactive approach to career development, encouraging you to anticipate changes and prepare for new opportunities. By fostering these competencies within the educational framework, you position yourself to thrive alongside advancing technologies rather than be left behind.
Case Studies of AI Implementation and Its Effects on Employment
Examining specific instances of AI implementation can provide valuable insights into how automation technologies impact employment across various industries. By analyzing these case studies, you can better understand the potential benefits and challenges that come with the integration of AI in the workplace. In the manufacturing sector, a large automotive company adopted AI-driven robotic process automation in its assembly lines. This move led to significant increases in productivity and reductions in manufacturing defects. However, the company faced challenges as many assembly line workers were trained for tasks that became automated. To mitigate job displacement, the company invested in reskilling programs, enabling workers to transition into roles focused on oversight, maintenance, and continual improvement of automated systems. This case highlights the dual impact of AI: while it can displace certain jobs, it also creates new opportunities through reskilling. In the financial services industry, several banks have incorporated AI systems for customer service functions, such as chatbots and automated loan processing. While these technologies improve efficiency and enhance customer experience, they also raise concerns about job losses in traditional call centers and loan processing departments. Some organizations have responded by repurposing those employees for more complex customer service roles that require human interaction, demonstrating a strategic approach to managing workforce transitions. A retail giant implemented an AI-driven inventory management system that analyzed customer purchasing patterns to optimize stock levels. This technology led to reduced costs and improved product availability. However, it also resulted in layoffs among inventory management personnel. The company opted to invest in transitional training for affected employees, focusing on positions in data analysis and strategy planning, guiding them toward new roles that leverage their existing skills in conjunction with the insights provided by AI. In healthcare, the deployment of AI for diagnostic purposes has shown promising results in areas such as radiology and pathology. AI systems can analyze medical images and identify anomalies more quickly than some human practitioners. This advancement can enhance patient care, but it has raised questions regarding the future role of radiologists. Hospitals have started to integrate AI not as a replacement for human expertise but as an augmentation, with AI systems treated as tools that can assist healthcare professionals in making better diagnostic decisions. As a result, the focus has shifted towards developing interdisciplinary roles that blend AI proficiency with medical expertise. These examples illustrate the complex interplay between AI technology and employment landscapes. Organizations that proactively address the impact of AI on their workforce through reskilling and workforce transformation strategies can not only mitigate the negative effects of job displacement but also harness the full potential of AI to create a more effective and agile workforce.
Future Outlook: The Evolving Relationship Between Humans and AI in the Workplace
As automation technologies advance, you will find the dynamics of human-AI collaboration in the workplace continually evolving. Rather than viewing AI solely as a threat to jobs, there is an emerging perspective focused on how AI can enhance human capabilities and optimize workflows. This shift in perception allows organizations to leverage AI as a tool for innovation rather than a mere replacement of the workforce. You can expect to see job roles transformed, wherein mundane and repetitive tasks are automated, freeing you to engage in more strategic and creative activities. This evolution requires a realignment of skill sets, emphasizing the importance of adaptability and continuous learning. Organizations may invest in reskilling programs to equip employees with the skills necessary for new roles that center around collaboration with AI technologies. The workplace of the future may also present opportunities for increased job creation in areas that AI cannot easily replicate. Fields such as AI ethics, maintenance of AI systems, and roles that require emotional intelligence will likely see growth as businesses adopt AI-driven solutions. Your role will increasingly involve navigating complex socio-technical systems, which demand human insight and empathy. Amid these changes, you should remain vigilant about the ethical implications of AI deployment. Questions surrounding data privacy, equity in job opportunities, and the potential for bias within AI systems will require your attention. Establishing transparent guidelines and fostering ethical AI practices will be imperative to ensure that the integration of AI in the workplace benefits everyone. As industries adopt AI at different paces, collaboration across sectors will be essential. Sharing best practices and experiences can help organizations thrive while fostering an inclusive environment where both AI and human workers can coexist. Adapting workplace culture to one that embraces technological advancement while maintaining a human touch will be key in navigating this new landscape. Ultimately, your willingness to embrace change and engage with AI as an augmentation to your work can lead to more efficient, innovative, and fulfilling workplace experiences. Preparing for an era where humans and AI collaborate seamlessly will set a foundation for sustainable growth and ethical business practices.
| 2022-06-21T00:00:00 |
https://www.hakia.com/ai-and-job-displacement-navigating-the-ethical-implications-of-automation
|
[
{
"date": "2022/06/21",
"position": 82,
"query": "robotics job displacement"
},
{
"date": "2022/06/21",
"position": 26,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 35,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 72,
"query": "robotics job displacement"
},
{
"date": "2022/06/21",
"position": 24,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 76,
"query": "robotics job displacement"
},
{
"date": "2022/06/21",
"position": 33,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 29,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 27,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 26,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 27,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 25,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 25,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 26,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 27,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 26,
"query": "automation job displacement"
},
{
"date": "2022/06/21",
"position": 73,
"query": "robotics job displacement"
},
{
"date": "2022/06/21",
"position": 27,
"query": "automation job displacement"
},
{
"date": "2025/05/02",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2025/05/02",
"position": 18,
"query": "automation job displacement"
},
{
"date": "2025/05/02",
"position": 91,
"query": "robotics job displacement"
},
{
"date": "2025/05/02",
"position": 67,
"query": "machine learning workforce"
},
{
"date": "2025/05/02",
"position": 6,
"query": "reskilling AI automation"
},
{
"date": "2025/05/02",
"position": 77,
"query": "robotics job displacement"
}
] |
|
8 Job Roles in Machine Learning - Wake Forest University
|
8 Job Roles in Machine Learning
|
https://business.wfu.edu
|
[] |
According to O*Net Online, those jobs are expected to grow 15% or more from 2020 to 2030, much faster than the average for all jobs. The career ...
|
Published 06/29/2022
Artificial intelligence (AI) and machine learning job titles vary wildly, and it’s easy to be overwhelmed by the many roles of AI experts, data scientists, and machine learning engineers. Many job titles refer to AI, data or machine learning, such as AI data analyst, AI engineer, AI research scientist, data scientist, and ML engineer.
Because of the wide range of titles, it’s hard to pin down information on growth opportunities. For example, the future looks bright for data scientists. According to O*Net Online, those jobs are expected to grow 15% or more from 2020 to 2030, much faster than the average for all jobs. The career outlook for computer information and research scientists is even rosier, predicted to grow 22% from 2020 to 2030, according to the U.S. Bureau of Labor Statistics.
We talked with Jeffrey Camm, Associate Dean of Business Analytics and the Inmar Presidential Chair of Analytics, and Tonya Etchison Balan, Associate Teaching Professor, both from the School of Business at Wake Forest University, about the job titles you might find in the machine learning industry.
8 Typical Job Titles in Machine Learning
1. Artificial Intelligence Engineer
Artificial intelligence is considered an emerging field, continuing to grow in the last several years, according to LinkedIn’s 2022 Emerging Jobs Report.
An artificial intelligence engineer works with traditional machine learning techniques like neural networks and natural language processing. They build models that power applications based on AI.
“Two of the most important technical skills for an AI engineer to master are programming and higher-level math such as statistics,” said Camm. “A good grasp of soft skills is also important, such as creativity, communication, an understanding of business, and an ability to build prototypes.”
2. Big Data Engineer
“Big data” is the growing amount of large, diverse sets of information that is being compiled at ever-increasing rates. According to the International Data Corporation, 163 zettabytes of data will be stored across the globe by 2025 (one zettabyte is equal to one trillion gigabytes). That is 10 times the amount of data generated in 2016 alone. This data will open up new user experiences and a world of business opportunities.
Big data engineers interact with that information in large-scale computing environments. They mine it to find relevant sets for analysis, which organizations then use to predict behavior and make other adjustments.
“Nearly every department in a company can use big data,” Balan says. “However, so much data is coming in that knowing how to use it can cause problems. That’s why a good big data engineer must have problem-solving skills along with database and data integration knowledge.”
3. Computer and Information Research Scientist
As noted earlier, the future is bright for those pursuing computer and information research careers. It’s not only data gathering that’s driving this growth. The BLS says more computer scientists will be needed to strengthen cybersecurity, finding innovative ways to prevent cyberattacks.
These scientists may use data to design new technological solutions for businesses, as well as finding and developing innovative uses for existing technology. Robotics and programming, as well as algorithms and cloud computing, may be part of the job for computer and information research scientists.
“Computer and information research scientists turn ideas into technology,” says Camm. “As demand for new and better technology grows, demand for computer scientists is likely to grow, too.”
4. Data Analyst
In a 2018 study, the World Economic Forum projected that 85% of companies would adopt big data and analytics by 2022. That indicates a big need for people who can analyze all this data.
Data analysts interpret data, gather information from various sources, and turn it into actionable insights which can offer ways to improve businesses and organizations. Data analysts can work in finance, healthcare, marketing, retail, and many other fields.
“Skilled data analysts are some of the most sought-after professionals in business,” Balan says. “Because the demand is so strong, and the supply of people who can truly do this job well is so limited, data analysts are really in the driver’s seat with respect to their careers.”
5. Data Engineer
Data engineers are generalists with advanced software development skills and expertise in databases. They typically create code, work on datasets, and implement requests from other data professionals, such as data scientists.
This IT role, which DICE’s 2020 Tech Jobs Report says is the fastest-growing job in technology year over year, requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages.
“This role is different from data analysts in their use of the data,” says Camm. “Data engineers do not typically have any role in analyzing data, but their purpose is to make data ready for internal use.”
6. Data Scientist
Data scientists, as with data engineers, are looking at a bright future due to the ever-growing use of big data. In fact, the U.S. Department of Labor predicts a 31% growth in employment between 2020 and 2030, a result much higher than the general job market.
Data scientists develop and implement techniques or analytics applications to turn raw data into meaningful information. They apply data mining, modeling, language processing, and machine learning to pull this data with programming languages and visualization software.
“A data scientist should have a strong foundation in computer science and programming,” Balan says. “It’s not all numbers, though. Data scientists must have excellent interpersonal skills to collaborate with colleagues and communicate their findings.”
7. Research Scientist/Applied Research Scientist
Research scientists take promising data leads uncovered by data scientists and build on them, or experiment with other approaches. They are experts at framing experiments, developing hypotheses, and getting results.
Applied research scientists take this data and help pursue industrial applications of their findings. They are experts at using this new knowledge and implementing solutions at scale.
Research scientists, along with computer scientists, are expected to have job growth of 22% from 2020 to 2030, much faster than the average, according to the BLS. The largest employers of computer and information research scientists in 2019 were:
Federal government (excluding postal service)
Computer systems design and related services
Research and development in the physical, engineering, and life sciences
Software publishers
Colleges, universities, and professional schools (state, local, and private)
8. Machine Learning Engineer
A 2020 report from Robert Half says 30% of U.S. managers are using AI and machine learning, and that 53% intend to begin within the next five years. This growth bodes well for machine learning engineers.
Machine learning engineers build programs that control computers and robots. They develop algorithms to help a machine find patterns in its own programming data. The machine eventually is able to teach itself to understand commands and then “think” for itself.
“A machine learning engineer is expected to master the software tools that make these models usable,” Balan says.
How to Navigate AI and Machine Learning Job Titles
As discussed earlier, there are a variety of machine learning job opportunities with a mix of job titles. These can confuse their intent and make it hard to find the right position. Here are two things you can do when looking at job titles to make a search easier:
1. Look at the Title
Decide whether the title refers to data, artificial intelligence, or machine learning—look for “AI,” “ML,” and the like. Notice whether the title says architect, developer, engineer, researcher, or scientist. A third indicator may be the seniority level, such as “junior,” “senior,” or “chief.” This will help you sort out where you fit in.
“Titles are important, but they can still leave the intent of the job unclear,” Camm says. “That’s why you really need to find out what the job entails.”
2. Look at the Description
The job description in the end is more informative than the title. This will usually tell you whether you’ll be expected to apply tools, build real applications, design systems, or develop novel methods.
“The description will tell better what’s really involved in the job and what’s expected of you,” Balan says. “If you’re uncertain about where you’ll fit in even after reading the description, be sure to ask. Get clarification and figure out how you’ll work in the position.”
Pursue Your MSBA Online
Successful organizations in nearly every industry rely on professionals who can make data-driven decisions. These experts inspire innovation, improve efficiencies, and influence teams and organizations.
You can become an in-demand analytics expert with the Wake Forest online business analytics master’s degree. Empower yourself with the analytical, technology, and management skills to become a leader of tomorrow. Contact us today for more information.
| 2022-06-29T00:00:00 |
https://business.wfu.edu/masters-in-business-analytics/articles/machine-learning-job-roles/
|
[
{
"date": "2022/06/29",
"position": 74,
"query": "machine learning job market"
},
{
"date": "2022/06/29",
"position": 64,
"query": "machine learning job market"
},
{
"date": "2022/06/29",
"position": 74,
"query": "machine learning job market"
},
{
"date": "2022/06/29",
"position": 75,
"query": "machine learning job market"
},
{
"date": "2022/06/29",
"position": 75,
"query": "machine learning job market"
}
] |
|
Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 ...
|
Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond
|
https://link.springer.com
|
[
"Li",
"Lli Odu.Edu",
"Old Dominion University",
"Norfolk",
"Va",
"Ling Li",
"Search Author On",
"Author Information",
"Corresponding Author",
"Correspondence To"
] |
... automation that transforms jobs (Whiting, 2020). Elementary and ... Artificial Intelligence (AI). Since 2000, particularly after 2015 ...
|
Industry 4.0 is revolutionizing manufacturing processes and has a powerful impact on globalization by changing the workforce and increasing access to new skills and knowledge. World Economic Forum estimates that, by 2025, 50% of all employees will need reskilling due to adopting new technology. Five years from now, over two-thirds of skills considered important in today’s job requirements will change. A third of the essential skills in 2025 will consist of technology competencies not yet regarded as crucial to today's job requirements. In this study, we focus our discussion on the reskilling and upskilling of the future-ready workforce in the era of Industry 4.0 and beyond. We have delineated top skills sought by the industry to realize Industry 4.0 and presented a blueprint as a reference for people to learn and acquire new skills and knowledge. The findings of the study suggest that life-long learning should be part of an organization’s strategic goals. Both individuals and companies need to commit to reskilling and upskilling and make career development an essential phase of the future workforce. Great efforts should be taken to make these learning opportunities, such as reskilling and upskilling, accessible, available, and affordable to the workforce. This paper provides a unique perspective regarding a future-ready learning society as an essential integral of the vision of Industry 4.0.
| 2024-10-14T00:00:00 |
2024/10/14
|
https://link.springer.com/article/10.1007/s10796-022-10308-y
|
[
{
"date": "2022/07/13",
"position": 46,
"query": "reskilling AI automation"
},
{
"date": "2022/07/13",
"position": 46,
"query": "reskilling AI automation"
},
{
"date": "2022/07/13",
"position": 46,
"query": "reskilling AI automation"
},
{
"date": "2022/07/13",
"position": 46,
"query": "reskilling AI automation"
},
{
"date": "2022/07/13",
"position": 45,
"query": "reskilling AI automation"
},
{
"date": "2022/07/13",
"position": 47,
"query": "reskilling AI automation"
},
{
"date": "2022/07/13",
"position": 43,
"query": "reskilling AI automation"
},
{
"date": "2022/07/13",
"position": 47,
"query": "reskilling AI automation"
}
] |
Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 ...
|
Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Ling Li",
"Old Dominion University",
"Norfolk",
"Va Usa"
] |
... automation that transforms jobs (Whiting, 2020). Elementary and ... Artificial Intelligence (AI). Since 2000, particularly after 2015 ...
|
Industry 4.0 is revolutionizing manufacturing processes and has a powerful impact on globalization by changing the workforce and increasing access to new skills and knowledge. World Economic Forum estimates that, by 2025, 50% of all employees will need reskilling due to adopting new technology. Five years from now, over two-thirds of skills considered important in today’s job requirements will change. A third of the essential skills in 2025 will consist of technology competencies not yet regarded as crucial to today's job requirements. In this study, we focus our discussion on the reskilling and upskilling of the future-ready workforce in the era of Industry 4.0 and beyond. We have delineated top skills sought by the industry to realize Industry 4.0 and presented a blueprint as a reference for people to learn and acquire new skills and knowledge. The findings of the study suggest that life-long learning should be part of an organization’s strategic goals. Both individuals and companies need to commit to reskilling and upskilling and make career development an essential phase of the future workforce. Great efforts should be taken to make these learning opportunities, such as reskilling and upskilling, accessible, available, and affordable to the workforce. This paper provides a unique perspective regarding a future-ready learning society as an essential integral of the vision of Industry 4.0.
While many educational organizations and individuals might still wonder how Industry 4.0 could affect the education system, some are implementing changes today and preparing for a future when artificial intelligence (AI) and cyber-physical systems can connect their business globally. In this study, we focus our discussion on the reskilling and upskilling of the future-ready workforce in the age of Industry 4.0 and beyond. The following sections cover several key elements that contribute to training a future-ready workforce. Section 2 provides background information about the top skills needed for Industry 4.0. Section 3 discusses reskilling and upskilling of the workforce in different parts of the world. In Section 4 , a life-long learning framework offers opportunities to reskill and upskill a future workforce. Finally, Section Five provides conclusions.
Life-long learning for all is becoming a reality. New skills and technologies have been introduced much faster than a decade ago. Respondents to the Future of Jobs Survey estimate that around 40% of workers will require reskilling for a length of six months. Half of the workforce will need to reskill in the next five years, as the double-disruption of the economic impacts of the COVID-19 pandemic and increasing automation that transforms jobs (Whiting, 2020 ). Elementary and middle school education at a young age remains mandatory and fundamental, and is the first phase of life-long learning. An upward trend of increasing job complexity has been observed during the progression of industrial revolutions. Learning throughout life, including at an older age, makes the difference in the higher education domain in the twenty-first century. However, the gap in life-long learning exists among individuals. European Commission ( 2020 ) estimated that less than two in five adults participate in learning every year in the EU, which is not enough to support the needs of Industry 4.0 and beyond. All of us should embrace the opportunities to upskill and reskill our professional skill sets and contribute to the economic development of the 21 century.
By giving all people opportunities to develop the skills they will need to participate fully in the future workplace, we ought to create more inclusive and sustainable economies and societies where no one is left behind. Industry 4.0 is about creating a unique life-long education system that ensures a future-ready workforce. Universities with a tradition of educating and training the world's most competent designers, engineers, technology specialists, consultants, operations professionals, and data analysts are in an exciting era to tackle these challenges quickly and collaboratively.
The World Economic Forum projected in its Future of Jobs Report 2020 that half of all employees worldwide would need reskilling by 2025 (Schwab & Zahidi, 2020 ). This estimation does not include all the people currently not in employment. Before COVID-19, the rise of automation and new technologies transformed the world of work, resulting in an urgent need for large-scale upskilling and reskilling. Now this need has become even more critical. In a 2016 World Economic Forum report, experts projected that 65% of children entering primary school today would ultimately work in completely new job types that do not exist today (Schwab & Samans, 2016 ). Developing new and diverse education programs and promoting innovative curricula are some of the STEM program's primary goals that provide skills, knowledge, and attitudes needed for an entrepreneurial culture (Li, 2020 ).
Industry 4.0 (I4.0) is in the process of revolutionizing manufacturing and engineering all over the world. I4.0 is a virtual reality fusion system based on traditional manufacturing and transformed with cyber-physical systems, the Internet, the Internet of Things (IoT), and Industrial Internet of Things (IIoT), artificial intelligence, machine learning, hyper-converged infrastructure, deep learning, virtualization, and more to create an intelligent production system (Li, 2018 , 2020 ; Xu et al., 2018 ; Li & Zhou, 2020 ; Xu et al., 2014 ). Workforce, capital, and technology are the three major components that significantly contributed to the evolution of the past three industrial revolutions. Therefore, it is time to look at the talent required to realize the vision of Industry 4.0 and beyond.
The rapid growth in high-tech export, patent application, and receipts for letting others use intellectual property show that it is essential for society to emphasize workforce skilling, reskilling, and upskilling because technology innovation and workforce’s knowledge and skill level go hand in hand.
Tables 6 and 7 show payments for using intellectual property and receipts for letting other companies use its innovation. The authorized use of proprietary rights (such as patents, trademarks, copyrights, industrial processes, and designs, including trade secrets and franchises) are granted through licensing agreements. Companies in the EU and the US have collected the most receipts by letting other companies use their intellectual properties.
We further analyzed the number of pattern applications, payments of use of intellectual property, and the receipts of use of the intellectual property. These metrics show the growth of high-tech innovation and the global sharing of technology innovation. An upward trajectory of pattern applications is shown in Tables 4 and 5 . A product or process that provides a new way of doing things or offers a new technical solution to a problem will be eligible for patent application (the World bank). Patent applications are generally filed through the Patent Cooperation Treaty procedure or with a national patent office by a resident of a country or a non-resident of the country. In ten years, from 2010 to 2019, the number of patent applications filed by the residents in East Asia & Pacific countries and China increased steadily at a rate of greater than 300%. On the other hand, the patent filing rate by residents in EU countries and the United States remained stable; and it did not show an upward trajectory as observed in East Asia & Pacific countries, and China.
In 2017, 2018, and 2019, 30% of the manufacturing exports of East Asian and Pacific countries and China are high-tech products (Table 3 ). At the same time, high-tech exports accounted for about 15 to 20% of the total exports in the U.S. and European Union countries. On the other hand, Sub-Saharan African countries exported significantly fewer high-tech products than East Asian and Pacific countries. Therefore, there is an excellent opportunity for African and Sub-Saharan African countries to catch up with the wave of Industry 4.0. Education, training, and skill re-tooling will play an essential role in enabling African countries to catch up with the high-tech wave in the era of Industry 4.0.
As the speed at which emerging technology evolves, manufacturing companies have increased the rate at which they design, develop, and export high-tech products. Therefore, it becomes essential that manufacturing and supply chain managers support the development of workforce capabilities (Doherty & Stephens, 2021 ). To better picture technology advancement and the need for workforce skill re-tooling, we present the trend of digitalization using the metrics of high-tech in manufacturing export and patent applications from the World Bank. Table 3 shows high-tech exports as a percentage of manufacturing exports. The World Back defines high-tech exports as “products with high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery.”
As the demand for renewable energy continues to increase, the industry is looking to recruit high-caliber candidates to drive the green energy business forward. According to the U.S. Department of Energy, the solar workforce increased by 25% in 2016, while wind employment increased by 32% (Mellett & Finnell, 2021 ). Although this may seem obvious, work in the energy sector requires a range of technical skills to excel as an employee and eventually as a leader to lead teams and projects. In addition, employees in the renewable energy sectors need an excellent grasp of scientific principles and concepts to make good decisions based on facts and factual data rather than opinion or perception.
A clean energy plan is an essential integral part of Industry 4.0, underscored by global leaders, energy sector administrators, and prominent corporate executives. According to the US Department of Energy, 5 “the clean energy industry generates hundreds of billions in economic activity and is expected to grow rapidly in the coming years.” As a result, there is a tremendous economic opportunity to develop green energy, including solar, wind, water, nuclear, geothermal, bioenergy, and more. Moving forward, the world will continue to drive strategic investments in the transition to a cleaner and more secure energy future.
In 2020, the US government decided to collect data on the 330 million people living in the country while keeping their identities private. The data is released in statistical tables that policymakers and academics can utilize when writing legislative documents or conducting research. By law, the Census Bureau must ensure that it can’t lead back to any individuals (Temple, 2020 ); so the census data scientists added some “noise” into the data. For example, it might change a resident’s age or race to hide their identity. Differential privacy is a mathematical technique that makes this process rigorous by measuring the degree of privacy increases when added noise. Apple and Facebook already use the method to collect aggregate data without identifying particular users (Temple, 2020 ). The US Bureau of Labor Statistics estimates a 20% job increase in information security analysts by 2026. 4
In a digital era, technologies, such as computer systems, the Internet, and smart devices, play a fundamental role in everyday life. However, while we enjoy the convenience and efficiency provided by the new technologies, we face new risks and threats caused by using technology. In recent years, businesses in all industries and of all sizes have experienced the increased frequency, volume, and sophistication of cyber-attacks (Lu & Xu, 2018 ). For example, on May 7, 2021, an American oil supply system, Colonial Pipeline, suffered a ransomware cyberattack that impacted the computerized equipment that operates the pipeline.
Digital skills such as coding skills, data analytics, human–machine interaction, and understanding of information technology were regarded as basic skills because they will be required in the manufacturing industry by employees. The vast amount of data is not helpful unless we have human insights to make sense of it. We will need many more data scientists to write algorithms and build AI to help us make predictions and reasonable decisions based on the data and facts. The U.S. Bureau of Labor Statistics estimates 19% job growth for computer and information research specialists by 2026. 3
By 2025, nearly 30 percent of the data will be of the "real-time" variety. 2 Real-time data refers to data gathered from customer insights or enterprise hardware and software as the gears of industry turn, rather than after the fact. As information technology and operational technology converge, companies begin to find new ways to connect. Data collected from suppliers, customers, and enterprises can be aligned with detailed production information and be fine-tuned in real-time. The digital and physical worlds have become irrevocably linked with machines, systems, and people, which are able to exchange information automatically.
The Industrial IoT extends Information Technology (IT) to Operational Technology (OT), adding intelligence to manufacturing equipment, processes, and management (Umar, 2005 ; Ustek-Spilda et al., 2021 ). The Industrial IoT (IIoT) refers to interconnected sensors, instruments, and other devices networked with industrial applications that enable data collection, exchange, and analysis (Sigov et al., 2022 ). The IIoT is an evolution of a distributed control system (SDS) that allows for a higher degree of automation by using cloud computing to refine process controls.
Industry 4.0 has dramatically impacted the number of networking professionals in manufacturing and other critical sectors. Some examples of Industrial IoT (IIoT) and networking technologies are intelligent factories, connected fabrication and material handling equipment, remote sensors for freight condition monitoring and inspection, automated infrastructure and smart metering for utility management and energy-saving efforts, and tracking systems for vehicles and other assets. Facing the interconnected world, all businesses will need many more networking and IoT specialists than they currently employ. For example, the American Bureau of Labor Statistics expects the U.S. to add more than 15,000 jobs in network and computer systems administrators 1 by 2029. This is an example of the many disciplines required in this exciting field.
5G is a generation of cellular networks designed to enhance the efficiency of data transmission. 5G networks provide higher data rates, lower latency, massive device connectivity, higher capacities, better consistent service quality, and lower cost than 4G networks (Sigov et al., 2022 ). However, 5G is insufficient for IoT devices to exchange various data types in real-time. 6G as the next generation of 5G is at the corner. 6G will exhibit more heterogeneity than 5G and support applications far beyond anything seen. 6G will connect everything, provide full dimensional wireless coverage, and integrate all functions, including sensing, communication, computing, caching, control, positioning, radar, navigation, and imaging, to support full-vertical applications.
Current computing technologies have limitations due to the restriction of bits of 0 and 1. The computation must be done with bits in storing or processing data. Quantum information technology is a new paradigm that can process more than digital data consisting of 0 and 1 (Sigov et al., 2022 ). If quantum technology is applied to Information and Communications Technology (ICT), it will enable rapid computational processing and un-hackable internet systems. It is expected that the next generation of ICT will overcome the limitations of existing digital computers (Sigov et al., 2022 ). An internet based on quantum physics promises inherently secure communication. In 2020, a research team headed by Stephanie Wehner at Delft University of Technology built a network connecting four cities in the Netherlands entirely through quantum technology. Messages sent over this network would be unhackable (Temple, 2020 ). A team in China used the technology to construct a 2,000-km network backbone between Beijing and Shanghai. Google has provided the first clear proof of a quantum computer outperforming a classical one, although a full-scale quantum computer has not yet been developed.
Quantum Computing is a disruptive technology that tries to understand the processing and transmission of information using quantum mechanics principles. It integrates quantum effects in physics into the study of Information and Communication Technology (ICT), including theoretical issues in computational models and experimental topics in quantum physics. As a result, quantum technologies are anticipated to create a massive paradigm shift in how Industry 4.0 operates, which incorporates the digital revolution into the physical world and provides new directions in artificial intelligence and nanotechnology (Kim, 2017 ).
From data collection to organizational architecture design, the AI development strategy and AI project prioritization are as complex as the technology itself. To successfully leverage the benefits of AI applications, researchers and industry experts need to build more powerful algorithms, use more significant amounts of data and computing power, and rely on centralized cloud services.
Since 2000, particularly after 2015, the development and utilization of artificial intelligence (AI) have escalated following the rapid growth of sensors and computer chips, the evolution of algorithms, and big data support. AI has been recognized as a strategic information technology innovation tool to improve companies’ competitiveness. AI technologies, such as natural language processing, machine learning, and deep learning, bring sophisticated data analysis capabilities to applications across various industries (Chen et al., 2021 ). For example, AT&T investigates how to use AI algorithms to enable drones to check and repair base stations. SK Telecom in South Korea applied machine learning to analyze network traffic to detect anomalies and strengthen network operations (Chen et al., 2021 ). Although some AI initiatives have been adopted in leading technology companies, many applications of AI are still at their conceptual stage. As a result, they have not generated much commercial value, particularly in network management and predictive maintenance applications.
In the next ten years, both manufacturing and service firms will have to adapt to or adopt Industry 4.0 principles and technologies to survive the competition. The vast majority of business leaders (94%) now expect employees to pick up new skills on the job (Whiting 2021). They believe that investing in the right people and the right skillsets today ensures a favorable position well into the future. Based on the literature, we discuss seven vital disruptive technologies that require significant skill upgrade for a future-ready workforce. These technology groups are far from comprehensive, but they can serve as a guideline for organizations to formulate their technology portfolio and invest in reskilling and upskilling their employees and staff.
The advancement of disruptive technology accelerates the reskilling requirements. The global supply chain, for example, has already experienced a great deal of change in the past five years. Online shopping, e-commerce, automated warehouse operations, and digitized seaport shipping information exchange are a few examples. Disruptive technologies are opening up new possibilities for society, providing innovative technology applications, novel materials, and processes to create products and services that until recently were unimaginable. As a result, those working in the manufacturing and service sectors will need new skills. Mobile internet, cloud technology, and artificial intelligence are already impacting how we work. While quantum computing and 6G are still in their early stages of use, the pace of change will be fast. Table 2 lists seven disruptive technologies that play an important role in transforming our society in a digital era.
Similarly, soft skills in the cognitive scope, such as quality control and active listening, and emotional intelligence, considered core skills on the 2015 skill list, disappeared entirely from the top 10 skill list of 2025. Instead, this year's newly emerging items are skills in self-management such as active learning, resilience, stress tolerance, and flexibility.
Negotiation and people management were ranked high on the 2015 skill list. However, these skills began to drop on the 2020 list and do not appear on the 2025 list. As companies and managers increasingly use masses of data and make decisions based on data analytics, negotiation and people management retreat their positions in the decision-making process. Society expects artificial intelligence and machine learning to provide decision support information to a company’s board of directors by 2026.
Items six to 10 under 2025 (Table 1 ) are newly emerging skills focusing on technology-related competencies and skills, cognitive reasoning capability, and leadership, with a sharp uptake from 2020. Five years from now, over two-thirds of skills (67%) considered important in today’s job requirements will change. In addition, a third of the essential skill sets in 2025 will consist of technology competencies not yet regarded as crucial to today's job requirements.
Critical thinking and problem-solving skills, which were at the top of the skill list in 2020 and 2015, are now relegated to 3 rd and 4 th places in 2025’s skill list (Table 1 ). But these two skills, along with creativity, have consistently been viewed as critical skill sets since the first report was published in 2016. With the avalanche of new technologies, new products, and new working processes, employees will become more creative to respond to and benefit from technological changes.
Looking forward to 2025 and beyond (Table 1 ), analytical thinking and innovation skills crown the skill-set list that employers believe will grow in prominence in the next five years. Active learning and learning strategies are a new skill set that trailed behind the top one. Analytical thinking and active learning ranked number 1 and number 2 in 2025, emphasizing cognitive self-management.
For those workers who stay in their roles, the share of core skills that will change from 2020 to 2025 is more than 60% (Table 1 ). Seven out of 10 top skills listed under the column “in 2025” are not listed under 2020 and 2015. While between 2015 and 2020, skill requirements overlap considerably, eight out of ten top skills are the same for the two periods (Table 1 ).
Table 1 shows the top 10 skills for 2015, 2020, and 2025 (Gray, 2016 ; Whiting, 2020 ). The top 10 skills for 2015 are listed under Column 1 on the right-hand side of Table 1 , and the top 10 skills for 2020 are listed under Column 2 on the right-hand side. The middle column, column 3, compares the change of rank of the top skills in 2015 and 2020. For example, complex problem solving is ranked number 1 in 2015 and 2020, while critical thinking is moved up to number two in 2020 from its rank of number four in 2015. The first column from the left-hand side shows the changes in top skills in 2015, 2020, and 2025. For example, “Analytical thinking and innovation” is listed as the top 1 skill but was not on the list in 2015, neither 2020. “Complex problem-solving” is the third most important skill in the 2025 list but was ranked number 1 in 2015 and 2020.
The World Economic Forum has published several reports on the future of jobs and top skills that will play significant roles in future technology advancement (Schwab & Samans, 2016 ; Schwab & Zahidi, 2020 ). The authors summarized the perspectives of strategy officers and chief human resources managers from leading global companies about the current shifts in required skills, and recruitment across industries. These reports analyze skills needed for the labor market and track the pace of changes. A quick rate of technology adoption signals that in-demand skills across jobs will change over the next five years or longer; therefore, skill gaps will continue to be significant.
Industry 4.0 is a significant transformation to the digitization of manufacturing and the creation of a cyber-physical system. I4.0 connects production and process technologies, integrates vertical and horizontal value chains, and digitalizes product and service offerings to pave the way for new production and economic value chains. This transition has an enormous impact on higher education which has a role of training talents, leading scientific innovation, disseminating knowledge, as well as preparing a future-ready workforce.
In summary, in the Industry 4.0 era, a transformation of education and skill-development systems appears necessary to all industries. Around the world, work is changing as digitization and automation spread. As a result, millions of people will need to update and refresh their skills, and some will change occupations (Garbellano & Veiga, 2019 ). An estimated one-third of the global occupational transitions will happen in the twenty-first century. A few practices and models we discussed above, such as the Japanese automakers’ implementation of on-the-job training in Central and Eastern Europe, Mexico’s on-the-job training experiments, the Nordic countries’ learning-rich forms of work organization, and China’s new priority of workforce skilling and reskilling, could offer a helpful reference point to all.
In general, China has an education system that serves its industrial economy effectively. However, the gap exists in reskilling and upskilling its future workforce and training life-long learners (Wu & Ye, 2018 ). In August 2021, A draft revision of the Science and Technology Progress Law was submitted to the Standing Committee of China’s 13th National People's Congress for deliberation. The draft stipulates focusing on major national strategic tasks, promoting core technology research, and achieving self-reliance on core technology. Thus, China will focus on workforce skilling, reskilling, and maintaining a sustainable talent pool.
China is rebalancing its economic structuring by moving toward high value-add innovative industries, such as robotics, AI, and semiconductor products. However, some employees are not able to keep up with the change. A recent study on the first job insights (Li et al., 2018 ) indicated that the average time in the first job for the generation born in the 1990s in China was 19 months; the employees who were born in the 1980s spent 43 months in their 1 st jobs, and those who were born in the 1970s stayed on their 1 st job for 51 months. The average time on the 1 st job has decreased exponentially over the past three decades. However, many Chinese employers lack comprehensive training programs. At the same time, some Chinese companies regard reskilling and upskilling as an expense rather than an investment in their human resources.
China is modernizing and digitizing its industry like the rest of the world and is now turning its attention to ensure that its workforce will have the skills and knowledge needed for the next phase of the country’s economic journey, especially in the high-tech area. Thus, reskilling, upskilling, and vocational training are urgent tasks to transform China’s workforce into lifelong learners. Currently, finding employment after graduation from a technical or vocational school is not always straightforward in China (Woetzel et al., 2021 ). Germany sets a good example for China and other countries. The German education system integrates vocational schooling with industry needs. Students of German vocational programs “find it relatively easy to be recruited by companies in their skill area, and they have comparable job satisfaction and career trajectory levels with their counterparts who pursued an academic path” (Woetzel et al., 2021 ).
China has gone through 40 years of economic reform and is undergoing another significant economic undertaking of domestic-led consumption, services, and innovation. Since 1978, China’s economic development has evolved from an export-led economy to a global manufacturing hub and an investment-led economy. In response to the recent sanctions from the U.S. government on exporting high-tech products to China, China has prioritized science and technology development to focus more on self-made critical technologies. This new economic development goal has compelled China to examine its industrial policies and strategies over the past 40 years and formulate its investment in an Industry 4.0 economy.
In contrast to Norway, the UK’s manufacturers are on a low level of automation. When reflecting on the industry’s readiness for Industry 4.0, interviewees stated that some manufacturers in the UK had not done industry 3.0 yet. However, the researchers in the university robotics centers and funding bodies noted that substantial resources had been invested in developing technologies for health and social care, such as robotic surgical tools and interactive assistive robots to support independent living (Lloyd & Payne, 2019 ).
Current debates in the developed countries around advanced technologies such as robotics and artificial intelligence are dominated by concerns over the threat to employment amid widely varying estimates of potential job losses (Lloyd & Payne, 2019 ). The published literature covers a range of perspectives regarding narrow and broad approaches to innovation. The former approach highlights scientific and technological innovation and the links between publicly funded R&D institutions and firms (Edquist, 1997 ). The latter focuses on the role of employees’ learning by doing and interacting inside organizations to support or drive incremental innovation (Lundvall, 2016 ). The Nordic countries implement learning-rich forms of industrial organizations that are linked to education systems, strong vocational training, and collective regulation of the labor market (Arundel et al., 2007 ; Lloyd & Payne, 2019 ).
The developed countries have made a substantial investment in advanced technology in the Industry 4.0 era. In a study of analyzing the similarities and differences of supports for the development and diffusion of robotics and AI in the United Kingdom (UK) and Norway, Lloyd and Payne ( 2019 ) considered country effects by exploring the role of institutions and social actors in shaping technological change in the two countries. Drawing upon interviews with technology experts, employer associations, and trade unions, they examined public policies support for the development and diffusion of robotics and AI, along with potential consequences for employment, work, and skills. Consequently, both UK and Norway have provided more funding for R&D, including increased resources for universities and research institutes to train, upskill, and reskill the future workforce.
Continuing with the current advancement of technology could mean a significant displacement of qualified and unskilled jobs, which could increase the unemployment rate (Santiago, 2020 ). To adopt and manage Industry 4 technologies, workers will need a high content of knowledge and creativity. On the other hand, employees who have a less-technology-intensive job are at risk of being replaced by an imminent development of AI. Therefore, reskilling and upskilling the workforce is an urgent priority for Mexico to keep up with the pace of digitalization.
As a neighboring country of the U.S., Mexico is a favorable location for American manufacturers to expand their facilities because of the low cost of labor and proximity. Mexico is one of the largest auto manufacturers and auto parts exporters. Yet, many manufacturers in Mexico still use legacy systems that run production with data siloes or cumbersome processes, which contribute to delays, outdated information, and lower productivity. The reality is that many manufacturing companies in Mexico are behind with technology (Lara, 2019 ). In addition, the idea of digitalizing manufacturing is not as mature in Mexico as it is in the U.S., Europe, and China. In order to set the path for Industry 4.0 and the Industry Internet of Things, Mexican industry managers have begun to think about how to implement technology on the shop floor. Connecting equipment, machines, and sensors on the shop floor allows workers to observe how production performs and comprehend what is working and what needs to be improved.
After the falling down of the Berlin Wall, the former socialist economies have reconnected with the western European countries and the global economic systems. Investors have been attracted by the low-cost labor, the local government’s support, and cultural and geographic proximity to Western European markets (Olejniczak et al., 2020 ). Major automakers in the world have set up production facilities in the former Eastern-Bloc countries. Several Japanese carmakers built their capital-intensive automotive factories in the Visegrád Group, a cultural and political alliance of four Central European countries, the Czech Republic, Hungary, Poland, and Slovakia. The Japanese automakers introduced their unique management style to European workers. Paired with advanced automation in the form of Industry 4, Japanese managers embedded regular job rotation to develop a more flexible workforce that possesses multi-skills. They introduced quality circles and the kaizen system to workers at the factory level. The Japanese automakers successfully transferred the concept of on-the-job training and development of multi-skilled employees to their European subsidiaries, resulting in a completely new system in an Industry 4.0 production environment that is neither a copy of the original model nor a replica of existing local patterns.
Maisiri and Van Dyk ( 2021 ) explored Industry 4.0 skill needs in South Africa. Based on surveys with industry experts, they reported that the South African manufacturing industry consists of a significant percentage of the low-skilled workforce that deviated from the higher skill levels required in the Industry 4.0 era. The participants of their study pointed out that Industry 4.0 makes jobs more meaningful and interesting by enabling lower-skilled people to do higher-skilled jobs using technologies such as augmented reality and virtual reality. Industry 4.0 technologies enable employees who have been stuck in low-paying jobs and menial labor to be more relevant and perform higher functions in their companies (Maisiri & Van Dyk, 2021 ). Though the South African manufacturing industry has adopted Industry 4.0 principles and technologies and made a noticeable contribution to the country’s economy, its manufacturing industry is currently characterized by significant unskilled and semi-skilled workers. Thus, workforce reskilling and upskilling remain vital to the success of the country’s economic development.
The application of Industry 4.0 technologies has a significant impact on the developing countries in Africa, which is relatively weak in human capacity development. Adepoju and Aigbavboa ( 2021 ) provided insightful facts to support the need for reskilling and upskilling the workforce in Africa in the Industry 4.0 working environment. “Nigeria is a developing country with the largest economy and population in Africa. The Nigerian economy accounts for approximately 55% of the West African GDP, 35% of Sub-Saharan Africa's GDP, and one-fifth of the African population. As a result, the economy has been acknowledged as one of the fastest-growing economies in Africa. However, there is still a challenge of low human capital in Nigeria” (Adepoju and Aigbavboa, 2021 ). The most recent report on world human capital ranking shows that Nigeria, the largest economy in Africa, is ranked 152 out of 157 economies in the world (The World Bank, 2020 ). Therefore, it is obvious the next frontier for technology skill advancement will be in Africa.
Industry 4.0 has shifted manufacturing operations away from mechanical technologies and toward digitalization. Responding to the acceleration of digital transformation, industries worldwide have introduced advanced technologies to their production lines and processes. With increasing trade and communication, more and more companies extend their reach across continents and oceans. Today, goods are transported worldwide by container ships, trucks, air, and various transportation modes. Business activities, including material acquisition, production of goods, facilities management, professional services and maintenance, and logistics outsourcing, can all be part of international processes. Industry 4.0 is revolutionizing and digitizing businesses and has a powerful impact on globalization by changing the workforce and increasing the mobility of people around the World. The need for upskilling and reskilling the workforce is a global issue since international trading and outsourcing prevail in today’s economy (Li & Lu, 2021 ; Li, 2018 ; Xu, 2011 ). In the following section, we will discuss several sample cases regarding workforce training and skilling efforts in developing economies and developed countries.
A System Driven Blueprint for Reskilling and Upskilling the Future-ready Workforce
In the Industry 4.0 era, the world faces massive change and transformation. Rapid advances in industrialization and digitalization have spurred tremendous progress in developing the next generation of technologies, including AI and machine learning, quantum computing, 6G, IoT, IIoT, Big Data and business intelligence, cybersecurity, and green energy. Industry 4.0, which is different from the previous industrial revolutions, places a premium on human capital and intellectual resource for innovation.
In the twenty-first century, knowledge dissemination, learning, and education are more accessible, to more people, in more places, and in more ways than ever before in human history. We have observed and experienced an upward trend of technology innovation (Tables 3 and 4), increasing job complexity and technology integration during the progression of industrial revolutions. The success of Industry 4.0 depends not only on technology but also on people. A significant change in the competency requirements has been recorded in the global supply chain and manufacturing industry (Ahmad, 2019). The vision of advanced manufacturing will be realized through the effort of a future-ready workforce (Li, 2020).
The several studies that we cited in Section 3 of this article ventured into examining the subject of Industry 4.0 skills in South Africa, Mexico, Central Europe, and other countries. The conclusions of these studies result in a broad consensus that the onset of intelligent software systems, AI, and machine learning will not lead to mass unemployment. Instead, the likelihood is that many job functions will be downgraded or even disappear, while training, retraining, re-skilling, and upskilling will be necessary to prepare today’s students and workforce to be more creative to respond to the call of Industry 4.0 (Ahmad, 2019; Li, 2020; Schwab & Zahidi, 2020).
As technology evolves, some people are not able to get good jobs due to a lack of the right skills, while others are afraid of low-skilled jobs being threatened by automation. As a result, skill gaps are inevitably increasing unless today’s workers, who are at most risk of losing their jobs, learn new technology and take the opportunity to acquire the skills required for future employment. While certain higher-skilled workers have seen their pay increase, many others have seen median wages stagnate, and their job security becomes more precarious (Moritz & Zahidi, 2021). Indeed, by focusing on scalable reskilling and upskilling, people would be fully equipped to participate in economic development, reducing inequality and leading to better social stability (Moritz & Zahidi, 2021).
Scenario for Skilling and Upskilling The latest Future of Jobs report by the World Economic Forum (Schwab & Zahidi, 2020) estimated that by 2025, 85 million jobs might be displaced by a shift in the division of labor between humans and machines, while 97 million new jobs that do not exist today may emerge. These new jobs are more adapted to the new division of labor between humans, machines, and algorithms (Schwab & Zahidi, 2020). The top skills which will rise in the lead include analytical thinking and innovation, active learning, critical thinking, complex problem-solving capability, and skills in self-management such as stress tolerance and flexibility. In their report, Schwab and Zahidi (2020) stated that 84% of employers would engage in digitalized working processes, including a significant expansion of remote work. Therefore, those currently unemployed should emphasize learning digital skills such as big data analytics, cybersecurity, and information technology.
Defining Reskilling and Upskilling through College Education As the world is experiencing digital transformation in Industry 4.0, we are experiencing a paradigm shift that has profound implications for the workforce and will affect strategy, talent, innovation, and business models. The 21st-century workforce is committed to 21st-century technologies and skills (Li, 2020). To advance their work skills, the future-ready workforce will take upskilling and reskilling continuously as they advance their career and secure their employment. Upskilling means that employees gain new skills to help in their current job responsibility. For example, an accountant, who used to use an abacus for accounting and computing, learns digital spreadsheets to balance the company’s balance sheet. On the other hand, reskilling means employees need the knowledge and skills to take on different or entirely new roles. For example, the switchboard operator position disappeared after the cell phone became a primary communication device. As a result, those operators will need to reskill to take on a new career.
Which Industrial Sectors Need the Most Reskilling and Upskilling? The new digitalization revolution will profoundly impact employment in the coming years. Nearly every job will change, and the overwhelming majority of today’s employees will need to learn new skills. Ellingrud, Gupta, and Salguero from McKinsey and Company (2020) estimated that 39 to 58 percent of work activities in operationally labor-intensive sectors could be automated due to these tasks' predictable and repetitive nature. To assume a new role, workers in traditionally labor-intensive sectors, such as manufacturing, food service, retail, agriculture, mining, etc., will need reskilling. Some senior workers, whose skills were valued when they started their careers, have been left behind by the demand for new skill requirements. The workers in the labor-intensive sectors may need more reskilling than those with higher education training. There are increasing new job opportunities, but to take on new jobs, one needs to have the skills and knowledge that industries seek. To a certain degree, the advancement of technology has attributed to job polarization. Skill-biased technical change in recent decades may have benefited skilled workers more than unskilled workers. Some tasks are easily substitutable, easily codifiable, and can be easily automated; others are not. Therefore, while the relative supply of more skilled workers has increased since the mid-1980s, the demand for skilled labor increased even more because of technological change. Information technology alone can explain between 60 and 90% of the estimated increase in the relative demand for college-educated workers from 1970 to 2000 (Kim & Park, 2020). In summary, in a labor-intensive industry, routine tasks will need reskilling while skilled professionals need upskilling.
A Reskilling and Upskilling Collaborative Ecosystem in the Era of Industry 4.0 Both employers and employees recognize that work is becoming digital, and this new environment requires updated skills. In responding to growing demands across various occupational sectors for multi-talented and highly skilled workers, more institutions have invested in multiple innovative approaches emphasizing the integration of skill-sets training. The Industry 4.0 smart systems emphasize the need to shift from focusing on automation to an intelligent collaboration between humans and machines. As such, we propose a reskilling and upskilling blueprint that is at the heart of human capital development and lifelong learning in the era of Industry 4.0 (Fig. 1). The system concept that consists of technology, people, and organization motivates the creation of the innovative skill-update program. Figure 1 summarizes and recommends steps and options necessary for the workforce to reinvent, re-orientate, reskill, and upskill around a human–machine collaboration framework to create a win–win scenario for industrial advancement. The building blocks of the innovative training and skilling programs are delineated. Early childhood education and K-12 education remain fundamental and mandatory for every citizen in the twenty-first century. In addition, diverse degree and non-degree options will provide avenues for citizens of the world to be lifelong learners. Non-traditional options such as employer-sponsored on-the-job training, seminars, self-study, and taking certificates from technology companies such as Microsoft are valuable opportunities. Fig. 1. Open in a new tab A Blueprint of Workforce Reskilling and Upskilling Industry 4.0 leads society going through a digital transformation. This transformation centers on a vision of new education and learning programs that can effectively provide training, skilling, reskilling, and upskilling to the future-ready workforce. Both higher education programs and non-traditional options can offer opportunities to the workforce to advance their skill sets. To make this happen, business leaders, educators, and governments must proactively build facilities and programs for society to benefit from learning new skills, innovative knowledge, and advanced theories. When the university, government, and business organizations become members of the education alliance, they are part of the reskilling and upskilling collaborative ecosystem to train a future-ready workforce (Li, 2020). Digital transformation has launched a global competitiveness pace which has initiated an instructional paradigm shift for learning and teaching. The US National Education Association (NEA), a founding member of the Partnership for the twenty-first century, is a viable advocator who encourages schools, districts, and states to infuse technology into education and provide tools and resources to facilitate that effort.6 College degree programs are usually favorable for many people to upskill their capability and improve their credentials. In recent years, many universities have created new programs, such as data science and cybersecurity, to help the workforce strengthen their skills in critical thinking, complex problem solving, creativity, and communication (Li, 2020). The COVID-19 pandemic accelerated the pace of automation. Employees and students swiftly acquired cloud technologies, video conferencing skills, and remote telework skills. Schools, retailers, banks, and many businesses are emerging from the crisis into a world of physical distancing workplaces that changed customer behaviors and preferences. Many people learn these skills over online programs, such as online workshops offered by their companies, YouTube videos, and self-training. Recovery is forcing organizations to re-imagine their operations for the new normal. Manufacturing companies are reconfiguring their supply chains and production lines using Industry 4.0 technologies. Service providers are adapting to digital operations and contactless services. Those changes will significantly affect the requirements for staff’s skill sets because some face-to-face office roles may be replaced with a dramatic increase in home-based and remote working settings (Ellingrud et al., 2020). Companies can improve and promote diverse approaches to address skill gaps. For example, they can build skills internally, retain their existing staff by supporting them to work on advanced degrees, reimburse their tuitions, or invite training experts to train their staff. Alternatively, companies can recruit new employees with the right skills. A hybrid approach, including using a skilled contract workforce to fulfill short-term needs while developing the necessary skills internally, is also feasible. Upskilling and Reskilling through Higher Education In recent years, universities have promoted many new programs to support the digitalization needs of Industry 4.0. As a result, employers expect to recruit new staff who have some basic knowledge of their particular specialism and additional business skills. Furthermore, business recruiters are increasingly looking for workers who have expertise in information technology. On the other hand, companies do expect to put all graduates through induction training as well as specialized training throughout their careers (O'Brien & Deans, 1996). While completing a college degree in four years is a performance target of college education, Stanford University rolled out a new degree program called the Stanford2025 project. The Stanford2025 project allows students to extend their education over longer timeframes. One model is the “open loop university,” where students can experience six years of higher education over their entire adult careers that provide an opportunity for them to blend their learning with life experience and provide value to the campus by returning as expert practitioners over several intervals to re-charge with new skills and knowledge. Another model is named AXIS FLIP, which prioritizes skill development and competency training over disciplinary topics. It is hypothesized that by applying these proposed degree models, students would constantly renew their skills and update their knowledge throughout their careers (Stanford2025, 2013). Yet, new student learning assessment measures and methods need to be developed to gauge the learning outcome. Experiential Learning Experiential education serves as an integral part of a higher education degree program. While universities have established a student-centric effort to provide a hands-on and industry-oriented learning experience, they pay equal attention to developing students’ ability to apply theories to practical problems. Internships are a valuable step to becoming a future-ready employee. By working in their chosen field and interacting with employers and customers before graduation, students are more precise about their career goals and become stronger candidates for future jobs (Li, 2020). Meantime, measurable rubrics should be created to assess students' problem-solving skills, critical thinking capability, and device operation skills during their experiential learning projects. Universities are no longer solely emphasizing degree programs. Non-degree options have become part of higher education curriculum offerings. Many universities have added non-degree certificate programs to their catalog. For example, business schools in many countries have taken the lead in contributing to learning opportunities for a wide variety of individuals at different stages of their career paths. Fostering greater educational access requires business schools to accelerate their move beyond the bounds of traditional degree-based education. Higher education will need to redefine itself within the campus, business community, and future-ready workforce management systems. As hubs of learning, universities need to partner with universities in other countries, industry clusters, and organizations within the public and private sectors. This approach helps achieve knowledge creation, innovation, and community-building missions (Gleason, 2018a, b; Li, 2020). Technical and Vocational Colleges and Schools Germany, a leading manufacturing country globally and one of the best performing OECD countries in reading, mathematics, and science, redesigned the format of secondary vocational education for students to learn advanced skills for a specific profession. Most of Germany's highly skilled workforce has gone through a dual system of vocational education and training (VET) (Hockenos, 2018). In Germany, the VET programs have partnered with about 430,000 companies. Students learn skills through these programs that are easily transferable to an aspired profession. Once a company commits to an employee from one of these vocational schools, they have a commitment to each other, and about 80% of those companies hire students from the apprenticeship programs to get a full-time job. This educational system is very encouraging to young individuals because they can predict their career paths (Hockenos, 2018). China, the largest manufacturing country, has a long tradition of training middle school or high school graduates in vocational and technical schools or three-year colleges that focus on a specific profession or skill set. China urgently needs a high-level skilled workforce to support its booming economy. Vocational schools and technical colleges became part of the economic development engine by training a job-ready workforce. By integrating theories into practices in their curricula, vocational education ensures that students will have the skills companies seek. For example, Wuxi Machinery Manufacturing School invested 6,000,000 yuan in building Numerical Control Technology Center, Advanced Electrical Center, and Automobile Testing Center to provide hands-on laboratories for students to learn advanced manufacturing technologies and skills (Wu & Ye, 2018). The ability to fully harness advanced technology and skills is vital to the full realization of Industry 4.0. However, many countries may not yet fully be able to execute in practice. Reskilling and Upskilling through Non-Traditional Training As digitalization grows in every workplace, it becomes increasingly essential to direct employees’ time toward higher-value work. Professional associations recognize the need to upskill their members and workforce because upskilling is a requirement of many high-skilled professions. For example, university professors, nurses, accountants, and physicians must stay up-to-date on the knowledge of their professional area. Professional Certificate Many professional societies offer certificates via exams. Association of Supply Chain Management (ASCM), the largest nonprofit association for supply chain professionals, offers globally recognized certification programs to help industry professionals upskill and reskill to respond better to supply disruptions, respond to demand variations, and manage supply chain risks.7 The certification program serves the needs of both employers and supply chain professionals to be more competitive in today’s global economy. As the pandemic caused a significant shift in consumer demand and put a spotlight on supply chain vulnerabilities, ASCM rolled out a new certificate in Planning and Inventory Management (CPIM) which supports supply chain professionals to develop the competencies and skills they need to successfully work across all functions of the supply chain and logistics to respond better to supply disruptions and demand variations and manage supply chain risk. Re-certification Some professional jobs require regular re-certification to ensure that those who serve in a specific profession keep up with the advanced technology and the most current best practices. For example, nurse practitioners provide patient care in the middle of the Information Age, which means the amount of knowledge they need to know to do the job will double approximately every three years or even faster. Therefore, nurse practitioners certified by the AANP Certification Program must recertify every five years8 by taking the appropriate examination or meeting the clinical practice and continuing education requirements established for recertification. In addition, there are various professional development and upskill options that a nurse practitioner can take. Typically, they can combine professional development, such as continuing medical education, enrolling in academic courses, sharing patient treatment experience with peers at professional conferences, publishing peer-reviewed journal articles, etc. Company-Sponsored on-the-job Training The relationship between an organization and its people is a two-way street; therefore, the design phase of a future-of-work program should focus on a business’s offer to its staff (Ellingrud et al., 2020). Companies need to develop clear and compelling value propositions to ensure their staff sees the benefits of acquiring new skills and learning new technology. For years, Japanese companies have created a valuable culture of on-the-job training programs to upskill and reskill their employees since they have a life-long employment tradition. Quality Circle is one of the well-known employee-supported upskill efforts. Quality Circle involves employees in decision-making and shifts the organization toward a more participative culture. The program trains people to be critical thinkers and problem solvers when they perform their roles on the job. A team leader who typically is a trained staff of the management team helps train circle members and ensures that things run smoothly. These quality circles generally meet four hours a month on company time, and members would be recognized if their suggestions for production improvement are adopted (Lawler & Mohrman, 1984). In the dawn of Industry 4.0, digital links have increasingly replaced physical connectivity, and companies use more complex data networks in their operations. Greater inter-organizational collaboration is more possible than ever before (Xu, 2014). Using cloud-based software, any staff member in any geographical location can contribute to a design. To communicate more effectively, companies tend to use some standardized software such as Oracle, and Microsoft Office. Collaborating with third-party education providers to provide on-the-job training in information systems is a favorable choice to upskill employees. For example, as Microsoft added more functions to Excel spreadsheets, many companies quickly adopted new methods to run their operational business to interact with their business partners. University instructors are invited to business organizations to teach new Excel functions such as Power BI to help business employees stay current and effectively do business within the company and with their business partners. Self-Study Open-Course Programs In recent years, self-study programs have been available online to support people who are willing to reskill or upskill their intellectual capability. MIT open courses9 are open course programs that focus on “unlocking knowledge” and “empowering minds.” MIT OpenCourseWare (OCW) is a free, publicly accessible, openly licensed digital collection of high-quality teaching and learning materials presented in an easily accessible format. Learners can take more than 2,500 MIT on-campus courses and use supplemental resources for knowledge advancement. Technological innovation provides exciting opportunities and significant challenges for mature workers who are accustomed to routines, tasks, processes, and steps. These people tend not to like change. Yet, new technology is frequently updated at workplaces, regardless of employee’s age. As a result, some technology companies started to offer self-study open-course programs. SAS, Inc. (Statistical Analysis System) provides self-paced free courses for users to upskill their coding capability using new interfaces and functions. Selecting from a variety of course topics created by the industry's top experts, SAS helps existing users and new users stay aware of the technologies that employers are looking for.
| 2022-07-13T00:00:00 |
2022/07/13
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC9278314/
|
[
{
"date": "2022/07/13",
"position": 45,
"query": "reskilling AI automation"
}
] |
Is AI recruiting (un)ethical? A human rights perspective on the use of ...
|
Is AI recruiting (un)ethical? A human rights perspective on the use of AI for hiring
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Anna Lena Hunkenschroer",
"Chair Of Business Ethics",
"Technical University Of Munich",
"Arcisstr.",
"Munich",
"Alexander Kriebitz"
] |
Overall, we think that the use of AI could contribute to more efficient and more valid recruiting decisions. Although AI alone cannot capture ...
|
The use of artificial intelligence (AI) technologies in organizations’ recruiting and selection procedures has become commonplace in business practice; accordingly, research on AI recruiting has increased substantially in recent years. But, though various articles have highlighted the potential opportunities and ethical risks of AI recruiting, the topic has not been normatively assessed yet. We aim to fill this gap by providing an ethical analysis of AI recruiting from a human rights perspective. In doing so, we elaborate on human rights’ theoretical implications for corporate use of AI-driven hiring solutions. Therefore, we analyze whether AI hiring practices inherently conflict with the concepts of validity, autonomy, nondiscrimination, privacy, and transparency, which represent the main human rights relevant in this context. Concluding that these concepts are not at odds, we then use existing legal and ethical implications to determine organizations’ responsibility to enforce and realize human rights standards in the context of AI recruiting.
The contributions of our article are threefold. First, we address the need for domain-specific work in the field of AI ethics [ 14 – 16 ]. In examining the ethicality of AI recruiting, we go beyond general AI ethics guidelines that present overarching normative principles [e.g., 15 , 17 ] and study in detail the ethical implications of AI usage in this specific business function. Second, our paper expands the theoretical research in the field of AI recruiting. Though various extant articles have a practical [e.g., 18 ], technical [e.g., 19 ], or empirical [e.g., 20 , 21 ] focus, very few articles refer to ethical theories [e.g., 22 ] in this context (see review article [ 23 ]). To the best of our knowledge, our approach is one of the first to normatively assess whether the use of AI in the recruitment context is (un)ethical per se. By analyzing the use of AI in hiring from a human rights perspective, our paper overlaps with the work of Yam and Skorburg [ 11 ]. Nevertheless, while these authors evaluate whether various algorithmic impact assessments sufficiently address human rights to close the algorithmic accountability gap, we examine more fundamentally whether AI hiring practices inherently conflict with human rights. Third, our article provides implications for practice. By defining the ethical responsibilities of organizations, we aim to guide organizations on how to deploy AI in the recruiting process and enhance morality in hiring.
The remainder of the paper is organized as follows: Sect. 2 clarifies the concept of AI recruitment; in Sect. 3 , we outline the normative foundation of our approach, which is based on human rights discourse, and explore human rights’ implications for corporations and AI recruiting. In Sect. 4 , which is purely analytical, we discuss whether AI inherently conflicts with the key principles: validity, human autonomy, nondiscrimination, privacy, and transparency, which represent the human rights relevant in the AI-based recruitment context. Lastly, we discuss the contingent limitations of the use of AI in hiring. Here, we use existing legal and ethical implications to discern organizations’ responsibility to enforce and realize human rights standards in the context of AI recruiting, before outlining our concluding remarks.
This paper aims to fill this gap and provide an ethical analysis of AI recruiting to answer the question of whether AI recruiting should be considered (un)ethical from a human rights perspective, and if so, for what reason. We chose this perspective because human rights are internationally accepted as normative criterion for corporate actions and, increasingly, are integrated in soft law for business [ 8 – 10 ]. Human rights are overarching and comprehensive, yet also aim to be sensitive to cultural nuance [ 11 ]. Furthermore, as a legal framework, human rights carry significant implications for the moral underpinnings of business [ 12 , 13 ].
Still, many providers of AI recruiting tools advertise their products by claiming that they reduce bias and increase fairness in recruitment processes. In addition, widely held assumptions about the objectivity of learning algorithms contribute to a rather positive image of AI-aided recruitment among practitioners [e.g., 6 , 7 ]. The contrast between this positive image and the ethical concerns of AI recruitment’s critics calls for a normative assessment, essential for a more nuanced view of the ethical status of AI recruitment.
Increasingly, companies are using artificial intelligence (AI) recruiting tools to enhance the speed and efficiency of the applicant recruiting process. Especially in large companies, such as Vodafone, KPMG, BASF, or Unilever, the use of AI tools is already well-established to handle large numbers of incoming applications [ 1 , 2 ]. However, AI’s application to recruitment is the subject of controversy in public and academic discourse, due to the close relation between AI-based decision-making and ethical norms and values. One line of criticism considers it problematic that important decisions affecting people’s lives are outsourced to AI, which is especially problematic if mistakes are made. One of the best-known real-world examples is the case of Amazon in 2018, where a tested AI software systematically discriminated against women in the hiring process [ 3 ]. Various researchers, therefore, have warned of the significant risk these tools’ unknown flaws, such as algorithmic bias [ 4 ], pose to organizations implementing new forms of AI in their human resources (HR) processes. Similarly, several philosophers [e.g., 5 ] have condemned the use of AI in recruitment, denying that AI could possess the social and empathetic skills needed in the selection process.
These technologies can be applied across four commonly accepted stages of the recruiting process: outreach, screening, assessment and facilitation [ 25 ]. In the outreach stage, AI can be leveraged for targeted communication across online platforms and social media [ 26 ] or for de-biasing the wording of job ads to make them gender neutral and attract a diverse pool of applicants [ 27 ]. Moreover, algorithms are used to screen applicants’ CVs and derive a short list of the most promising candidates [ 19 ]. These screening tools are considered highly efficient, especially for top employers who receive huge numbers of applications for a single position. In the assessment stage, face recognition software can be used to analyze video interviews, evaluate applicants’ responses, and provide insight into certain personality traits and competencies [ 28 ]. In addition to interviews, AI-powered and gamified skill tests are used to assess further qualities, such as persistence or motivation. Therein, target variables do not need to be predefined by the company; ML algorithms can analyze the data of a company’s current top performers and determine which applicant characteristics and skills have been associated with better job performance [ 29 ]. Lastly, AI can also be leveraged to facilitate the selection process, for example, in scheduling activities [ 30 ].
We define AI recruiting as any organizational procedure during the recruitment and selection of job candidates that makes use of AI, whereas AI itself refers to “a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” [ 24 ]. This definition encompasses a diverse set of technologies, including complex machine learning (ML) approaches, natural language processing, and voice recognition.
Table 1 summarizes the implications for AI recruiting related to the underlying human rights and AI properties. However, these implications require a more detailed examination, explicitly for understanding the specific conditions for AI use in the recruiting context.
Transparency The outcomes of AI decisions are beyond the full control of human beings, making it difficult to trace responsibilities [ 17 ]. Literature on AI has referred to this aspect as its black-box character, as it is difficult for its users to understand why the algorithm has decided in a certain way. However, the right to be told the truth and the right to lodge a complaint when applicants feel treated unfairly (General Act on Equal Treatment §13 [ 40 ]) make it necessary for AI recruiting to be transparent.
Privacy AI decisions are typically based on a specific data input. The input used by an AI solution could conflict with the human right to privacy if the data was obtained by violating ethical principles (e.g., without the applicant having consented to its use). This risk is magnified by AI’s ability to access applicants’ personal information using, for example, facial recognition software. As addressed by regulations in the traditional context of recruiting (Sec. 2 U.S. Rehabilitation Act of 1973), data privacy is another important ethical concern in AI recruiting.
Nondiscrimination Data sets are susceptible to many types of bias [ 51 ], increasing the likelihood that AI that is reliant on historic data will fail in realizing its aims. If a decision made by AI impacts human beings, especially in the selection of job candidates, AI might lead to discrimination. However, the right to equality provides the basis for countering this vulnerability at all costs and makes nondiscrimination a prerequisite for the use of AI in recruiting. Notably, nondiscrimination and validity might not be the same in recruiting, as there might be specific legal obligations to respect certain quotas or to respect the rights of disabled persons (see Sec. 2, U.S. Rehabilitation Act of 1973).
Autonomy AI might reduce human involvement, as human beings cede certain decision-making or analytical tasks to automated machines. As a result, certain applications of AI could conflict with the right to human self-determination and threaten human freedom if they render certain choices obsolete. Therefore, AI recruiting tools should only be used to the extent that they do not limit human autonomy, so as not to conflict with human dignity and the right to occupation.
Validity AI is developed by human beings, who are not always perfect in their judgment and who will make mistakes in designing, programming, and using AI solutions. These mistakes might result in human rights violations, for example, when it comes to injuries or psychological stress incurred by ill-calibrated AI solutions (compare with Floridi et al.’s [ 17 ] principle of non-maleficence). However, the validity of AI recruiting can be considered a precondition for its ethicality, given companies’ need to find the right candidate. Along with efficiency, the validity of the data-driven predictions made by AI serves as the main determinant for judging the superiority [or beneficence] of AI solutions over traditional recruitment practices. This connects to the larger debate on how AI can promote human rights.
By combining the human rights requirements for recruitment with the discourse on AI ethics that addresses the critical properties of AI, we can derive the specific human rights implications of AI recruiting. These implications depict the analytical tool for our ethical examination, which addresses the following aspects:
These looming conflicts AI may have with a series of human rights and other normative principles (such as happiness or economic growth) have given rise to an intense debate on the regulation of AI. Several ethics guidelines, including the Montreal Declaration for Responsible AI [ 46 ] and AI4People’s principles for AI ethics [ 17 ] 3 have been released by various stakeholder groups.
The existing literature has examined, apart from the broader implications of human rights for enterprises and recruiting, the more specific implications of human rights on the use and development of AI. The starting point of the debate on these latter implications has been that the properties of AI solutions make this technology unique, differentiating it from older technologies such as computers, airplanes, or nuclear power plants. These properties, such as automated decision-making, use of historic data, access to private data [ 42 , 43 ], and AI’s black-box character [ 44 , 45 ], highlight potential areas of human rights violations, as they could stand in a more general—perhaps even inherent—conflict with specific human rights, as discussed in the literature [ 36 ].
In a nutshell, the pre-existing discourse on human rights in recruitment entails key normative implications for the management of the recruiting process. The first implication is that the process of hiring and access to jobs are highly relevant for many human rights in that these connect the larger debates on freedom of occupation, transparency, and human dignity with labor law and nondiscrimination legislation. The second implication is that anti-discrimination and privacy norms are closely linked and support each other in realizing dignity in the workplace. Therefore, the human rights perspective on AI recruiting has to be aware of these important connections.
Apart from obvious implications, such as bans on child labor or forced labor, there are other major implications of human rights that have already been discussed in the specific context of hiring. Among others, Alder and Gilbert [ 41 ] refer to the right to personal dignity. The applicants’ right to dignity requires that care be taken when it comes to potentially invasive assessment techniques such as personality tests and drug testing. The US Employee Polygraph Protection Act (EPPA) forbids private employers from using most lie detector tests, considered disrespectful and demeaning. Similarly, managers have a duty to preserve individuals’ right to privacy by safeguarding their personal information and exercising discretion when conducting background checks [ 41 ]. The right to privacy suggests that applicants have also the right to deny statements or withhold information on such topics as marriage, pregnancy, or religious affiliation, all of which could potentially be used for purposes of discrimination. Some legislation even stipulates the notion of a right to lie. The right to privacy is closely connected with anti-discrimination regulations, which derive primarily from the right to equality (one of the earliest constitutionally guaranteed rights) and are mandatory for companies (General Act on Equal Treatment §12 [ 40 ]). These regulations protect applicants’ right not to be rejected on the basis of a non-work-related characteristic such as age, gender, or ethnicity. Given the power asymmetry between applicant and employee, some scholars have expressed the view that applicants also have a right to be told the truth. Alder and Gilbert [ 41 ] have argued that managers have a moral duty to be upfront with applicants, providing them with honest assessments, updates of their status in the hiring process, and realistic previews of the job. Finally, all these rights link up to the key norms on which market economies are based, namely, the general right to freedom and the specific right to occupation that are necessary for realizing and expanding human autonomy.
The rights to property and freedom of contract, however, are limited so that companies may not disregard the interests and rights of (potential) employees. The human rights perspective suggests that hiring companies have a moral duty to safeguard applicants’ rights not only in the hiring decisions they make but also in how they treat applicants during the selection process (General Act on Equal Treatment §2 [ 40 ]; [ 41 ]). The International Bill of Human Rights 2 includes a range of rights and freedoms linked to international labor standards, such as the rights to human dignity, occupational choice, equality, privacy, education, and favorable conditions of work. In addition, the International Labor Organization’s Declaration on Fundamental Principles and Rights at Work has addressed, in particular, freedom of association and collective bargaining, forced labor, child labor, and nondiscrimination [ 13 ].
The notion that business enterprises have to honor human rights has major implications for recruiting, which has become an important source of sustainable competitive advantage for organizations [ 25 ]. Here, the context of recruiting is dominated by diverging interests and different rights applicable between companies and potential employees. Companies have a legitimate interest in selecting and filtering out the best candidates for a certain job and also a right to information gained by checking whether an applicant fulfills the qualifications demanded by the company. This right to information is, strictly speaking, not a human right as such, but rather arises from the right to property of companies and their owners, as well as from their legal interest in an effective process that ensures the selection of the right employees. In view of HR’s relevance to an enterprise’s commercial success, the company needs to have sufficient insight into the qualities of the potential employee. Here, the limitations of collecting information and the limitations of the general right to property connect to a wider legal debate on the derogation of the right to privacy and the right to property [ 37 , 38 ] as well as to the discourse on whistleblowing [ 39 ].
The discourse on business and human rights explores whether and to what extent companies must fulfill human rights responsibilities and obligations [ 9 , 31 ]. Conventional wisdom suggests that business and human rights inherently stand in conflict, given the interest of companies in maximizing their profits and the intense competition they face, which enhances the pressure on decision makers to reduce costs. The notion of the primacy of profitability and fiduciary responsibility was encapsulated in Friedman’s dictum: “the business of business is to make profit” [ 32 ]. As society increasingly scrutinizes the actions of companies, contemporary theories of business ethics and corporate social responsibility have acknowledged the existence of company-specific human rights obligations [ 33 , 34 ]. An emerging consensus implies that human rights are of increasing significance for business and that corporate decision makers are required to protect, respect, and remedy human rights. This notion is reflected in the UN Guiding Principles, which are grounded in the belief that business enterprises are “required to comply with all applicable laws and to respect human rights.” [ 35 ] Hence, human rights are boundaries that corporate actions must not cross, a principle that implies that certain acts, such as discrimination or violation of the human dignity of employees, are morally reprehensible. 1 Companies are obliged to comply with these legal responsibilities “through their own activities” (United Nations General Principles, Principle 13), including business operations such as recruiting and the use of AI [ 36 ].
In the following section, we summarize the different implications of AI recruiting as derived from the discourse on human rights. As a starting point for our approach, we focus on human rights, given their international acceptance as a normative concept for corporate action [ 8 – 10 ]. To structure our review of normative approaches and discussions, we have distinguished between different coinciding discourses. These include the more general debate on business and human rights, establishing that not only states but also companies are accountable for human rights; the specific human rights implications of recruiting; and, finally, the discourse on the ethical regulation of AI. All three of these perspectives are pertinent in carving out the ethical materiality of AI usage in hiring, as they outline the responsibilities of the key actors: companies that define recruitment practices and standards and establish criteria for the judgment of AI solutions.
Ethical analysis: is AI recruiting unethical per se?
In the following, we explore the question of whether AI recruiting should be considered unethical per se. We distinguish between actions that inherently—and thus per se—conflict with human rights and actions that present a contingent conflict with human rights [see 36]. Individuals’ and organizations’ actions conflict inherently with human rights if they constitute a violation of human rights irrespective of circumstance. Based on our theoretical discussion in Sect. 3, we opt for human rights as our concept for companies’ ethical actions. Moreover, we integrate utilitarian and other approaches to ethics if they are helpful for interpreting human rights or if our analysis touches areas where human rights implications or established legal conventions do not offer straightforward solutions [34, 52]. The remainder of Sect. 4 is structured as follows:
In the first part (Sect. 4.1), we examine whether AI recruiting fulfills the precondition of providing a valid assessment of applicants. We consider this to be a necessary prerequisite because utilitarian theories of effective altruism [53] argue that ethicality involves the criterion of improvement of outcomes: status quo post must surpass status quo ante. Thus, unless AI recruiting is superior to traditional recruiting, using this technology is not only unethical but also possibly inefficient. In the following Sects. 4.2–4.5, we discuss ethical issues beyond validity, including human autonomy, nondiscrimination, privacy, and transparency. In assessing each of these principles, we address the potential reproaches against AI recruiting as well as the counterarguments for each. Table 2 summarizes this section’s discussion and the implications for organizations, which will be outlined in Sect. 5.
Table 2. Summary of ethical analysis of AI recruiting and implications for organizations Ethical principles Is AI recruiting inherently unethical? Implications for organizations Precondition Validity Reproach Lack of empathy and social intelligence Missing scientific validation Counterargument Validity of decisions depends on what activity AI is used for Data-driven predictions are better than human ones Establishing mechanisms for auditing and quality control Ensuring statistical expertise in HR departments Using AI for objectively measurable requirements Using AI as complementary recruiting tool Autonomy Reproach Dependence on AI-made decisions Reduction of chance to perform for applicants Dehumanization of hiring process Lack of control of every single step by recruiters Counterargument Applicants always depend on others’ decisions Humans are not inherently better interview partners than AI AI allows recruiters to have control over final decisions Using AI as additional recruiting tool Establishing human oversight over process Creating transparency/ explainability reports Nondiscrimination Reproach Risk of algorithmic bias Risk of standardized discrimination Unfair treatment of nonstandard/disabled people Counterargument AI is never inherently racist but may be thus programmed/trained by humans AI may reduce human bias Reconfiguring AI to prevent bias against disabled people can offer a chance for inclusion Auditing AI with regard to bias and discrimination Validating AI tools for nonstandard people Implementing diverse data scientist teams Privacy Reproach Access to additional types of data (e.g., sexual orientation) Collection and usage of many data points Counterargument: Firms can define and control the input data used and stored Obtaining consent for data use from applicants Establishing data minimization: collection and storage of minimal and relevant data Transparency Reproach Black-box character: lack of transparency for the single case Counterargument Transparency for the general mechanism is given (e.g., in the form of open code) AI may enable regular updates and timely feedback for applicants Disclosing selection and success criteria Reducing complexity of algorithms Creating transparency/explainability reports Communicating about discrimination cases and number of claims Open in a new tab
Precondition: is AI a valid tool in the recruiting and selection process? Considering that many companies have already implemented AI technologies in their recruiting process, we assume that AI recruiting is time and cost efficient, something research agrees on [26, e.g., 54–56]. However, critics warn about AI recruiting’s potential constraints in terms of validity. One such argument states that AI represents only a simplified model of human behavior that is restricted to a set of measurable behavioral dimensions [4, 57, 58]. Thus, AI lacks empathy and cannot detect applicants’ emotional intelligence, which reduces the validity of an AI assessment [5]. Although AI may be able to recognize and imitate emotions with sensors (known as affective computing), it cannot understand complex emotions and feelings. Complex forms of sadness, such as self-pity, regret, and loneliness, are just as unreadable as complex forms of joy, such as schadenfreude, pride, and confidence. AI also cannot perceive and understand values or charisma. The same applies to many contexts where psychometric quantifications are inherently incapable of capturing contextual meanings of competence. One can try to program values into AI—but nuances will be lost [59–61]. Therefore, AI cannot assess an applicant’s personal or team fit or determine whether an applicant is truly motivated or reflective—or whether their statements are substantiated. From our point of view, however, this argument against AI recruitment tools can be weakened by the fact that team fit and social intelligence are only two criteria among many in the recruiting process. Even in non-AI-based procedures, the screening and shortlisting of CVs is based on fixed and quantified criteria, such as average academic grades or months of prior job experience. These sort of criteria could be easily managed by AI. This example also leads to the question of whether academic grades are an effective predictor of subsequent performance at all and highlights the added value of another feature of AI: based on ML and the data of current top performers, AI can assess which characteristics make an applicant a good fit for a given role, thus enhancing the selection process’s accuracy [18, 62]. Again, it can be argued that AI tools are often not scientifically validated but have emerged as technological innovations only. Similarly, the underlying criteria for the prediction of job performance may not be derived from scientific research programs [63, 64]. Moreover, ML algorithms predict future human behavior based on historical data, ignoring novel patterns and parameters [65]. Therefore, predictions are often proven wrong because of changes in the overarching ecosystem [66, 67]. However, we think that it is questionable whether people, with their subjective perceptions and assessments, perform better than AI in this regard. Because AI is data-based and can process a much larger range of behavioral signals than humans can, AI may even outperform human inferences about future performance in accuracy and validity [18, 68]. This is also in line with Kahnemann’s [69] findings that algorithmic predictions generally perform better than human ones and suggests that whenever we can replace human judgments with formulas, we should at least consider it. Overall, we think that the use of AI could contribute to more efficient and more valid recruiting decisions. Although AI alone cannot capture all potential job criteria, it is not a non-valid tool per se. Consequently, AI decisions’ validity depends on the activity for which AI is used for. Assigning appropriate tasks to AI therefore requires recognition of its shortcomings, e.g., its reductionist nature that cannot interpret contexts. That being said, validity is a contingent rather than inherent limitation to AI development and deployment in a hiring context.
Does autonomy inherently conflict with AI recruiting? Autonomy has been classically seen as expression of the right to freedom and self-determination in combination with more specific rights, such as freedom of occupation and freedom of movement. Although autonomy’s importance has been emphasized by various scholars [e.g., 70, 71] and in various frameworks [14, 17], its exact meaning remains disputed. Relevant questions for the interpretation of autonomy are as follows: What degree of human control is implied by the concept of autonomy? Should we try to realize human control in areas that have not yet been controlled? Autonomy’s implications depend on the answers to these questions. One might argue that human actions should not be constrained by technologies—compared to the ex ante status quo—and that humans should have control over the outcome. Here, we often encounter the notion of meta-autonomy, defined as the voluntary decision to “delegate specific decisions to machines” [17]. Other positions argue that human actions should be enhanced through technologies and that limits should be imposed on technologies [72]. In the context of AI recruiting, AI generates implications for the autonomy of not only the applicants but also the recruiters. Hence, in our analysis, we embrace both of these perspectives. Considering the applicant perspective, first, one may argue that the use of AI tools conflicts with applicants’ autonomy. By interacting with an AI instead of humans, applicants lose the opportunity to get to know the company in the form of future colleagues and to evaluate whether the company culture fits their needs and expectations, fully depending on the AI-made decision. Thereby, the asymmetry of time and effort investment increases: applicants invest the same amounts of time and effort as required for human-based procedures, whereas companies automate the process, saving time and money. However, regardless of the recruiting procedure used, applicants are always subject to the company’s process and depend on others’ decisions. Thus, in this regard, we do not see any impact on applicants’ autonomy. Without any personal interaction in the process, it may be even easier for applicants to accept rejection and reorient themselves afterward. Second, one may argue that candidates’ autonomy is reduced because they cannot demonstrate all their empathetic, social, and soft skills in interviews with AI because the latter cannot fully value them. In this way, AI interviews may even lead to changes in applicants’ behavior, such as using special buzzwords that the AI will recognize. However, we would counter that human interviewers are not always better listeners or conversation partners in interviews. In fact, applicants may feel less embarrassed when sharing personal experiences with an AI than when doing so with a human. Moreover, adapting one’s behavior to an interview partner applies to not only AI interviews but also face-to-face (FTF) interviews with different types of interviewers. Lastly, a frequent line of argument is that AI recruiting represents a conflict with human autonomy because weighty decisions are taken over by AI with huge impact on human lives. This stands in direct conflict with the meaning of human rights because it leads to a dehumanization of the recruiting process and a devaluation of human lives, especially when these tools are used for only certain types of jobs and applicants (e.g., low-impact jobs and not top-manager positions). Furthermore, although recruiting can become more efficient by using AI tools, it can ultimately lead to mechanizing the hiring process, leading to little or no direct human contact between individual applicants and the future employer [4]. This might lead to the reification of interpersonal relationships, whereby both applicants and recruiters would experience a loss of individuality and autonomy [4, 73, 74]. When taking the recruiters’ perspective to analyze whether AI recruiting conflicts with autonomy, we must consider the differing interpretations of autonomy and their underlying expectations regarding human control. If autonomy is understood as the control of every single step in the recruiting process, AI recruiting may indeed conflict with this concept. When AI applications take over certain activities, including data analyses and decision-making, or at least shape human decisions by interfering with deliberation processes, this results in meta-autonomy and a reduction of control for recruiters [75]. The more recruiters’ decision-making is substituted by AI, the less opportunities and autonomy recruiters will have to make their own decisions, whereby their learning capacities will be reduced [4]. This reduction of control and autonomy for recruiters may be particularly problematic if competitive pressure forces companies to use AI. Therefore, companies might opt for cost-efficient solutions at the expense of quality standards. This applies specifically to scenarios in which recruiters must process large volumes of applicants under time pressure. However, the assessment differs when understanding autonomy in the sense of end control. End control is provided to recruiters when they can overrule AI decisions or when AI is used as an additional recommendation tool, but human recruiters make the final decision about who is offered a position. Thereby, realizing human autonomy may depend on whether the team of recruiters understands the rationale of the AI solution and decision. In this case, AI recruiting would not be unethical per se, but it would require that the criteria and algorithms behind each hiring decision be explainable and known by the company. Likewise, recruiters would have to consider additional mechanisms for quality assurance. For example, randomly selected applicants who are eliminated during the AI-based process could be reevaluated by a human evaluator as a check. Although we acknowledge that AI use may lead to a dehumanization of the recruiting process, AI usage in recruiting does not constitute an inherent breach of human rights according to our understanding. A specific debate concerns the notion of statistical dehumanization that reduces human beings to a number [76]. Similar views have been raised in the press, arguing that large numbers entail a dehumanization tendency. In our view, however, this is an ethical point that is excessively fundamental. Even today, companies are confronted with high numbers of applications that make it difficult to concentrate on individuals. One way out might lie in the aforementioned idea of allowing for exemptions from AI hiring solutions through a random review of individual cases to avoid a systematic dehumanization. Nevertheless, we consider the dehumanization argument to be a philosophical question that, first, is generally directed against any technological progress that reduces human interaction and, second, leads to further philosophical questions, such as the following: Which measures should society employ to regain humanity? Because this question is too fundamental in nature to be solved within our contribution, we treat it as an underlying assumption behind contemporary recruiting practice. Therefore, the perspective of AI solutions as conflicting inherently with human rights originates in a specific interpretation of human oversight.
Does nondiscrimination inherently conflict with AI recruiting? The right to nondiscrimination derives primarily from the right to equality. However, it has only recently been applied in private law. Beyond the controversial debate on quotas, diversity, and specific interpretations of the right to equality, we maintain an understanding of nondiscrimination meaning that everyone should have the same chances, regardless of personal attributes, such as ethnic, cultural, and migration backgrounds and gender. The mathematical term for nondiscrimination is that there is the same likelihood for a specific outcome given the same properties of the individuals being assessed (compare: Basic Law of the Federal Republic of Germany, Art. 3). Although discrimination entails different dimensions that transcend mathematical formulations [77], the presented approach presents a key threshold of the mathematical process underlying AI hiring. Do AI recruiting tools per se discriminate against certain groups of applicants? The Amazon case illustrates that the use of AI in recruiting may introduce algorithmic bias due to poorly trained algorithms [e.g., 58, 78], which may result in (unintended) discrimination against certain applicant groups [e.g., 51]. Critics argue that such discrimination by a machine is even worse than discrimination by a human being because algorithmic bias standardizes and magnifies discrimination, which could also result in institutionalized racism [26, 29]. Moreover, AI may introduce new types of biases, which are not yet defined within nondiscrimination literature [79]. However, in many contexts, it is not feasible to formalize all dimensions and context-dependencies of discrimination in such a way that the extent of AI discrimination can be compared to that of human discrimination. This is also true, for example, when it comes to intersectional discrimination.4 However, we would argue that AI is not inherently racist and merely follows codes and criteria that are programmed by humans. Thus, the original source of algorithmic bias is human—either in the form of human behavior that the AI simulates or in the form of a programmer who (deliberately or unintentionally) programmed the AI in a racist manner. Nevertheless, we admit that adverse effects can occur when AI is used for recruiting, bearing an ethical risk. Here, the question arises of whether the risk for such algorithmic bias should be considered unethical. Although algorithmic bias may be much easier to detect and remove compared with human biases [7, 56], a conflict between AI recruiting and nondiscrimination may emerge if one argues that the pure risk of discrimination delegitimizes the use of AI. However, it can be argued at this point that even today’s human-based selection procedures are not free of bias. Rather, the opposite is the case; scientists broadly agree that the practices currently in place are far from being effective and unbiased [e.g., 7, 81] and that AI has the potential to reduce human bias in these processes. For example, AI can address bias in the form of gendered language in job descriptions, making them gender-neutral and more inclusive [82]. Moreover, in the screening and assessment stages, subjectivity can be reduced by using algorithms that evaluate all applicants against the same criteria, thereby reducing human bias related to applicants’ physical appearance because AI can be taught to ignore people’s protected personal attributes and focus only on specific skills [83, 84]. Thus, if one argues that AI should be considered ethical as long as it has the potential to reduce human bias, we do not see an inherent conflict between human rights and AI recruiting. Another line of argument states that the standardized process that comes along with AI recruiting triggers an unfair treatment for nonstandard applicants, such as disabled people. Scott-Parker [85] argued that when considering disabled people, fairness does not mean making the recruiting process more consistent and standardized, but rather making the process more flexible to generate equal opportunities for all applicants. This flexibility is not provided by highly automated and rigid AI recruiting processes, which are not yet validated for disabled people and ignore the impact of disabilities on voice, word choice, and movements, among other factors. For example, gamified assessments are often difficult for people with only one hand, in wheelchairs, or who are color-blind, thus discriminating against disabled people. Scott-Parker [85] called this “disability bias,” which is crucial in the AI recruiting context but is not yet often referenced in the AI debate. We fully support this reasoning and concern; however, we do not consider it to fundamentally conflict with AI recruiting. Instead, it underscores the following needs: for AI recruiting to be validated for disabled people, to include disabled people in original databases, and to generate equal chances for all applicants. We would go even further, arguing that reconfiguring AI to disabled persons’ needs could even be a chance for inclusion. Overall, we argue that AI recruiting does not inherently conflict with the principle of nondiscrimination, but potential systemized, algorithmic bias constitutes a contingent limitation. Although algorithmic bias may occur unintentionally and be based on unknown criteria, we consider this rather a problem of the AI tool’s validity, which should be correctly trained and programmed to work in the same way for all groups of applicants. Thus, technical due diligence and auditing regarding valid data sets and algorithmic designs are crucial to keep the risk of algorithmic bias low.
Does privacy inherently conflict with AI recruiting? One the one hand, privacy can be considered an essential part of human dignity and, thus, an intrinsic human right. Likewise, privacy can be derived from Articles 12, 18, and 19 of the Universal Declaration of Human Rights [86]. This understanding has been promoted, for example, by the German Federal Constitutional Court, which has interpreted a person's intimate sphere as a central human right. Thus, the court stated that the right of personality belongs to the essence of human dignity [87]. Thus, this right enjoys special protection against encroachment by others for commercial or artistic purposes. On the other hand, the right to privacy can be derived from the idea that individuals have the right to conceal information from others. Therefore, it might be considered an instrumental right because it allows individuals to engage in activities or to have preferences that are not shared by everyone or that are scrutinized by societies. Throughout history, sexual minorities have been often targeted by social stigma, which is ongoing. To the same extent, information concerning people’s ethnic backgrounds has been used to commit human rights violations. On the contrary, utilitarian approaches would challenge privacy’s innate value. These would argue that personal privacy must be balanced with other aims, such as economic efficiency or societal safety and health (as contemporarily discussed in the context of action against COVID-19). The key question, therefore, is as follows: What type and amount of data is a potential employer allowed to collect and store concerning applicants? With the development of the General Data Protection Regulation (GDPR), privacy is already a regulated area in hiring. This regulation is aimed to protect EU citizens’ rights by governing how to collect, store, and process personal data. Moreover, individuals have the right to conceal from employers any personal information that is irrelevant for the fulfillment of the potential job task (e.g., sexual orientation). Does privacy inherently conflict with AI recruiting? To answer this question with “no,” the GDPR states that applicants in a recruiting process must have the opportunity to explicitly consent to the use of their data. However, an ethical dilemma emerges at this point because of the power asymmetry in the job market between employers and applicants. This means that generally applicants may be unable to refuse the use of certain personal data without being disadvantaged in the process. However, this dilemma is not caused by the use of AI, but applies to the general context of hiring as well as to human-led processes [88]. The same is true for the argument that it is unethical to collect social media data for hiring purposes when users generally use social media platforms for other purposes [29, 64]. It is questionable whether social media is a good information source or a reliable indicator of job performance [19]. However, this discussion on the use of social media information in the hiring context is not new. A study in Sweden showed that at least half of the interviewed recruiters scanned candidates’ social media profiles at some point before hiring [81]. Some of AI recruiting’s inherent properties distinguish it from traditional recruiting practices, and we will focus on whether these properties conflict with the right to privacy. First, AI recruiting allows for access to more types of data than human recruiting. For example, AI in the form of face recognition tools and prediction algorithms may forecast which candidates are most likely to become pregnant or reveal candidates’ sexual orientations [22, 89]. This access to candidates’ personal attributes conflicts with their privacy rights and increases the risk of information misuse and discrimination [83]. Through AI use, applicants are facing increasingly invasive methods of information gathering, which are expanding from applicants’ work life to social and even physiological domains [4]. Second, an inherent property of AI recruiting is that generally, this approach involves the collection and use of more data for decision-making than human recruiting. Whereas a human assessment is mainly based on an interviewer’s intuition and value assessment [81], an AI tool automatically captures millions of data points from applicants’ behavior, such as their verbal and body language, for a data-driven assessment of personality [90]. On the one hand, this may lead to a more data-driven and objective assessment of applicants; on the other hand, one could argue that this increased amount of collected and stored data may conflict with applicants’ privacy rights. However, from our perspective, these two properties of AI recruiting do not inherently conflict with the right to privacy. Although AI enables organizations to collect more data and access additional types of data, it still depends on the organization to determine and define which kinds of data the AI should collect, store, and use as input for the selection process. As long as the data collected refers to candidates’ personality traits or skills that are relevant to the job, we would not consider the use of additional data as inherently unethical, acknowledging that the distinction between relevant and irrelevant information can be blurred sometimes. However, individuals with a strong focus on data privacy might have objections to this view and consider the collection and use of certain data, such as biometric data, to be an inherent limitation of AI-based hiring.
| 2022-07-25T00:00:00 |
2022/07/25
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC9309597/
|
[
{
"date": "2022/07/25",
"position": 64,
"query": "artificial intelligence hiring"
},
{
"date": "2022/07/25",
"position": 64,
"query": "artificial intelligence hiring"
},
{
"date": "2022/07/25",
"position": 62,
"query": "artificial intelligence hiring"
},
{
"date": "2022/07/25",
"position": 44,
"query": "artificial intelligence hiring"
},
{
"date": "2022/07/25",
"position": 55,
"query": "artificial intelligence hiring"
},
{
"date": "2022/07/25",
"position": 58,
"query": "artificial intelligence hiring"
},
{
"date": "2022/07/25",
"position": 62,
"query": "artificial intelligence hiring"
},
{
"date": "2022/07/25",
"position": 55,
"query": "artificial intelligence hiring"
},
{
"date": "2022/07/25",
"position": 61,
"query": "artificial intelligence hiring"
}
] |
How AI Predicts Salaries: What Your Future Pay Could Be
|
How AI Predicts Salaries: What Your Future Pay Could Be
|
https://ownersmag.com
|
[
"Kaytie Cayton",
"Harper Goldman",
"Margaret Zander"
] |
This is where AI predicts salaries based on data and job descriptions. How, then, does AI predict salaries? AI Predicts Salaries.
|
68 SHARES Share Tweet
Interested in signing up for Demio? You can support us by getting started with this link.
I kind of hate the word “webinar.”
I’m not alone, either. You can find it in several lists of the English language’s biggest travesties. It’s a holdover from the heyday of lame Web 2.0 portmanteaus, alongside “webisode,” “netizen,” and “listicle.”
However you feel about the word, the webinar itself is anything but dated. The more work moves online, the more vital webinars become for drawing new clients (and keeping the old ones).
Yet, despite their importance, many platforms still haven’t nailed the experience. Some are clunky, others unreliable.
In this updated Demio SaaS review, we take another look at the browser-based webinar tool by Banzai to see if it still strikes the right balance between simplicity and functionality in 2025. Can Demio stay ahead of the curve—or is it time to move on?
Let’s find out.
What is Demio?
Demio is a browser-based webinar platform designed to make hosting and attending online events as frictionless as possible. Founded in 2014 and now part of the Banzai ecosystem, it was built in response to the clunky, download-heavy webinar tools that dominated the early 2010s.
As this Demio SaaS review shows, that original mission still holds up in 2025. While the pandemic era pushed dozens of companies to improve their virtual tools, many platforms still require attendees to install software or jump through technical hoops just to join a session.
Demio’s solution? Keep it in the browser. No downloads. No plugins. Just clean, streamlined webinar tech that anyone can use right away.
It’s positioned squarely in the SaaS space, with subscription plans that scale from solo creators to enterprise teams. And while it’s optimized for marketing and lead generation, the platform’s ease of use makes it appealing across industries.
Looking for other video communication tools? Check out our Loom review.
Getting started with Demio: Free Trial and Pricing
No Demio SaaS review will be complete without the pricing tier. To sign up for a 14-day free trial, just create an account, and you’re ready to explore the platform.
When you’re ready to upgrade, Demio offers three main plans tailored to different business needs:
Starter – $45/month per host (paid yearly). Perfect for small businesses and solo entrepreneurs getting started with webinars. This tier is for one host for up to 50 attendees. It also comes with core features to launch live webinars easily.
– $45/month per host (paid yearly). Perfect for small businesses and solo entrepreneurs getting started with webinars. This tier is for one host for up to 50 attendees. It also comes with core features to launch live webinars easily. Growth – $80/month per host (paid yearly). Ideal for growing companies that need more flexibility and brand control. This tier accommodates multiple hosts, with attendee rooms from 150 up to 3,000. This plan also comes with custom branding and enhanced integrations, and reporting.
– $80/month per host (paid yearly). Ideal for growing companies that need more flexibility and brand control. This tier accommodates multiple hosts, with attendee rooms from 150 up to 3,000. This plan also comes with custom branding and enhanced integrations, and reporting. Premium – $196/month per host (paid yearly). Designed for larger teams and enterprise use. This tier comes with dedicated CSM and priority support, premium integrations and custom domains, Demio AI, and access to beta features. With this plan, you can have up to 10 people on stage, with attendee rooms of 150, 500, 1,000, or 3,000.
Demio’s free trial requires no commitment or credit card details. Just sign up, fill out a brief survey on how you plan to use the app, and you’re golden.
Demio Features
Demio keeps things simple without skimping on functionality. Once you’re signed in, you’re welcomed by a clean, intuitive dashboard that puts your upcoming events front and center.
Here’s a breakdown of the core features that make Demio a standout in the crowded webinar space:
Dashboard
Demio’s dashboard is built for clarity. You can quickly scroll through upcoming sessions, monitor your events, and navigate between tabs like Schedule and Events. It’s functional, but still has room to improve,especially when switching between creating and managing events. A unified view would make it even smoother.
Events
Demio lets you create three types of events, each tailored to different use cases:
Standard Events – Traditional live webinars where attendees register for a single session at a specific time.
– Traditional live webinars where attendees register for a single session at a specific time. Series Events – Great for multi-part webinars or training sessions. When users register for one, they’re automatically signed up for the entire series.
– Great for multi-part webinars or training sessions. When users register for one, they’re automatically signed up for the entire series. Automated Events – Pre-recorded sessions that run on autopilot. Perfect for lead nurturing or delivering evergreen content without going live.
Automated events continue to be one of Demio’s strongest features, letting you scale your content while staying hands-off.
Customization
Before your webinar goes live, the Customize tab lets you tweak everything from registration forms to event visuals. You can upload slide decks, create interactive polls, set up handouts, and even brand your webinar pages to match your company’s look.
For Growth and Premium users, custom domains and branding take things even further—ideal for marketing teams or agencies.
Once you’re ready to get started, you can join your session in the Schedule tab. The layout is familiar, with speakers’ video taking up the left and center while the chat tab takes up the right side.
Only one person can be “on stage” at a time, but you can also add and access materials like slides and videos with the middle button on the bottom toolbar. Meanwhile, the + icon next to the chat box lets users access polls, links, and handouts.
Reports
After your session ends, head to the Activity tab to access attendance reports. You’ll see who registered, who actually attended, how long they stayed, and what they engaged with during the session.
Downloadable CSV files make it easy to follow up with participants or segment your leads—an especially useful feature for marketers.
While the data is useful, the reporting could be more advanced (think engagement heatmaps or behavioral trends). Hopefully, that’s in Demio’s roadmap for the near future.
Integrations
I’d honestly like to see a little more variety from Demio’s integrations. On the one hand, their tilt towards martech integrations makes sense. Webinars are generally used for marketing, and being able to connect with Keap, Mailchimp, or your CRM of choice has obvious benefits.
Still, I think there’s a lot more potential to be had with connecting different software to a video conferencing tool. Translators, editing tools, OBS… the sky’s the limit.
Perhaps the most useful integration is with Zapier. Their micro-integrations let you connect to PayPal, Gmail, Slack, and more.
Conclusion: Is Demio worth it?
If you’re seeking a platform to create engaging webinars, Demio is a great place to look. It’s as intuitive as they come, with a number of unique features that set it apart from the competition. Even among browser-based video tools, the fact that it works on any browser puts it ahead.
As of now, Demio is completely focused on webinars. It’s a leader in that market, so they’re clearly doing something right. Where it disappoints, however, is where it feels too laser-guided towards marketing. By just slightly expanding a few features (integrations, reports, in-call elements), I think Demio’s potential could be that much greater.
PROS
No-download, browser-based platform
Quick, user-friendly setup
Supports live, automated, and series events
Clean, customizable interface
Great for marketing and lead generation
Solid integrations with CRMs and email platforms
Zapier access unlocks thousands of app connections
Custom branding and domains (Growth & Premium plans)
Strong customer support and onboarding
Scalable plans for teams of any size
CONS
Limited native integrations outside of marketing tools
Reporting could be more robust (e.g., engagement insights, AI summaries)
Dashboard navigation could be more streamlined
Higher-tier pricing may be steep for very small teams
Overall rating: 8.9/10
Ready to give Demio a try? Sign up here.
Frequently Asked Questions
Is demio.com safe?
Yes, demio.com is a secure and reputable site owned by Banzai, using encryption and standard security protocols to protect user data and webinar content.
Is Demio like Zoom?
Demio and Zoom both support video communication, but Demio is specifically built for webinars and marketing events, while Zoom is designed primarily for meetings and general video conferencing.
Is Demio easy to use?
Yes, Demio is known for its clean interface and intuitive setup, making it easy for both hosts and attendees to run or join webinars directly from a browser.
| 2025-06-20T00:00:00 |
2025/06/20
|
https://ownersmag.com/ai-predicts-salaries/
|
[
{
"date": "2022/07/25",
"position": 54,
"query": "AI wages"
},
{
"date": "2022/07/25",
"position": 60,
"query": "AI wages"
},
{
"date": "2022/07/25",
"position": 58,
"query": "AI wages"
}
] |
What Job Applicants Need to Know About AI in Hiring
|
What Job Applicants Need to Know About AI in Hiring
|
https://career.ufl.edu
|
[
"Farias"
] |
Artificially intelligent programs now routinely screen job applications, often before a human hiring manager ever sees a single resume.
|
Artificial intelligence is not just about self-driving cars and Silicon Valley. AI has found its way into nearly every job — and even into landing that job in the first place. Artificially intelligent programs now routinely screen job applications, often before a human hiring manager ever sees a single resume. Companies are also increasingly turning to AI job interviews, a kind of recorded interview that can screen for job knowledge and even analyze body language.
At the end of the day, the same skills that work for the traditional hiring process can be applied to this brave new world. Here are some simple tips on how to sail through the AI systems so you can land your dream job.
Write for the computer — and the human
Employers are increasingly using AI systems to help with screening and sifting through job applications, leaning most heavily on tools known as applicant tracking systems, or ATS. An ATS can automatically compare resumes against the job description and rank candidates based on how well it thinks they fit the qualifications.
That filtering process mostly boils down to how well the software thinks your resume lines up with keywords it notices in the job description or that the hiring manager asked it to search for.
So, how do you spot those key phrases to make an ATS friendly resume?
“The biggest thing is using the job description as a guide,” said Sara Gould, senior assistant director for career engagement at the University of Florida’s Career Connections Center. “Go through it old school with a highlighter, find the language they’re using in their document and then apply that to yours.”
Zero in on required certifications or skills before worrying about “preferred” qualifications. Look for words or phrases that are repeated or meaningful in the industry. See how your background and skills can be shared using this language.
Formatting is also key. If the application specifies what kind of document to submit, follow those directions closely. Typically, a plain Word document or PDF is safe. Fancy graphics or complex columns might confuse the machine reader, so stick to a straightforward layout. Most hiring managers are interested in the substance, rather than the style, of a resume, anyway.
And don’t make the mistake of trying to outsmart the machine. Tricks like posting the entire job description into the resume in invisible white text will get you noticed, but not in a good way. “Those things can be flagged as an anomaly,” Gould said. “It’s a machine learning system, so they’re learning those tricks, too.”
Employers who see these warnings on your application will know you’ve tried to game the system.
But never forget that your goal is to impress the human behind the AI. Avoid robotic lists of keywords. Try to seamlessly weave in the most important qualifications, skills and key phrases into normal language so your own humanity shines through.
Resume keyword scanners also make the personal touch as important as ever.
“Don’t let them stop you from networking, following up, reaching out,” Gould said. “There’s still a person there.”
| 2022-09-13T00:00:00 |
2022/09/13
|
https://career.ufl.edu/what-job-applicants-need-to-know-about-ai-in-hiring/
|
[
{
"date": "2022/08/10",
"position": 47,
"query": "artificial intelligence hiring"
},
{
"date": "2022/08/10",
"position": 63,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 69,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 63,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 59,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 61,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 53,
"query": "artificial intelligence hiring"
},
{
"date": "2022/08/10",
"position": 54,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 51,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 45,
"query": "artificial intelligence hiring"
},
{
"date": "2022/08/10",
"position": 52,
"query": "artificial intelligence hiring"
},
{
"date": "2022/08/10",
"position": 49,
"query": "artificial intelligence hiring"
},
{
"date": "2022/08/10",
"position": 62,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 60,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 49,
"query": "artificial intelligence hiring"
},
{
"date": "2022/08/10",
"position": 70,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 53,
"query": "artificial intelligence hiring"
},
{
"date": "2022/08/10",
"position": 49,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 44,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 48,
"query": "AI hiring"
},
{
"date": "2022/08/10",
"position": 47,
"query": "artificial intelligence hiring"
}
] |
Which Workers Are the Most Affected by Automation and What ...
|
Which Workers Are the Most Affected by Automation and What Could Help Them Get New Jobs?
|
https://www.gao.gov
|
[] |
Researchers estimate that anywhere from 9% to 47% of jobs could be automated in the future. To better understand the scope of automation's ...
|
Self-checkout at the grocery store, electronic record keeping, even tax preparation. Increasingly, technology is automating tasks previously performed by people. While automation technology has changed some jobs, it has eliminated others entirely.
Today’s WatchBlog post looks at our new report about which kinds of workers are most at risk of losing their jobs to automation, and what skills they need to get in-demand jobs.
You can also listen to our podcast with GAO’s Dawn Locke, an expert on workforce training and education, to learn more.
Image
Who is at risk of losing their job to automation?
Workers with lower levels of education and who perform routine tasks—think cashiers or file clerks—face the greatest risks of their jobs being automated. However, automation is likely to have widespread effects. Researchers estimate that anywhere from 9% to 47% of jobs could be automated in the future.
To better understand the scope of automation’s effects, federal agencies are working to gather more data on how automation will affect the workforce. For example, the Department of Labor is planning to gather information from industries such as retail trade, healthcare, and transportation and warehousing to learn more about how automation is affecting jobs.
What are the in-demand skills for in-demand jobs?
Workers impacted by automation may need new skills to adapt to changing job requirements or get a new job.
The Department of Labor’s data indicate that the skills needed for in-demand jobs (meaning those jobs projected to grow fastest in the next 10 years) will include a mix of:
soft skills—like interpersonal skills to successfully interact with people,
process skills that help a person acquire knowledge quickly—like active learning and critical thinking, and
specific technical expertise skills—like equipment maintenance.
The Department of Labor’s data also show that in-demand jobs with a greater number of “important” skills tend to require more education. Important skills include active listening, social perceptiveness, and critical thinking.
Skills Deemed Important in the Top 20 In-Demand Occupations, by Education Level
Image
What challenges do workers face in getting those skills?
While research indicates that some in-demand jobs with skills like judgement and management might be more resistant to automation, workers trying to grow their skills face challenges.
For example, workforce stakeholders we interviewed for our new report told us that training programs sometimes focus on helping people get a job quickly, which could lead to a short-term or low-wage job. Others told us that workers face challenges accessing programs—for example, finding childcare or being in a training program without having a way to still pay bills.
How can organizations help workers overcome those challenges?
Workforce stakeholders we interviewed had a number of suggestions to address these challenges. For example, some stakeholders said that training programs should focus on in-demand skills needed for high-growth jobs that are less likely to be automated.
Research also noted that training should build on workers’ existing skills to help them build skills toward high quality jobs. Other stakeholders said training programs should help workers obtain industry-recognized credentials. Stakeholders also suggested that providing wraparound services, like childcare, and offering financial support can help workers access training, though they acknowledged the cost of that help.
Learn more about our work on automation and workforce development by checking out our new report and podcast, and our Key Issue page on Employment in a Changing Economy.
| 2022-08-23T00:00:00 |
https://www.gao.gov/blog/which-workers-are-most-affected-automation-and-what-could-help-them-get-new-jobs
|
[
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 87,
"query": "robotics job displacement"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 83,
"query": "robotics job displacement"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 85,
"query": "robotics job displacement"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 87,
"query": "robotics job displacement"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 66,
"query": "robotics job displacement"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 89,
"query": "robotics job displacement"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 2,
"query": "automation job displacement"
},
{
"date": "2022/08/23",
"position": 1,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 3,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 3,
"query": "job automation statistics"
},
{
"date": "2022/08/23",
"position": 70,
"query": "robotics job displacement"
}
] |
|
Considering a MS/PhD in ML/AI. What is career path/salary? - Reddit
|
The heart of the internet
|
https://www.reddit.com
|
[] |
MS has highest average salary over the course of a person's life. PhDs have a lot of volatility, have seen people with starting offers of 400k+, others with ...
|
Currently working as a SWE, but thinking of getting a Master's in ML/AI in 2 years. Is it worth it to go back to school?
What is the typical salary of a ML engineer with a MS? What kind of work do they do? What is their career path and respective salary?
Additionally, what would be the benefits of pursuing a PhD in ML/AI? What kind of work do they do? What is their career path and respective salary?
| 2022-09-04T00:00:00 |
https://www.reddit.com/r/cscareerquestions/comments/x5dk5k/considering_a_msphd_in_mlai_what_is_career/
|
[
{
"date": "2022/09/04",
"position": 31,
"query": "artificial intelligence wages"
}
] |
|
Collaboration among recruiters and artificial intelligence: removing ...
|
Collaboration among recruiters and artificial intelligence: removing human prejudices in employment
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Zhisheng Chen",
"College Of Economics",
"Management",
"Nanjing University Of Aeronautics",
"Astronautics",
"General Avenue",
"Jiangning District",
"Nanjing"
] |
Further, the study analyzes that AI plays an important role in each stage of recruitment, such as recruitment promotion, job search, application ...
|
Abstract In the global war for talent, traditional recruiting methods are failing to cope with the talent competition, so employers need the right recruiting tools to fill open positions. First, we explore how talent acquisition has transitioned from digital 1.0 to 3.0 (AI-enabled) as the digital tool redesigns business. The technology of artificial intelligence has facilitated the daily work of recruiters and improved recruitment efficiency. Further, the study analyzes that AI plays an important role in each stage of recruitment, such as recruitment promotion, job search, application, screening, assessment, and coordination. Next, after interviewing with AI recruitment stakeholders (recruiters, managers, and applicants), the study discusses their acceptance criteria for each recruitment stage; stakeholders also raised concerns about AI recruitment. Finally, we suggest that managers need to be concerned about the cost of AI recruitment, legal privacy, recruitment bias, and the possibility of replacing recruiters. Overall, the study answers the following questions: (1) How artificial intelligence is used in various stages of the recruitment process. (2) Stakeholder (applicants, recruiters, managers) perceptions of AI application in recruitment. (3) Suggestions for managers to adopt AI in recruitment. In general, the discussion will contribute to the study of the use of AI in recruitment, as well as providing recommendations for implementing AI recruitment in practice. Keywords: Collaboration, Recruiters, Artificial intelligence, Human prejudices, Employment
Introduction Traditionally, organizations use lower technical approaches, for example, newspaper ads or referrals from employees, to draw in qualified applicants (Singh and Finn 2003). Traditional recruitment methods are now less efficient because it involves a substantial investment of time and does not always lead to optimal results (Ahmed and Reviews 2018; Edwards and Journal 2016). Since the late 1990s, the labor market began to face economic challenges that witnessed a high demand for highly skilled candidates (Abou Hamdan 2019). If organizations need to meet the needs of their customers in this competitive technological advantage, it is only possible by hiring talent (Nawaz and Engineering 2019). Hiring has changed from an essential human resource initiative to a major strategic concern for organizations because of the shift in talent as a provider of value and commercial advantage. In recent times, organizations have ranked attracting, selecting, and retaining talents as the primary strategic focus (Black and van Esch 2020b, a). At this time, e-recruitment systems are gradually growing, surpassing traditional methods (Enăchescu 2016). The use of AI tools becomes popular for recruiters in 2018 (Upadhyay and Khandelwal 2018). Talent selection must be carried out carefully for companies to ensure that organizational goals are reached. The determination associated with talent selection is a challenging task because it is restricted to the decision maker’s numeracy, vision, analytical skills, and internal bias (Abou Hamdan 2019). The new era of recruitment with the strength of artificial intelligence is enabling employers to tackle the challenges of hiring. The COVID-19 outbreak in 2020, offices are locked down, physical distance is maintained between people, and masks are worn. Because of the virtual office, traditional work methods are not as effective. While the virtual office offers a great deal of flexibility, it also poses several challenges for HR recruitment. These include how to schedule interviews, select the right candidates, and attract people to submit their resumes while avoiding face-to-face contact between people (Pan and Zhang 2020). We can use artificial intelligence to solve the challenges because AI can provide various services related to HRM practices (Chattopadhyay and Technology 2020). Today, companies are embarking on implementing “Digital Recruiting 3.0”. The shift crux is the AI application in the recruiting process (Black and van Esch 2020b, a). In any case, with AI, recruiters may handle large amounts of information to search for the right candidate. With AI support, recruiters also can reach beyond a candidate's personality traits and traditional resume to see whether it is a suitable match. Artificial intelligence is impartial and treats all candidates equally when screening resumes (Upadhyay & Khandelwal 2018). Its prevalence is based on the idea that AI recruiting tools can create a fair process and help achieve high-quality and optimal results in less time and cost than humans (Solascasas Morales 2020). AI system is revolutionizing the recruiting task by replacing the repetitive duties which were traditionally executed by professional recruiters in the past. However, possible conflicts of shared control between humans and autonomous systems. For example, the conflicts that may arise when drivers interact with AI-based support. Therefore, the interaction between AI and humans in different application domains must be extended with state-of-the-art technology. Competence-Availability-Possibility-to-act (CAP) defines shared control scenarios (Vanderhaegen 2021). CAP-based autonomy is decomposed into several scenarios of shared control within or between workspaces. A car driving application validates the relevance of the approach.
Background The definition of artificial intelligence The idea of artificial intelligence was started during the Second World War by the British mathematician and computer scientist Alan Turing. In 1950, Alan Turing pointed out that if the person working with a machine does not know whether he or she is interacting with the machine, then he or she will assume that it is intelligent (Stuart and Peter 2016). The term “artificial intelligence” was coined by John McCarthy in 1956. McCarthy and his colleagues drafted a proposal to the Rockefeller Foundation to fund their project to explore “the possibilities of intelligent machine implementation”. The term “artificial intelligence” first appeared in this proposal (Jain and Research 2018). However, a more comprehensive definition of artificial intelligence is possible. Artificial intelligence is a science that aims to design systems that can think like a human, have the ability to learn, and perform tasks that require human intelligence (Balaban and Kartal 2015). The purpose of so doing is to enable human actions to be accomplished by machines as well, such as perceiving, remembering, recalling, understanding, deducing, comparing, making decisions, thinking, making suggestions, or taking action (Shabbir and Anwer 2018). Artificial intelligence also shows how our brain works, and it is an expression of the code stored in our neurons (Palm 2012). AI Categories and contributions on cognitive workload There are three kinds of AI. Artificial Narrow Intelligence (ANI). This category of artificial intelligence, narrowly referred to as weak AI, focused on a specific product, service, or job (Frank et al. 2019); Artificial General Intelligence (AGI). The ability of this type of AI to mimic human cognitive activity is indistinguishable from humans (Strelkova 2017); Artificial Super Intelligence (ASI). Super-artificial intelligence does not only imitate human intelligence, but it has more intelligence than human systems. What currently exists is a narrow AI, but it will accumulate data in the next decade also with the rapid generation of other types of AI. Identifying the user’s cognitive workload is often used as a fundamental concern in human–computer systems when research focuses on “AI-specific” work. Humans have a limited capacity for processing information, such as short-term memory in the brain, thus we frequently use our surroundings to lighten our cognitive burden (Wilson 2002). AI assists humans in problem-solving and transfers human cognitive effort to the “global brain” (Barsalou 2014). For the goal of adaptation and personalization, information systems specifically examine cognitive effort in the area of human–computer interaction (Ren et al. 2012). Some researchers employ user perception to examine the cognitive workload of users and its variations (Gupta et al. 2013). Higher AI support often helps users work less cognitively. Job seekers with professional work experience were encouraged to role-play applying for a job in an interview scenario set up by Buettner (2013). The users in systems with greater AI help experienced lower levels of cognitive burden, according to the results of simulated interviews. Therefore, AI has some contribution to reducing human cognitive workload. Deep learning and neural network method A neural network is a type of computer model that uses a lot of artificial neurons connected together to simulate the structure and operation of a biological neural network. Neural network-based target identification methods are extremely desirable in the deep learning era. Deep learning aims to educate computers to perform tasks that come naturally to people. Deep learning, which enables autonomous cars to recognize stop signs and tell pedestrians from street light posts, is also a critical technology. In deep learning, computer models learn directly from images, text, or sound how to perform classification tasks directly. Deep learning models can achieve the highest levels of accuracy, sometimes even exceeding that of humans. We typically train models using a large amount of labeled data and a neural network architecture containing many layers. Neural network learning is closely related to the way we normally learn. We first complete a certain amount of work and get corrected by a trainer to do that work in a better way the next time. Using the difference between the actual value and the predicted value, an error value is calculated and sent back to the system. The error is examined for each layer of the network and utilized to modify the threshold and weights for the following input. The closer the actual value is to the projected value, the less the error. Thus as the network continues to learn, the error decreases with each run. This process is called backward propagation and is carried out continuously through the network until the error value is kept to a minimum. In Fig. 1 above, the four variables listed (e.g., basic information of the applicant’s experience, education, skills, responsibilities, etc.) are connected to the neurons via synapses. First, the system feeds new data into the input layer. In the hidden layers 1 and 2, respectively, calculate the node value. In the output layer, it calculates the output value and using the difference between the actual value and the predicted value, it calculates the value error and sends it back to the system (reverse propagation). This way, as the network continues to learn, the error is reduced for each run. Fig. 1. Open in a new tab Deep learning and neural network New technologies of the future bring a whole new imagination to existing information processing and artificial intelligence techniques, such as quantum computing. Quantum AI has led to the application of quantum computers in the field of artificial intelligence. Unlike the binary system processing used in classical computers, the base-3 system is introduced in quantum computers. In quantum computers, it is possible to select not only 1 or 0 bits but also both 1 and 0, resulting in a third state. As a result, the processing power of quantum computers is greatly improved. It can add the possibility of extreme computing power to AI. Quantum AI, as a field of interfusion between quantum computing and artificial intelligence, this direction can make both disciplines complement each other. Artificial intelligence can take advantage of the information processing of quantum computing to promote new types of AI algorithms, while quantum science can take advantage of deep learning techniques to achieve better manipulation of microscopic systems. Human–AI interaction process and usefulness Process automation enabled by robotics means automating work that operates according to processes with strong rules and a large number of recurring occurrences. In the recruitment field, intelligent automation technologies, comparable to mimicking the collaborative work of humans, lead to more complex recruitment tasks and settle in richer recruitment scenarios. To achieve intelligent robotics in the recruitment field, such as automatic recruitment, resume screening, interview chatbot, and smart interview scoring, it is not enough to rely on computer technology alone, but AI technology is needed to complete the “perception—cognition—execution” closed loop of human–AI interaction. AI technology includes natural language processing (NLP), optical character recognition (OCR), chatbot, intelligent decision-making, etc. AI technology may assist in providing business solutions, operations scheduling, and QC functions. Follow Fig. 2 illustrates human–AI interactions and usefulness in the recruiting field. Fig. 2. Open in a new tab Human–AI interaction process and usefulness Most of the human–AI interaction is performed through perception, including visual, auditory, and tactile information. Cognition mainly involves human cognitive processing (e.g., thinking, decision-making, learning), including experience cognition and thought cognition. Human–AI interaction interface design cannot be separated from human perception and cognitive characteristics. During the implementation process, defects are found through user feedback to improve the human–AI interaction. The robot works in an open environment and serves ordinary users such as job seekers or recruiters who expect flexible interaction rules. There are many uncertainties in the overall human–AI interaction process, and AI algorithms need to be combined with other technologies (e.g., Machine Sensing, Mechatronics) to meet users' expectations of intelligent robots. Further, Human–AI interaction can fully utilize humans as a general intelligence to compensate for AI through robot’s mobility and active interaction capability. AI’s interaction capability can significantly improve the overall service capability and promote the wide application of intelligent robots in the recruitment field. In future, with the improvement of algorithm capability and hardware technology, AI applications in the recruitment field will gradually expand. Digitalization of recruitment: from 1.0 to 3.0 era Before the mid to late 90s, typically, Job seekers must search for opportunities on job boards or in the newspaper (Black and Esch 2020b, a). Once they find a suitable job, they usually have to come to the company that posted the job opportunity, acquire a job application form, fill it out manually, and hand it in. However, it is difficult for information in job advertisements to reach all interested job seekers and the recruitment process makes recruiters vulnerable to cognitive biases. In addition, the number and speed of recruiting performed by human recruiters cannot be compared with AI (Judge et al. 2000). Digital Recruitment 1.0 era. In the mid to late 1990s, the way the Internet digitized work and candidate information breached the previous boundaries of information scope and richness (Black and Esch 2020b, a). A rich job description can be delivered to many potential employees with the least cost, as it does not require printing or shipping newspapers. Job seekers also do not need to search for print job information or take the time to mail many application forms. Digital Recruitment 2.0 era. Emerging ten years after Digital Recruitment 1.0 era began, it can afford to aggregate job postings on multiple independent job websites (Black and Esch 2020b, a). Job seekers do not have to visit and search every job board, and recruiters can find distinct job seekers across all job platforms, and they do not need to post job information on every platform. By 2015, with the increasing maturity of the Digital Recruitment 2.0 era, the Digital Recruitment 3.0 era has also been commercially applied. The emergence of the AI system has been a major feature of the Digital Recruitment 3.0 era (Kaplan and S 2018). AI software can understand speech, analyze emotions, recognize pictures, and then make decisions based on different criteria (Ahmed and Reviews 2018). Artificial intelligence software can automate recruitment tasks through algorithms. Algorithms and machine learning tools can quickly ingest data and identify patterns (Chichester Jr and Giffen 2019). Background factors for the emergence of Digital Recruiting 3.0 include: The incredible number of applications that Digital Recruitment 1.0 era and 2.0 era caused for each position (Maurer and Liu 2007). A dramatic drop in the cost of applying for jobs and a surge in the number of applicants for each position forced companies to spend more time reviewing all new applicants. There is widespread acceptance of the importance of talent. Research is also beginning to prove that high-quality talent can make a competitive difference when tangible assets are the primary source of a company's value (Paschen et al. 2020). This difference proves that finding the right candidate among a large pool of applicants is critical. As a whole, these intelligent “people” of the 3.0 Recruiting Era have transformed the traditional recruiting function. AI can help companies identify talented applicants and hire those who meet the job (People-Job Fit) and organizational (People-Organization Fit) requirements (Kristof-Brown 2010). It offers increasing advantages and potential for human resource management (HRM) by reducing and almost eliminating friction in the “job finding” and “people finding” processes.
Application of AI in the recruitment stage The recruiting procedure is considered a business one, in line with Davenport and Short. It covers every path of a candidate (Davenport and Short 1990). Artificial intelligence recruitment tools are used for six general activities: job advertisement, job search, application, selection, assessment, and coordination (Black and Esch 2020b, a). In the job advertisement promotion stage, companies try to identify candidates and present them with job opportunities. Faced with a multitude of job opportunities, artificial intelligence can help applicants analyze the entire career path and filter out the appropriate results from the career web portals (Laurim et al. 2021). Applicants may be required to fill out an electronic application form or send an electronic resume. After the applicants submit the applications, AI tools begin screening and assessing them. If candidates pass the preliminary screening and assessment, the employer needs to evaluate the candidates to decide who is the best fit for the position. The phase may include more than one round of evaluation, but the final goal is to find who is the best candidate. AI also may be leveraged to coordinate with candidates throughout the recruitment stage. Table 1 illustrates how AI can support the interaction between the candidates and the company during the recruiting stage. Table 1. The application of artificial intelligence tools in recruiting stage Recruitment stage AI application Job advertisements Recruiters can develop appropriate job ads and control online channels with the support of artificial intelligence to increase the total number of applicants and identify more suitable candidates Job searching AI tool may assist the job seeker to find a suitable job based on skills, geographical, and demographic data Application Application guides and digital helpers could handle the task of writing job applications for applicants Selection Resume analysis presents applicant data to the company in the best possible way. Artificial intelligence tools analyze the application and assess candidates and, thus, decide the match of a candidate Assessment Gamified tests and recorded videos may be assessed by AI for candidates Coordination? A self-learning chatbot could answer some questions of applicants or propose a suitable job Open in a new tab Recruitment promotion Job descriptions for ads posting can be completed with the help of artificial intelligence. It can help in developing job descriptions and specifications. Organizations can use artificial intelligence to update job descriptions to match the work being performed (People-Job Fit) (Kristof-Brown 2010). The company needs to find the most suitable candidate, so the recruitment promotion advertisement needs to be broad and specific. Companies hope to reach suitable and active applicants. However, most are not actively looking for work. In a sense, they are passive candidates. Usually, the number of passive candidates exceeds the number of active candidates. However, passive candidates will still consider a suitable work opportunity if it takes the initiative to show them (Smith and Kidder 2010). As the AI service accumulates experience, the AI tool may learn what external channels are most effective for each type of candidate. More specifically, AI associates the proper presentation method (for example, ad, text, and email) with the most excellent candidates. The artificial intelligence system releases job opportunities through pop-up ads, emails, banners, texts, etc. to get the best uptake and response from candidate profiles (Black and Esch 2020b, a). These are the tools to attract potential job seekers successfully (Jäger 2018). Artificial intelligence can be used for the presentation of job opportunities and precise job descriptions. Adjusting the ad wording and tracking the impact of these changes on the number of applications and applicants may help companies improve the effectiveness of their promotions. Additionally, AI can decide which aspects of a company, such as culture and accomplishments, should be showed to candidates to cause the most positive feedback. Artificial intelligence can assist companies to increase the pool of applicants and target more suitable ones. In 2017, Loreal used artificial intelligence to present job chances to active candidates and identify passive ones. As a result, it gained 2 million applications for 5000 positions (Black and van Esch 2020b, a). The increased number of applications gives companies more options. Many companies already have a pool of rejected candidates from the past (Kakatkar et al. 2020). While the fact that these rejected candidates are not a fit for previous jobs can not imply a mismatch with current positions. However, since this past candidate information is out of different information platforms, such as in local servers or third-party digital storage, the manual testing database would be too costly. Artificial intelligence tools can filter past candidates and match them with current positions (Black and Esch 2020b, a). Job Search Traditional job search websites are dependent on the entry of search terms. Artificial intelligence refines the search outcome on a wider and more refined foundation. Candidates may upload their resumes. The educational and professional qualifications required for the position are related to the candidate's maturity, qualifications and occupation, and location (Jäger 2018). In addition, job seekers who are interested in certain company positions can ask about it through the functional chatbot. This model makes it very interesting for job seekers when an intelligent chatbot is similar to digital assistants that can provide help to job seekers. Job applications Sophisticated AI parsing techniques adopt artificial neural networks and deep learning methods for text understanding (Petry 2018). Applicants upload their CVs, profile information is analyzed, and AI identifies which information must be transferred to which pre-structured data fields (Strohmeier and Piazza 2015). This technology reduces the workload of applicants and recruiters. A further approach is to fill out complex application forms through intelligent helper without the need for applicants to write and upload CVs (Verhoeven 2020). This method allows all application data to be made available online, and AI collects and sends data to the company's applicant management system. Screening and filtering out some candidates Automatic screening of resumes provides important support during the screening phase, as it reduces the number of errors that can occur when thousands of resumes are manually screened (Abou Hamdan 2019). The AI tools available to us are the “Resume Scorer” or “Optical Character Recognition” (OCR) (Laurim et al. 2021). A “Resume Scorer” is a tool that checks the skills or experience required for a particular job and matches it with the applicant's resume to screen out candidates who do not meet the requirements (Leong 2018). Artificial intelligence comes with OCR to search for keywords and match the applicant’s qualifications and job requirements (Dickson et al. 2010). It may be used to check parameters such as a candidate's skills, or salary expectations. Software controlled by artificial intelligence can identify hard skills and job-related soft ones and candidate personality characteristics. For example, companies can ask candidates to send in a video of themselves as part of the job application. The software analyzes a short video, explores the personality trait, and forms the analysis result (Escalante et al. 2017). Finally, the analysis results are compared with the competencies required for the recruitment, the organization’s values (Gupta et al. 2018), and the expected package, which leads to a better match between the candidate and the organization. When an individual's abilities, knowledge, and skills are aligned with the job requirements (P-J fit), they should perform better, are more possible to accept a job, and can continue to stay with the organization (Kristof-Brown 2010). Therefore, AI can enable companies to improve P-O and P-J fit. Studies have shown AI tools outperform humans in screening applicants by at least 25% (Kuncel et al. 2014). For example, Loreal used an AI screening tool to reduce the time to review resumes from 40 to 4 min (Black & van Esch 2020b, a). For some companies, reducing the time to hire key talent can gain a competitive advantage. The ability of artificial intelligence to decrease recruiting time represents an efficiency boost and is an advantage in the battle for talent. Post-screening assessment After a company has filtered out some candidates through screening, artificial intelligence evaluation may further narrow the pool. The evaluation may take many forms. Some are related to gamification tests provide a valuable understanding of skills, abilities, and traits. The evaluation game is implemented in the recruitment process to respond to the relationship between the performance of candidates in the game's success and in certain positions (Bersin and Chamorro-Premuzic 2019). Candidates who meet the job matching requirements in the game test can be scheduled for a final interview. Using a chatbot, artificial intelligence can support cognitive engagement by interactively asking candidate questions (Sharma 2018). The AI system then analyzes and compares the content of the candidate’s answers with those of the top-performing employees. AI also analyzes the vocabulary and sentence structure used in the answers and combines this with content analysis to create a total score for the candidate. The company can conduct final interviews with satisfied candidates and make a final selection decision. Video-recorded interviews analyzed by AI technology. When interviewing with the candidate, the AI-enabled system asked candidates some questions and they handed in the recorded responses. These questions come from the analysis of these past successful employees and the average employee. Based on this research, AI could analyze which competencies and traits were most possible to lead to success. AI analyzes the responses content, their wording, voice tone (e.g., being enthusiastic about the question), and facial movements (e.g., frowning when talking about previous jobs) (Black and van Esch 2020b, a), and correlated them with the responses of successful employees. The candidates may join in virtual interviews over a period of several days, on any day, or at any convenient time. Studies consistently show that the candidate has more positive arguments toward experiences where they can have much control over the recruitment procedure (Hamilton and Davison 2018). The AI-supported interview and assessments narrowed down the finalists. Coordination throughout the recruitment stage While AI-enabled recruitment outreach can generate a large number of applications, not everyone will end up getting hired. Companies need to ensure that all candidates, especially those who have been rejected, have a positive experience. Candidates, having had a positive experience when rejected, are more likely to remain open to other opportunities offered by the company (Swider et al. 2015). In addition, the rejected candidate's positive or negative attitudes can affect those around them, such as family and friends (Van Esch et al. 2014). If they have a very positive experience, they are still may recommend people they know to the company. Therefore, companies should construct a positive experience for candidates so that the rejected ones can promote positive word-of-mouth. For candidates who have been hired or rejected, the AI-supported system will make job applications smoother. With AI’s help, candidates do not have to fill out or hand in their resumes. By simply asking applicants to submit their profile, such as from LinkedIn, the AI system will intelligently sort through the candidate's profile and fill out the application for them (Black and van Esch 2020b, a). Once the candidate submits their application, the AI chatbot can answer the candidate's questions about the company, salary, career development, etc. In addition, it can also query the candidate for missing information in their application profile. Overall, an AI-enabled system may help companies expand their applicant amount, attract the right applicants, reach out to passive candidates, and increase the efficiency of the recruitment process.
Research methodology The objective of this study is to analyze the impact of AI on various participants in the overall recruitment process. These participants include recruiters, managers, and applicants. Data acquisition. Three target groups, including three recruiters, two managers, and ten applicants, were consulted about their perceptions of the AI recruitment process. Information about the respondents is presented in Table 2. 15 interviews were conducted in total. Table 2. Respondents’ information Respondent Basic information Personal Information Applicant 21–45 years old; 5 females, 5 males; 0–15 years of experience 3 Interns; 2 Sales Assistants; 2 Quality Engineer; 1 Finance Supervisor; 1 Assistant General Manager; 1 Sales Supervisor Recruiter 25–35 years old; 2 females; 1 male; 3–8 years of experience 2 Recruiters; 1 Senior Recruiter Manager 30–50 years old; 2 males; 8–22 years of experience 1 Vice President; 1 Sales Director Open in a new tab Respondents’ profile Recruiters: 3 people are responsible for the overall recruitment of the company. They are responsible for the whole process from recruitment advertisement promotion, applicant tracking, screening, and evaluation. Managers: 2 people from other departments and senior management. They are involved in the entire AI recruitment process and evaluate candidates that are relevant to their business based on their work experience. Applicants: 10 people, apply for different positions and come from different age groups. We can track candidates' experiences and feelings about AI recruitment. We adopted such a survey method as semi-structured interviews to ensure that participants could have enough discretion to express their whole experience toward AI recruitment and their expectations. The interview questions were as follows: First, each stakeholder was consulted about their experiences, feelings, and attitudes toward AI recruitment in general. Secondly, we set up the recruitment virtual scenario. After that, we invited them to conduct separate AI application scenarios during recruitment. Participants were asked how they felt about the application and recruitment process. The participants were asked about what requirements to meet for them to use AI tools; whether AI brought satisfactory services during their use; whether there was a recruitment function that could not be met; and what improvements needed to be made that could improve the AI recruitment function. Finally, we consulted managers, recruiters, and applicants about their suggestions and future expectations of AI as a recruiting assistant.
Stakeholder acceptance criteria In general, three groups of respondents agreed that AI is more useful for recruitment and job applications. In what follows, we explain the acceptance criteria for each recruitment stage by three groups. Table 3 describes the links between each stage of the recruitment process and the interviewees. Table 3. Acceptance criteria in the recruitment process Participants Recruitment stage Applicant Recruiter Manager Job advertisement The accuracy of the job description Job search Convenience and efficiency were used as criteria for this stage Application Application information was accurately parsed and conveyed Selection AI can accurately screen the desired talent instead of mistakenly discarding the potential talent Assessment The crucial aspect of this step is the fairness and impartiality of the AI-guided assessment as well as the trust-building Coordination The applicant can be treated as a real client and not just a user The accuracy of AI-based vacancy prediction Open in a new tab Job advertisement. The criterion used in this phase was the accuracy of the job description. The AI-enabled system in the making of job ads received a positive response from recruiters. This study indicates that recruiters’ motivation to adopt AI in daily operation is strongly influenced by AI’s ability to enhance recruiters’ job performance. Three recruiters were receptive to integrating AI-based software into their daily recruitment efforts. Recruiters found it difficult to design the job ads, mainly in terms of the description of the position elements, such as job content, duties, and requirements. Moreover, the recruiters are not necessarily proficient at writing job ads. AI can analyze and design the job advertisement text in multiple languages and meet the requirements of different positions, which is difficult for ordinary recruiters to accomplish. Job search. Convenience and efficiency were used as criteria for this stage. Several applicants used the chatbot and found its functionality to be positively evaluated. All applicants agreed that the chatbot was very interesting in finding job opportunities and that they were willing to attempt the feature. However, they also suggested that questions should be answered accurately by AI and that chatbots should ask questions that are more specific to job-related competencies and skills. This phase of the recruitment process also showed that chatbots can support candidates in many ways, but the applicant’s intention to use them depends on the convenience and efficiency of AI’s use. If applicants realize that AI can interact with them well and does not miss the necessary information, then to some extent, it is adapted to that stage of the job. According to the applicant, it takes time to accommodate the adoption of a chatbot, and of course, we have to fully trust the support provided by AI to build a trusting relationship between humans and machines. Application. Accurate data transfer to ensure that no talent information is missing is considered the criteria for this stage. Applicants can fill out and upload resumes, but a more advanced approach is for AI to assist in filling out complex application forms. Complex parsing techniques use deep learning and artificial neural networks ways to understand resume information (Petry 2018). After filling out the resume, the resume parser automatically transmits the data to the applicant tracking system. In the tracking system, there are pre-constructed data domains (Strohmeier and Piazza 2015). The parser identifies which components must be transferred to which data domains based on typical text modules. Applicants were surprised to witness that AI could automatically recognize the resumes they provided and even, in some instances, individuals’ profiles linked from social networking platforms. Applicants found AI assistance in filling out resumes to be speedy but were concerned that important application information would be lost in the data transmission. After several applicants filled out the job application form, the recruiter proceeded with the recruitment system tracking to compare whether the application information was accurately parsed and conveyed. AI’s resume parsing results satisfied the recruiter. Selection. This stage reflects the key is whether AI can accurately screen the desired talent instead of mistakenly discarding the potential talent. The algorithm of AI should be based on the principle of scientific empirical evaluation. AI builds a model for HR to screen resumes by analyzing the recruitment behavior of a large number of recruiters. In addition, AI combines current users' recruiting needs, company profiles, and candidate preferences to quickly screen a large number of resumes (İşgüzar and Ayden 2019). For recruiters to build trust in artificial intelligence, they will test artificial intelligence reliability by comparing AI decisions with recruiter decisions. AI reliability will make recruiters more confident in AI algorithms and they will choose to utilize them. AI plays an even more prominent role when there are many applicants, helping recruiters reject the unsuitable ones. AI not only screens out the right candidates for a job position but also identifies potential talent that is well suited to it. Applicants who are not suitable for the position currently applied for may be suitable for other positions in the company. Assessment. The crucial aspect of this step is the fairness and impartiality of the AI-guided assessment as well as the trust-building. Video interviewing is based on the analysis of gestures, the tone of voice, and micro-expressions in conjunction with video capture to complete the interview process (Merlin and Jayam 2018). Thus, it allows a holistic approach to exploring the suitability of the candidate for the job qualifications (Merlin and Jayam 2018). But there are some interpersonal relationships which artificial intelligence cannot perceive in the video. Moreover, recruiters are also concerned about the analysis criteria of video interviews with the help of artificial intelligence. Recruiters also believe that some candidates are not very adept at online gaming. Sometimes the lower scores were not due to their weaknesses in certain skills, but rather a lack of understanding of how to use the game’s features. Recruiters and managers also had concerns that some candidates had mastered AI questioning and had consciously avoided certain question traps to achieve high scores. Managers expressed low trust in AI, based on their limited experience with AI. One manager said he would rather spend time looking at a resume than accept the analysis provided by AI. Coordination. AI systems can automatically schedule calls, tests, and interviews (Kulkarni et al. 2019). This process reflects the principle of whether the applicant can be treated as a real client and not just a user. Applicants overall responded to a more positive feeling and hope to recommend people. However, applicants also mentioned that when communicating with the chatbot, it did not feel as natural as communicating with a genuine human, and the communication was rather stiff, like chatting with a machine. Therefore, the AI system needs to strengthen its algorithms and continuously optimize its communication. Vacancy prediction software may estimate the likelihood of employees leaving the company by interpreting their behavioral data. It creates job vacancy alerts and alerts recruiters when to advertise a job (Klucin 2020). However, recruiters are concerned about the accuracy of AI-based vacancy prediction, and they suggest that the algorithmic capabilities of AI tools need to be continuously optimized.
Stakeholder suggestions Candidates believe that AI recruitment tools do offer certain benefits, for example, increased interactive experience and faster application process. Recruiters and managers realized the potential of AI recruitment such as faster recruiting, high-quality hiring, decreasing workload, and reducing discrimination. However, participants also highlighted the importance of the criteria in each stage of the recruitment process, otherwise, companies would be afraid of not being able to recruit effectively through the AI system. Recruiters and managers suggested that AI decisions need match human hypothetical decisions. They believed that it is easier to trust the artificial intelligence tool if it is testable and assessable based on previous successful hiring experiences within the company. Also, recruiters and managers emphasized why AI systems decide whether an applicant is rejected or selected, which would provide more judgment and thus improve the chances of successfully using the AI tool. Furthermore, recruiters and managers indicated that the ranking of candidate scores provided by the AI system should not be a final decision to give people a sense of more control over the AI hiring process. The controllability also helps people adopt the AI tool. AI developers should provide a basis for corrective action to review and modify AI’s inappropriate decisions and provide a fair judgment for future AI-enabled recruitment. Applicants should understand the benefits of AI tools before applying for a job, otherwise, applicants will not have a strong desire to interact through technology such as chatbots. Furthermore, refinement of AI recruitment functions, as well as the transparency of how the system works, would be important factors in successful AI using by applicants. Based on this survey, we propose the need for AI governance. For example, AI must learn only what it is supposed to learn to reduce the possibility that AI may be imbued with bias; people need mechanisms to increase flexibility and control over the whole recruitment process. To avoid bias in the AI decision-making process, we make the following recommendations: the ranking of candidates provided by the artificial intelligence system is not the final decision to avoid bias in the decision-making system; humans, not machines, have the final decision on candidates; empirical evaluation and comparison with past recruitment data should be conducted before implementing AI recruitment; periodic training on machine learning improves the accuracy of the system (Gupta et al. 2018).
Implications for management The cost of AI recruitment AI recruiting systems in the 3.0 era are new and challenging. Attempting to implement the system at all levels of employees from the start can increase the cost burden. According to a survey, 68% of HR professionals agree that the main reason they have not implemented an AI recruitment system is that they do not have enough budget to invest (Solascasas Morales 2020). IBM's research team of 6000 people, which included senior HR managers, CEO, and employees, highlighted the fact that the majority of people believe that AI is needed, but they are not ready for the structural transformation (Eric et al. 2017). Some AI tool use can bring excessive costs. For example, candidates play the game for fun and may not take it seriously, and the score will not be a fully accurate predictor for their job performance, so recruitment costs will increase (Bersin and Chamorro-Premuzic 2019). We suggest that companies with ideas for large-scale implementations of AI recruitment systems should be cautious, as numerous studies have found that 60–80% of large organizational changes, such as digital transformation, suffer setbacks (Black & Gregersen 2013). Thus, companies may economically and effectively apply AI recruiting tools in phases that will create low to medium job or large staffing needs. AI-enabled recruitment bias Although AI can help in decision-making, the datasets and algorithms that guide AI may be influenced by human biases. Human decision-makers sometimes make intuitive decisions based on “a set of tacit preferences” (Shrestha et al. 2019). Research on artificial intelligence decision-making suggests that bias is one of the challenges in developing artificial intelligence (Kaplan and Haenlein 2020; Martin 2019). AI-driven Job advertisements automatically have some indirect discrimination (Dalenberg and review 2018). Artificial intelligence needs to provide employers with the ability to adjust advertisements according to factors, such as age, gender, language, education, experience, and relationship status. Therefore, these factors determine whether AI-driven job advertisement activities are discriminatory or not. Discriminatory advertisements reduce the opportunities of job seekers with workplace diversity and violate the principle of equality to some extent (Abou Hamdan 2019). Companies should provide transparency about the algorithm development process, and the training of program developers to prevent unconscious bias (Miller et al. 2018). Artificial intelligence does not know what bias is or whether it is learning bias (Black and van Esch 2020b, a). In the process of machine learning (ML) conducted by AI, HR practitioners, or managers, inappropriate assumptions can lead to biased decisions. For example, a manager's past decisions may lead to anchoring bias (Edwards and Rodriguez 2019). Especially when developers design artificial intelligence tools by observing present high-performing employees for identifying core competencies and personalities of potential candidates (Neubert and Montañez 2020). If some biases like gender, education, race, and age existed in the past and they are present in the high performers that the company currently uses as benchmarks, the algorithm would learn these conventions and perpetuate the biases (Lee and Shin 2020). It was recently reported that Amazon's AI hiring tool showed bias against women (Fernandez 2019). Artificial intelligence systems are created, directed, and trained by humans. AI developers need to code algorithms to be neutral concerning gender, race, color, religion, and ethnicity. With the potential for unconscious bias in the past, it is required to deliberately neutralize these biases by freeing AI to learn and emphasize new models and algorithmic inputs to ensure that AI recruitment tools are aligned with HR strategies (Weinstein 2012). As AI faces increasing discrimination challenges, companies should lead through non-discrimination laws to ensure that the target group meets the job requirements. Data privacy issues under the law To develop artificial intelligence for recruitment decisions, a large amount of data need to be collected from numerous sources. These data can come from internal or external sources, or both (Akerkar 2019). The use of external datasets may pose some legal privacy challenges (Chichester Jr and Giffen 2019). Extracting additional information can also lead to legal, ethical, and privacy issues (Akerkar 2019). We recommend AI system developers should be aware of the legal requirements in data acquisition and avoid violating the related law. However, as soon as laws are passed or people begin to dramatically limit their data on media, this may seriously influence the efficiency of AI outreach tools. Threat or support for HR recruitment positions There is a discussion about whether AI would pose a barrier or threat to the current recruiter's job (Hogg 2019). HR professionals may consider the artificial intelligence recruitment system as a threat to recruiters’ jobs and, therefore, they may not actively pursue AI recruitment tool applications. AI tools are integrated with the recruitment process to support human recruiters in the selection of applicants without replacing the role of humans. Artificial intelligence systems are a relief for less valuable tasks. Time-consuming administrative duties, such as sourcing and screening candidates, will be delegated to AI technology. It can also be a means of releasing HR departments for higher-value tasks, such as strategic HR matters, supply and demand planning, or specialized sourcing of top talent (Bhalgat 2019; Black and van Esch 2020b, a). The support of AI tools allows HR employees will be able to invest more time in thinking, creativity, and interpersonal relationships (Ahmed and Reviews 2018; George and Thomas 2019). Language bias and cultural understanding are challenges for AI. AI cannot have human abilities, such as persuasion, building relationships with candidates, and convincing them to stay with you (Guenole and Feinzig 2018). Artificial intelligence is proficient at identifying talent, but activities such as assessing cultural fit still require humans to do so (Soleimani et al. 2021; Upadhyay and Khandelwal 2018). It cannot understand cultural barriers and interact with another person better than humans can do (Nawaz 2019). Therefore, there are misconceptions about AI replacing the human workforce. Artificial intelligence software and automation take employees out of their workload and allows them to be more strategic and productive. Cooperation and competition principles The concepts of cooperation and competition must be taken into consideration to enable human behavior analysis, manage online dangers, and build effective automated aids (such as artificial intelligence) (Vanderhaegen et al. 2006). While competition corresponds to human unreliability support since each individual seeks to make the others fail, cooperation is perceived as human reliability support since the human operators participating wish to take action successfully. AI-based recruiting aims to design such AI tools to support quality control of talent selection. The HR work environment in an organization is a shared workplace in which other HR professionals may perceive comparable or dissimilar interactions. In such AI-based recruitment environments, recruiters may undertake actions to facilitate collaborative activities among themselves and others. As stated by Millot and Hoc (1997), two human operators (e.g., two recruiters) are cooperative if each attempts to manage interference to promote his activity and the activity of the other, otherwise, they are competitive. Further, the design of “cooperative” robots must also be a priority when investigating human–computer interaction in recruiting from a cognitive standpoint (Sarter et al. 1997). A partially autonomous AI agent can be thought of as a new team member. The development of new coordination requirements comes from treating human and automated agents as cooperative systems. Solutions that build on the principles of human–machine cooperation are provided to keep the human in the recruiting process control loop and to define different levels of involvement based on the level of automation. Possible conflicts of shared control Possible conflicts of shared control exist between humans and autonomous systems. The interaction between AI and humans in different application domains must be extended with state-of-the-art technology. Vanderhaegen (2021) proposes a heuristic-based, forward-looking approach to identify potential conflicts in shared governance between humans and autonomous systems. This approach uses three elements of Competence-Availability-Possibility-to-act (CAP), which reflects the autonomy features of decision-makers. CAP-based autonomy is decomposed into several control scenarios that are shared within or across workspaces. This heuristic-based approach consists of four main steps: testing shared control, defining detection parameters, identifying potentially conflicting decisions, and testing conflicts. Heuristics are useful for identifying the sources of human–machine conflict. This approach involves a joint management process of human and autonomous systems. The user's awareness of such conflicts is improved through technological learning, improvement of warning systems, and the avoidance of confusion between human and machine intentions. When a system can manage a process without assistance from people, it is said to be autonomous (Scharre 2015). However, some duties may conflict with or be delegated to humans. Therefore, joint control mechanisms between people and autonomous AI systems must be developed. In accordance with their degree of autonomy, humans and autonomous AI systems are each granted a certain amount of control over a particular process, according to the shared control concept (Vanderhaegen 1999). Human–AI interface tools that may combine several modalities, such as visual, auditory, tactile, and conversational, are necessary for the potential of such interaction. They serve as instruments for controlling distributional choices made by autonomous systems and people. Depending on the potential evolution of CAP parameters, the CAP-oriented joint control between humans and AI systems may be static or dynamic. Using CAP parameters, a technique is used to find AI application scenarios that divide control duties between people and AI systems. They can be used in a study on recruiting automation. Humans and autonomous systems may have misunderstandings or be confused, and some disputes may result in adverse circumstances. For example, when a candidate participates in a test conducted by an AI tool through extensive simulation interviewing training or computer applications with testing skills and receives a high score, the AI recommends these to the candidate pool. This behavior is not consistent with the assumptions of the recruiter or the company. Because people expect to recruit really qualified people. At this point, the machine has made an error in judgment; the AI is unfairly evaluating the candidate’s based on specific historical data (with some bias or prejudice), such as the AI’s belief that the technological field is more suitable for men than women in some company recruiting. In fact, the historical data itself are sourced from technology companies with an imbalance between men and women, which are looking to recruit some women to balance the company’s gender ratio. By identifying potential conflicts in shared control, the CAP-based approach is an effective and cutting-edge way to optimize the design of shared control processes between humans and autonomous systems. There are three techniques to manage task-sharing disputes between people and autonomous systems or to increase the autonomy of the systems: create online learning platforms that will give autonomous systems a greater sense of autonomy and make them more receptive to human habits. For instance, if some job candidates are not accustomed to utilizing an intelligent recruiting interface, this information must be incorporated into an AI-based recruitment system; a sophisticated alert system must also be developed to manage human attention. A chatbot session system based on intelligent interviewing would be appropriate; Users might be made aware of these conflicts using tutorials about the application of AI-based tools for recruitment. Limitations of the study and future research Although AI research results provide important contributions to theory and practice, we acknowledge that the study has certain limitations. The theoretical literature should be explored in more depth, such as the machine learning principles and algorithmic rules for AI recruitment; the data and scope of the quantitative study are not sufficient. The study should be extended to different companies or different countries to increase the number of respondents and the variability. We can also conduct future studies: group level or executive understanding and the perception of AI recruitment; performance of employees who join through the AI recruitment tool compared to human recruitment; development of operational guidelines and training materials to guide users in conjunction with AI developers; and comparison of attitudes toward AI recruitment among candidates from different positions and job titles.
Conclusion This study follows an application–criteria–attention approach. It analyzes and develops a paradigm for the application of AI tools in the recruitment process. Criteria for successful AI-based recruitment are also suggested. Managerial focuses on AI-based recruitment have been raised, such as fairness, legal privacy, and cost issues. In addition, this study presents that identifying the user’s cognitive workload is used as a fundamental concern in human–computer recruiting systems. The analysis points out that higher AI support often helps recruiters work less cognitively. Moreover, the study states that cooperation and competition must be taken into consideration to enable human behavior analysis, manage online dangers, and build effective automated aids. The study indicates that possible conflicts of shared control exist between humans and autonomous recruiting systems. The interaction between AI and humans in the recruiting process is extended with state-of-the-art technology, which uses three elements of Competence-Availability-Possibility-to-act (CAP). The user’s awareness of such conflicts is improved through technological learning, improvement of warning systems, and the avoidance of confusion between human and machine intentions.
Acknowledgements The authors would like to acknowledge the project team and the collaboration effort of the wider project team, which included Professor Sun Jie and Ms. Han Ying.
Author contributions Zhisheng Chen wrote the main manuscript text . The author reviewed the manuscript.
Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.
Declarations Competing interests The authors declare no competing interests. Conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Ethical approval No animals or humans were involved in the study.
| 2022-09-28T00:00:00 |
2022/09/28
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC9516509/
|
[
{
"date": "2022/09/28",
"position": 52,
"query": "artificial intelligence hiring"
},
{
"date": "2022/09/28",
"position": 49,
"query": "artificial intelligence hiring"
},
{
"date": "2022/09/28",
"position": 50,
"query": "artificial intelligence hiring"
},
{
"date": "2022/09/28",
"position": 48,
"query": "artificial intelligence hiring"
},
{
"date": "2022/09/28",
"position": 52,
"query": "artificial intelligence hiring"
},
{
"date": "2022/09/28",
"position": 47,
"query": "artificial intelligence hiring"
},
{
"date": "2022/09/28",
"position": 50,
"query": "artificial intelligence hiring"
},
{
"date": "2022/09/28",
"position": 50,
"query": "artificial intelligence hiring"
},
{
"date": "2022/09/28",
"position": 48,
"query": "artificial intelligence hiring"
},
{
"date": "2022/09/28",
"position": 48,
"query": "artificial intelligence hiring"
},
{
"date": "2022/09/28",
"position": 44,
"query": "artificial intelligence hiring"
}
] |
Artificial Intelligence in Health Care: Benefits and Challenges of ...
|
Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics
|
https://www.gao.gov
|
[] |
Machine learning technologies can help identify hidden or complex patterns in diagnostic data to detect diseases earlier and improve treatments.
|
What GAO Found
Several machine learning (ML) technologies are available in the U.S. to assist with the diagnostic process. The resulting benefits include earlier detection of diseases; more consistent analysis of medical data; and increased access to care, particularly for underserved populations. GAO identified a variety of ML-based technologies for five selected diseases — certain cancers, diabetic retinopathy, Alzheimer’s disease, heart disease, and COVID-19 —with most technologies relying on data from imaging such as x-rays or magnetic resonance imaging (MRI). However, these ML technologies have generally not been widely adopted.
Academic, government, and private sector researchers are working to expand the capabilities of ML-based medical diagnostic technologies. In addition, GAO identified three broader emerging approaches—autonomous, adaptive, and consumer-oriented ML-diagnostics—that can be applied to diagnose a variety of diseases. These advances could enhance medical professionals’ capabilities and improve patient treatments but also have certain limitations. For example, adaptive technologies may improve accuracy by incorporating additional data to update themselves, but automatic incorporation of low-quality data may lead to inconsistent or poorer algorithmic performance.
Spectrum of adaptive algorithms
We identified several challenges affecting the development and adoption of ML in medical diagnostics:
Demonstrating real-world performance across diverse clinical settings and in rigorous studies.
Meeting clinical needs, such as developing technologies that integrate into clinical workflows.
Addressing regulatory gaps, such as providing clear guidance for the development of adaptive algorithms.
These challenges affect various stakeholders including technology developers, medical providers, and patients, and may slow the development and adoption of these technologies.
GAO developed three policy options that could help address these challenges or enhance the benefits of ML diagnostic technologies. These policy options identify possible actions by policymakers, which include Congress, federal agencies, state and local governments, academic and research institutions, and industry. See below for a summary of the policy options and relevant opportunities and considerations.
Policy Options to Help Address Challenges or Enhance Benefits of ML Diagnostic Technologies
Opportunities Considerations Evaluation (report
page 28) Policymakers could create incentives, guidance, or policies to encourage or require the evaluation of ML diagnostic technologies across a range of deployment conditions and demographics representative of the intended use. This policy option could help address the challenge of demonstrating real world performance. Stakeholders could better understand the performance of these technologies across diverse conditions and help to identify biases, limitations, and opportunities for improvement.
Could inform providers' adoption decisions, potentially leading to increased adoption by enhancing trust.
Information from evaluations can help inform the decisions of policymakers, such as decisions about regulatory requirements. May be time-intensive, which could delay the movement of these technologies into the marketplace, potentially affecting patients and professionals who could benefit from these technologies.
More rigorous evaluation will likely lead to extra costs, such as direct costs for funding the studies. Developers may not be incentivized to conduct these evaluations if it could show their products in a negative light, so policymakers could consider whether evaluations should be conducted or reviewed by independent parties, according to industry officials. Data Access (report
page 29) Policymakers could develop or expand access to high-quality medical data to develop and test ML medical diagnostic technologies. Examples include standards for collecting and sharing data, creating data commons, or using incentives to encourage data sharing. This policy option could help address the challenge of demonstrating real world performance. Developing or expanding access to high-quality datasets could help facilitate training and testing ML technologies across diverse and representative conditions. This could improve the technologies' performance and generalizability, help developers understand their performance and areas for improvement, and help to build trust and adoption in these technologies.
Expanding access could enable developers to save time in the development process, which could shorten the time it takes for these technologies to be available for adoption. Entities that own data may be reluctant to share them for a number of reasons. For example, these entities may consider their data valuable or proprietary. Some entities may also be concerned about the privacy of their patients and the intended use and security of their data.
Data sharing mechanisms may be of limited use to researchers and developers depending on the quality and interoperability of these data, and curating and storing data could be expensive and may require public and private resources. Collaboration (report
page 30) Policymakers could promote collaboration among developers, providers, and regulators in the development and adoption of ML diagnostic technologies. For example, policymakers could convene multidisciplinary experts together in the design and development of these technologies through workshops and conferences. This policy option could help address the challenges of meeting medical needs and addressing regulatory gaps. Collaboration between ML developers and providers could help ensure that the technologies address clinical needs. For example, collaboration between developers and medical professionals could help developers create ML technologies that integrate into medical professionals' workflows, and minimize time, effort, and disruption.
Collaboration among developers and medical providers could help in the creation and access of ML ready data, according to NIH officials. As previously reported, providers may not have time to both collaborate with developers and treat patients; however, organizations can provide protected time for employees to engage in innovation activities such as collaboration.
If developers only collaborate with providers in specific settings, their technologies may not be usable across a range of conditions and settings, such as across different patient types or technology systems.
Source: GAO. | GAO-22-104629
Why GAO Did This Study
Diagnostic errors affect more than 12 million Americans each year, with aggregate costs likely in excess of $100 billion, according to a report by the Society to Improve Diagnosis in Medicine. ML, a subfield of artificial intelligence, has emerged as a powerful tool for solving complex problems in diverse domains, including medical diagnostics. However, challenges to the development and use of machine learning technologies in medical diagnostics raise technological, economic, and regulatory questions.
GAO was asked to conduct a technology assessment on the current and emerging uses of machine learning in medical diagnostics, as well as the challenges and policy implications of these technologies. This report discusses (1) currently available ML medical diagnostic technologies for five selected diseases, (2) emerging ML medical diagnostic technologies, (3) challenges affecting the development and adoption of ML technologies for medical diagnosis, and (4) policy options to help address these challenges.
GAO assessed available and emerging ML technologies; interviewed stakeholders from government, industry, and academia; convened a meeting of experts in collaboration with the National Academy of Medicine; and reviewed reports and scientific literature. GAO is identifying policy options in this report.
For more information, contact Karen L. Howard at (202) 512-6888 or [email protected].
| 2022-09-29T00:00:00 |
https://www.gao.gov/products/gao-22-104629
|
[
{
"date": "2022/09/29",
"position": 20,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 35,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 19,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 17,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 19,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 20,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 20,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 19,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 19,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 20,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 18,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 19,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 20,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 20,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 18,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 19,
"query": "artificial intelligence healthcare"
},
{
"date": "2022/09/29",
"position": 19,
"query": "artificial intelligence healthcare"
}
] |
|
Eighteen pitfalls to beware of in AI journalism - AI Snake Oil
|
Eighteen pitfalls to beware of in AI journalism
|
https://www.aisnakeoil.com
|
[
"Sayash Kapoor"
] |
Eighteen pitfalls in AI journalism · Flawed human-AI comparison · Hyperbolic, incorrect, or non-falsifiable claims about AI · Uncritically ...
|
Reporting about AI is hard. Companies hype their products and most journalists aren’t sufficiently familiar with the technology. When news articles uncritically repeat PR statements, overuse images of robots, attribute agency to AI tools, or downplay their limitations, they mislead and misinform readers about the potential and limitations of AI.
We noticed that many articles tend to mislead in similar ways, so we analyzed over 50 articles about AI from major publications, from which we compiled 18 recurring pitfalls. We hope that being familiar with these will help you detect hype whenever you see it. We also hope this compilation of pitfalls will help journalists avoid them.
We were inspired by many previous efforts at dismantling hype in news reporting on AI by Prof. Emily Bender, Prof. Ben Shneiderman, Lakshmi Sivadas and Sabrina Argoub, Prof. Emily Tucker, and Dr. Daniel Leufer et al.
You can download a PDF checklist of the 18 pitfalls with examples here.
Example 1: “The Machine Are Learning, and so are the students” (NYT)
We identified 19 issues in this article, which you can read here.
In December 2019, NYT published a piece about educational technology (EdTech) product called Bakpax. It is a 1,500-word, feature-length article that provides neither accuracy, balance, nor context.
It is sourced almost entirely from company spokespeople, and the author borrows liberally from Bakpax's PR materials to exaggerate the role of AI. To keep the spotlight on AI, the article downplays the human labor from teachers that keeps the system running—such as developing and digitizing assignments.
Bakpax shut down in May 2022.
This is hardly a surprise: EdTech is an overhyped space. In the last decade, there have been hundreds of EdTech products that claim to "revolutionize" education. Despite billions of dollars in funding, many of them fail. Unfortunately, the article does not provide any context about this history.
A GIF of our annotations showing the prevalence of issues throughout the article. Each annotation represents a pitfall. You can read the entire annotated article here .
Example 2: “AI may be as effective as medical specialists at diagnosing disease” (CNN)
We identified 9 issues in this article, which you can read here.
In September 2019, CNN published this article about an AI research study. It buries the lede, seemingly intentionally: the spotlight is on the success of AI tools in diagnosis, whereas the study finds that fewer than 1% of papers on AI tools follow robust reporting practices. In fact, an expert quoted at the end of the article stresses that this is the real message of the study.
Besides, the cover image of the article is a robot arm shaking hands with a human, even though the study is about finding patterns in medical images. These humanoid images give a misleading view of AI, as we’ve described here.
Example 3: “AI tested as university exams undergo digital shift” (FT)
You can read the annotated article here.
Many schools and universities have adopted remote proctoring software during the COVID-19 pandemic. These tools suffer from bias and lack of validity, enable surveillance, and raise other concerns. There has been an uproar against remote proctoring from students, non-profits, and even senators.
In November 2021, the Financial Times published an article on a product called Sciolink that presents an entirely one-sided view of remote proctoring. It almost exclusively quotes the creators of Sciolink and provides no context about the limitations and risks of remote proctoring tools.
Eighteen pitfalls in AI journalism
We analyzed over 50 news stories on AI from 5 prominent publications: The New York Times, CNN, Financial Times, TechCrunch, and VentureBeat.
We briefly discuss the 18 pitfalls below; see our checklist for more details and examples.
Flawed human-AI comparison
What? A false comparison between AI tools and humans that implies AI tools and humans are similar in how they learn and perform.
Why is this an issue? Rather than describing AI as a broad set of tools, such comparisons anthropomorphize AI tools and imply that they have the potential to act as agents in the real world.
Pitfall 1. Attributing agency to AI: Describing AI systems as taking actions independent of human supervision or implying that they may soon be able to do so.
Pitfall 2. Suggestive imagery: Images of humanoid robots are often used to illustrate articles about AI, even if the article has nothing to do with robots. This gives readers a false impression that AI tools are embodied, even when it is just software that learns patterns from data.
Pitfall 3. Comparison with human intelligence: In some cases, articles on AI imply that AI algorithms learn in the same way as humans do. For example, comparisons of deep learning algorithms with the way the human brain functions are common. Such comparisons can lend credence to claims that AI is “sentient”, as Dr. Timnit Gebru and Dr. Margaret Mitchell note in their recent op-ed.
Pitfall 4. Comparison with human skills: Similarly, articles often compare how well AI tools perform with human skills on a given task. This falsely implies that AI tools and humans compete on an equal footing—hiding the fact that AI tools only work in a narrow range of settings.
Hyperbolic, incorrect, or non-falsifiable claims about AI
What? Claims about AI tools that are speculative, sensational, or incorrect can spread hype about AI.
Why is this an issue? Such claims give a false sense of progress in AI and make it difficult to identify where true advances are being made.
Pitfall 5. Hyperbole: Describing AI systems as revolutionary or groundbreaking without concrete evidence of their performance gives a false impression of how useful they will be in a given setting. This issue is amplified when AI tools are deployed in a setting where they are known to have past failures—we should be skeptical about the effectiveness of AI tools in these settings.
Pitfall 6. Uncritical comparison with historical transformations: Comparing AI tools with major historical transformations like the invention of electricity or the industrial revolution is a great marketing tactic. However, when news articles adopt these terms, they can convey a false sense of potential and progress—especially when these claims are not backed by real-world evidence.
Pitfall 7. Unjustified claims about future progress: Claims about how future developments in AI tools will affect an industry, for instance, by implying that AI tools will inevitably be useful in the industry. When these claims are made without evidence, they are mere speculation.
Pitfall 8. False claims about progress: In some cases, articles include false claims about what an AI tool can do.
Pitfall 9. Incorrect claims about what a study reports: News articles often cite academic studies to substantiate their claims. Unfortunately, there is often a gap between the claims made based on an academic study and what the study reports.
Pitfall 10. Deep-sounding terms for banal actions: As Prof. Emily Bender discusses in her work on dissecting AI hype, using phrases like “the elemental act of next-word prediction” or “the magic of AI” implies that an AI tool is doing something remarkable. It hides how mundane the tasks are.
Uncritically platforming those with self-interest
What? News articles often use PR statements and quotes from company spokespeople to substantiate their claims without providing adequate context or balance.
Why is this an issue? Emphasizing the opinions of self-interested parties without providing alternative viewpoints can give an over-optimistic sense of progress.
Pitfall 11. Treating company spokespeople and researchers as neutral parties: When an article only or primarily has quotes from company spokespeople or researchers who built an AI tool, it is likely to be over-optimistic about the potential benefits of the tool.
Pitfall 12. Repeating or re-using PR terms and statements: News articles often re-use terms from companies’ PR statements instead of describing how an AI tool works. This can misrepresent the actual capabilities of a tool.
Limitations not addressed
What? The potential benefits of an AI tool are emphasized, but the potential limitations are not addressed or emphasized.
Why is this an issue? A one-sided analysis of AI tools can hide the potential limitations of these tools.
Pitfall 13. No discussion of potential limitations: Limitations such as inadequate validation, bias, and potential for dual-use plague most AI tools. When these limitations are not discussed, readers can get a skewed view of the risks associated with AI tools.
Pitfall 14. Limitations de-emphasized: Even if an article discusses limitations and quotes experts who can explain them, limitations are often downplayed in the structure of the article, for instance by positioning them at the end of the article or giving them limited space.
Pitfall 15. Limitations addressed in a “skeptics” framing: Limitations of AI tools can be caveated in the framing of the article by positioning experts who explain these limitations as skeptics who don’t see the true potential of AI. Prof. Bender discusses this issue in much more detail in her response to an NYT Mag article.
Pitfall 16. Downplaying human labor: When discussing AI tools, articles often foreground the role of technical advances and downplay all the human labor that is necessary to build the system or keep it running. The book Ghost Work by Dr. Mary L. Gray and Dr. Siddharth Suri reveals how important this invisible labor is. Downplaying human labor misleads readers into thinking that AI tools work autonomously, instead of clarifying that they require significant overhead in terms of human labor, as Prof. Sarah T. Roberts discusses.
Pitfall 17. Performance numbers reported without uncertainty estimation or caveats: There is seldom enough space in a news article to explain how performance numbers like accuracy are calculated for a given application or what they represent. Including numbers like “90% accuracy” in the body of the article without specifying how these numbers are calculated can misinform readers about the efficacy of an AI tool. Moreover, AI tools suffer from performance degradations under even slight changes to the datasets they are evaluated on. Therefore, absolute performance numbers can mislead readers about the efficacy of these tools in the real world.
Pitfall 18. The fallacy of inscrutability: Referring to AI tools as inscrutable black boxes is a category error. Instead of holding the developers of these tools accountable for their design choices, it shifts scrutiny to the technical aspects of the system. Journalists should hold developers accountable for the performance of AI tools rather than referring to these tools as black boxes and allowing developers to evade accountability.
While these 18 pitfalls appear in the articles we analyzed, there are other issues that go beyond individual articles. For instance, when narratives of AI wiping out humanity are widely discussed, they overshadow current real-world problems such as bias and the lack of validity in AI tools. This underscores that news media must play its agenda-setting role responsibly.
We thank Klaudia Jaźwińska, Michael Lemonick, and Karen Rouse for their feedback on a draft of this article. We re-used the code from Molly White et al.’s The Edited Latecomers’ Guide to Crypto to generate our annotated articles.
Leave a comment
| 2022-09-30T00:00:00 |
https://www.aisnakeoil.com/p/eighteen-pitfalls-to-beware-of-in
|
[
{
"date": "2022/09/30",
"position": 75,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 70,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 67,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 67,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 69,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 68,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 68,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 67,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 69,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 69,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 67,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 69,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 73,
"query": "AI journalism"
},
{
"date": "2022/09/30",
"position": 62,
"query": "AI journalism"
}
] |
|
Masters in Artificial Intelligence Salary: Earning The Degree & The ...
|
Masters in Artificial Intelligence Salary: Earning The Degree & The Dollars
|
https://aifwd.com
|
[
"Editorial Staff"
] |
A candidate hired for this position could expect to earn $129,300–$193,900 annually, with candidates holding a master's degree entering at the ...
|
What salary can you expect to earn with a master’s in artificial intelligence?
According to PayScale , the average salary for someone holding a master’s degree in artificial intelligence is $102,848 — but the actual salary for AI professionals varies depending on several factors, including job title, specialization, location, and employer.
Based on PayScale’s data, the jobs with the highest paying AI salaries are:
Artificial intelligence engineers , who reportedly earn an average of $171,715, with top earners in the same profession earning upwards of $257,530 in annual salary.
Research scientists with AI skills, who reportedly earn an average base salary of $134,456.
Senior data scientists , who reportedly earn an average base salary of $117,438 and up to $150,000.
Machine learning engineers, who earn an average base salary of $106,055 and up to $150,000.
Factors that affect your salary when you have an MS in artificial intelligence include:
Skills: Having skills in subdisciplines like machine learning, robotics, and computer vision or with software like Python, Java, R, ApacheSpark, or Amazon Web Services can increase your competitiveness and salary potential from AI jobs.
Experience: AI specialists with more professional experience and seniority tend to attract more money in salaries than those just starting in the field.
Location: AI specialists working in locations like New York, Washington DC, San Jose, Seattle, and San Francisco on average earn higher salaries due to the higher cost of living and greater demand.
Specializations and additional certifications: Some industries, such as finance and government, require additional skills, subject matter expertise, or security clearances. Those with these extras will receive higher salaries in return.
Employer: Big tech companies competing for top artificial intelligence specialists offer higher salaries and attractive benefits for people with in-demand AI skills. Top employers include Google, Microsoft, and Facebook, among others.
We’ve covered the earning potential in various AI positions for those with master’s degrees in AI, as well as some factors that will impact this earning potential — but what exactly do you need to do to unlock it in the first place? In the next sections, we’ll explore just that: what a master’s in artificial intelligence entails, what kinds of people are good candidates for the degree, and what the day-to-day looks like for high-earning AI careers .
What’s a master’s in artificial intelligence?
A master’s in artificial intelligence is an advanced degree, usually offered by a computer science department, that’s intended to familiarize students with the landscape of artificial intelligence as well as give students the skills and other practical knowledge they need to undertake doctoral research or build and deploy artificial intelligence solutions in the industry.
Skills include:
Mathematics, especially statistics and linear algebra
Programming using languages like Python, R, and Java
Data pipeline skills such as data mining, data analytics, and data visualization
Ability to write artificial intelligence algorithms and build artificial intelligence models using software like Apache Spark , TensorFlow , and PyTorch .
Potential knowledge areas:
Machine learning, including deep learning and neural networks
Natural language processing (NLP)
Computer vision
Robotics
While you can expect most master’s programs in artificial intelligence to offer a curriculum more or less in line with the above, there are still significant differences in how programs design their curricula’s scope and structure.
At Northeastern’s Khoury College of Computer Sciences , for example, graduate students in the MS in artificial intelligence program first develop a comprehensive knowledge of artificial intelligence before choosing a specialization in robotics, machine learning, computer vision, intelligent interaction, or knowledge management. At Boston University , master's students have the opportunity to pursue independent projects, such as an MS thesis that they publicly defend in their final semester.
Northwestern University’s McCormick School of Engineering takes an interesting approach by offering dual tracks, one for those who plan to continue in artificial intelligence and one for those with advanced degrees who plan to take what they learn and return to their home discipline. In the former, MSAI, students will complete an internship with one of Northwestern’s industry partners or work on a project in Northwestern’s artificial intelligence laboratory before finishing off with a capstone project in their final semester. In the latter, MSAI+X, students will start with a programming and math bootcamp while foregoing these later components.
Who’s a master’s in artificial intelligence for?
Northwestern’s bootcamp requirement for students without a computer science or math background is instructive.
In general, applicants to artificial intelligence master’s programs are expected to have gotten their bachelor’s degrees in computer science, mathematics, or another technical field.
Though they don’t have a parallel track for students without a CS background, Northeastern similarly requires that incoming students have a strong background in computer science and mathematics. This background can be demonstrated by passing two placement exams on the fundamentals of computer science and statistics, probability, and linear algebra, respectively, or acquired before beginning study through the completion of two introductory courses.
Philadelphia’s Drexel University follows suit, requiring that students enter their Master’s in AI and Machine Learning program with a four-year bachelor’s degree or master’s degree in computer science, software engineering, or a related stem field with relevant work experience.
| 2022-10-11T00:00:00 |
https://aifwd.com/career/masters-in-artificial-intelligence-salary/
|
[
{
"date": "2022/10/11",
"position": 33,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 28,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 29,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 25,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 32,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 32,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 32,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 33,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 33,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 32,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 26,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 28,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 21,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/11",
"position": 25,
"query": "artificial intelligence wages"
}
] |
|
The Exploited Labor Behind Artificial Intelligence - Noema Magazine
|
The Exploited Labor Behind Artificial Intelligence
|
https://www.noemamag.com
|
[
"Adrienne Williams"
] |
Supporting transnational worker organizing should be at the center of the fight for “ethical AI.”
|
Credits Adrienne Williams and Milagros Miceli are researchers at the Distributed AI Research (DAIR) Institute. Timnit Gebru is the institute’s founder and executive director. She was previously co-lead of the Ethical AI research team at Google.
The public’s understanding of artificial intelligence (AI) is largely shaped by pop culture — by blockbuster movies like “The Terminator” and their doomsday scenarios of machines going rogue and destroying humanity. This kind of AI narrative is also what grabs the attention of news outlets: a Google engineer claiming that its chatbot was sentient was among the most discussed AI-related news in recent months, even reaching Stephen Colbert’s millions of viewers. But the idea of superintelligent machines with their own agency and decision-making power is not only far from reality — it distracts us from the real risks to human lives surrounding the development and deployment of AI systems. While the public is distracted by the specter of nonexistent sentient machines, an army of precarized workers stands behind the supposed accomplishments of artificial intelligence systems today.
Many of these systems are developed by multinational corporations located in Silicon Valley, which have been consolidating power at a scale that, journalist Gideon Lewis-Kraus notes, is likely unprecedented in human history. They are striving to create autonomous systems that can one day perform all of the tasks that people can do and more, without the required salaries, benefits or other costs associated with employing humans. While this corporate executives’ utopia is far from reality, the march to attempt its realization has created a global underclass, performing what anthropologist Mary L. Gray and computational social scientist Siddharth Suri call ghost work: the downplayed human labor driving “AI”.
Tech companies that have branded themselves “AI first” depend on heavily surveilled gig workers like data labelers, delivery drivers and content moderators. Startups are even hiring people to impersonate AI systems like chatbots, due to the pressure by venture capitalists to incorporate so-called AI into their products. In fact, London-based venture capital firm MMC Ventures surveyed 2,830 AI startups in the EU and found that 40% of them didn’t use AI in a meaningful way.
Far from the sophisticated, sentient machines portrayed in media and pop culture, so-called AI systems are fueled by millions of underpaid workers around the world, performing repetitive tasks under precarious labor conditions. And unlike the “AI researchers” paid six-figure salaries in Silicon Valley corporations, these exploited workers are often recruited out of impoverished populations and paid as little as $1.46/hour after tax. Yet despite this, labor exploitation is not central to the discourse surrounding the ethical development and deployment of AI systems. In this article, we give examples of the labor exploitation driving so-called AI systems and argue that supporting transnational worker organizing efforts should be a priority in discussions pertaining to AI ethics.
We write this as people intimately connected to AI-related work. Adrienne is a former Amazon delivery driver and organizer who has experienced the harms of surveillance and unrealistic quotas established by automated systems. Milagros is a researcher who has worked closely with data workers, especially data annotators in Syria, Bulgaria and Argentina. And Timnit is a researcher who has faced retaliation for uncovering and communicating the harms of AI systems.
Treating Workers Like Machines
Much of what is currently described as AI is a system based on statistical machine learning, and more specifically, deep learning via artificial neural networks, a methodology that requires enormous amounts of data to “learn” from. But around 15 years ago, before the proliferation of gig work, deep learning systems were considered merely an academic curiosity, confined to a few interested researchers.
In 2009, however, Jia Deng and his collaborators released the ImageNet dataset, the largest labeled image dataset at the time, consisting of images scraped from the internet and labeled through Amazon’s newly introduced Mechanical Turk platform. Amazon Mechanical Turk, with the motto “artificial artificial intelligence,” popularized the phenomenon of “crowd work”: large volumes of time-consuming work broken down into smaller tasks that can quickly be completed by millions of people around the world. With the introduction of Mechanical Turk, intractable tasks were suddenly made feasible; for example, hand-labeling one million images could be automatically executed by a thousand anonymous people working in parallel, each labeling only a thousand images. What’s more, it was at a price even a university could afford: crowdworkers were paid per task completed, which could amount to merely a few cents.
“So-called AI systems are fueled by millions of underpaid workers around the world, performing repetitive tasks under precarious labor conditions.”
The ImageNet dataset was followed by the ImageNet Large Scale Visual Recognition Challenge, where researchers used the dataset to train and test models performing a variety of tasks like image recognition: annotating an image with the type of object in the image, such as a tree or a cat. While non-deep-learning-based models performed these tasks with the highest accuracy at the time, in 2012, a deep-learning-based architecture informally dubbed AlexNet scored higher than all other models by a wide margin. This catapulted deep-learning-based models into the mainstream, and brought us to today, where models requiring lots of data, labeled by low-wage gig workers around the world, are proliferated by multinational corporations. In addition to labeling data scraped from the internet, some jobs have gig workers supply the data itself, requiring them to upload selfies, pictures of friends and family or images of the objects around them.
Unlike in 2009, when the main crowdworking platform was Amazon’s Mechanical Turk, there is currently an explosion of data labeling companies. These companies are raising tens to hundreds of millions in venture capital funding while the data labelers have been estimated to make an average of $1.77 per task. Data labeling interfaces have evolved to treat crowdworkers like machines, often prescribing them highly repetitive tasks, surveilling their movements and punishing deviation through automated tools. Today, far from an academic challenge, large corporations claiming to be “AI first” are fueled by this army of underpaid gig workers, such as data laborers, content moderators, warehouse workers and delivery drivers.
Content moderators, for example, are responsible for finding and flagging content deemed inappropriate for a given platform. Not only are they essential workers, without whom social media platforms would be completely unusable, their work flagging different types of content is also used to train automated systems aiming to flag texts and imagery containing hate speech, fake news, violence or other types of content that violates platforms’ policies. In spite of the crucial role that content moderators play in both keeping online communities safe and training AI systems, they are often paid miserable wages while working for tech giants and forced to perform traumatic tasks while being closely surveilled.
Every murder, suicide, sexual assault or child abuse video that does not make it onto a platform has been viewed and flagged by a content moderator or an automated system trained by data most likely supplied by a content moderator. Employees performing these tasks suffer from anxiety, depression and post-traumatic stress disorder due to constant exposure to this horrific content.
Besides experiencing a traumatic work environment with nonexistent or insufficient mental health support, these workers are monitored and punished if they deviate from their prescribed repetitive tasks. For instance, Sama content moderators contracted by Meta in Kenya are monitored through surveillance software to ensure that they make decisions about violence in videos within 50 seconds, regardless of the length of the video or how disturbing it is. Some content moderators fear that failure to do so could result in termination after a few violations. “Through its prioritization of speed and efficiency above all else,” Time Magazine reported, “this policy might explain why videos containing hate speech and incitement to violence have remained on Facebook’s platform in Ethiopia.”
Similar to social media platforms which would not function without content moderators, e-commerce conglomerates like Amazon are run by armies of warehouse workers and delivery drivers, among others. Like content moderators, these workers both keep the platforms functional and supply data for AI systems that Amazon may one day use to replace them: robots that stock packages in warehouses and self-driving cars that deliver these packages to customers. In the meantime, these workers must perform repetitive tasks under the pressure of constant surveillance — tasks that, at times, put their lives at risk and often result in serious musculoskeletal injuries.
“Data labeling interfaces have evolved to treat crowdworkers like machines, often prescribing them highly repetitive tasks, surveilling their movements and punishing deviation through automated tools.”
Amazon warehouse employees are tracked via cameras and their inventory scanners, and their performance is measured against the times managers determine every task should take, based on aggregate data from everyone working at the same facility. Time away from their assigned tasks is tracked and used to discipline workers.
Like warehouse workers, Amazon delivery drivers are also monitored through automated surveillance systems: an app called Mentor tallies scores based on so-called violations. Amazon’s unrealistic delivery time expectations push many drivers to take risky measures to ensure that they deliver the number of packages assigned to them for the day. For instance, the time it takes someone to fasten and unfasten their seatbelt some 90-300 times a day is enough to put them behind schedule on their route. Adrienne and many of her colleagues buckled their seat belts behind their backs, so that the surveillance systems registered that they were driving with a belt on, without getting slowed down by actually driving with a belt on.
In 2020, Amazon drivers in the U.S. were injured at a nearly 50% higher rate than their United Parcel Service counterparts. In 2021, Amazon drivers were injured at a rate of 18.3 per 100 drivers, up nearly 40% from the previous year. These conditions aren’t only dangerous for delivery drivers — pedestrians and car passengers have been killed and injured in accidents involving Amazon delivery drivers. Some drivers in Japan recently quit in protest because they say Amazon’s software sent them on “impossible routes,” leading to “unreasonable demands and long hours.” In spite of these clear harms, however, Amazon continues to treat its workers like machines.
In addition to tracking its workers through scanners and cameras, last year, the company required delivery drivers in the U.S. to sign a “biometric consent” form, granting Amazon permission to use AI-powered cameras to monitor drivers’ movements — supposedly to cut down on distracted driving or speeding and ensure seatbelt usage. It’s only reasonable for workers to fear that facial recognition and other biometric data could be used to perfect worker-surveillance tools or further train AI — which could one day replace them. The vague wording in the consent forms leaves the precise purpose open for interpretation, and workers have suspected unwanted uses of their data before (though Amazon denied it).
The “AI” industry runs on the backs of these low-wage workers, who are kept in precarious positions, making it hard, in the absence of unionization, to push back on unethical practices or demand better working conditions for fear of losing jobs they can’t afford to lose. Companies make sure to hire people from poor and underserved communities, such as refugees, incarcerated people and others with few job options, often hiring them through third party firms as contractors rather than as full time employees. While more employers should hire from vulnerable groups like these, it is unacceptable to do it in a predatory manner, with no protections.
“AI ethics researchers should analyze harmful AI systems as both causes and consequences of unjust labor conditions in the industry.”
Data labeling jobs are often performed far from the Silicon Valley headquarters of “AI first” multinational corporations — from Venezuela, where workers label data for the image recognition systems in self-driving vehicles, to Bulgaria, where Syrian refugees fuel facial recognition systems with selfies labeled according to race, gender, and age categories. These tasks are often outsourced to precarious workers in countries like India, Kenya, the Philippines or Mexico. Workers often do not speak English but are provided instructions in English, and face termination or banning from crowdwork platforms if they do not fully understand the rules.
These corporations know that increased worker power would slow down their march toward proliferating “AI” systems requiring vast amounts of data, deployed without adequately studying and mitigating their harms. Talk of sentient machines only distracts us from holding them accountable for the exploitative labor practices that power the “AI” industry.
An Urgent Priority For AI Ethics
While researchers in ethical AI, AI for social good, or human-centered AI have mostly focused on “debiasing” data and fostering transparency and model fairness, here we argue that stopping the exploitation of labor in the AI industry should be at the heart of such initiatives. If corporations are not allowed to exploit labor from Kenya to the U.S., for example, they will not be able to proliferate harmful technologies as quickly — their market calculations would simply dissuade them from doing so.
Thus, we advocate for funding of research and public initiatives that aim to uncover issues at the intersection of labor and AI systems. AI ethics researchers should analyze harmful AI systems as both causes and consequences of unjust labor conditions in the industry. Researchers and practitioners in AI should reflect on their use of crowdworkers to advance their own careers, while the crowdworkers remain in precarious conditions. Instead, the AI ethics community should work on initiatives that shift power into the hands of workers. Examples include co-creating research agendas with workers based on their needs, supporting cross-geographical labor organizing efforts and ensuring that research findings are easily accessed by workers rather than confined to academic publications. The Turkopticon platform created by Lilly Irani and M. Six Silberman, “an activist system that allows workers to publicize and evaluate their relationships with employers,” is a great example of this.
Journalists, artists, and scientists can help by drawing clear the connection between labor exploitation and harmful AI products in our everyday lives, fostering solidarity with and support for gig workers and other vulnerable worker populations. Journalists and commentators can show the general public why they should care about the data annotator in Syria or the hypersurveilled Amazon delivery driver in the U.S. Shame does work in certain circumstances and, for corporations, the public’s sentiment of “shame on you” can sometimes equal a loss in revenue and help move the needle toward accountability.
Supporting transnational worker organizing should be at the center of the fight for “ethical AI.” While each workplace and geographical context has its own idiosyncrasies, knowing how workers in other locations circumvented similar issues can serve as inspiration for local organizing and unionizing efforts. For example, data labelers in Argentina could learn from the recent unionizing efforts of content moderators in Kenya, or Amazon Mechanical Turk workers organizing in the U.S., and vice versa. Furthermore, unionized workers in one geographic location can advocate for their more precarious counterparts in another, as in the case of the Alphabet Workers Union, which includes both high paid employees in Silicon Valley and outsourced low wage contractors in more rural areas.
“This type of solidarity between highly-paid tech workers and their lower-paid counterparts — who vastly outnumber them — is a tech CEO’s nightmare.”
This type of solidarity between highly-paid tech workers and their lower-paid counterparts — who vastly outnumber them — is a tech CEO’s nightmare. While corporations often treat their low-income workers as disposable, they’re more hesitant to lose their high-income employees who can quickly snap up jobs with competitors. Thus, the high-paid employees are allowed a far longer leash when organizing, unionizing, and voicing their disappointment with company culture and policies. They can use this increased security to advocate with their lower-paid counterparts working at warehouses, delivering packages or labeling data. As a result, corporations seem to use every tool at their disposal to isolate these groups from each other.
Emily Cunningham and Maren Costa created the type of cross-worker solidarity that scares tech CEOs. Both women worked as user experience designers at Amazon’s Seattle headquarters cumulatively for 21 years. Along with other Amazon corporate workers, they co-founded the Amazon Employees for Climate Justice (AECJ). In 2019, over 8,700 Amazon workers publicly signed their names to an open letter addressed to Jeff Bezos and the company’s board of directors demanding climate leadership and concrete steps the company needed to implement to be aligned with climate science and protect workers. Later that year, AECJ organized the first walkout of corporate workers in Amazon’s history. The group says over 3,000 Amazon workers walked out across the world in solidarity with a youth-led Global Climate Strike.
Amazon responded by announcing its Climate Pledge, a commitment to achieve net-zero carbon by 2040 — 10 years ahead of the Paris Climate Agreement. Cunningham and Costa say they were both disciplined and threatened with termination after the climate strike — but it wasn’t until AECJ organized actions to foster solidarity with low-wage workers that they were actually fired. Hours after another AECJ member sent out a calendar invite inviting corporate workers to listen to a panel of warehouse workers discussing the dire working conditions they were facing at the beginning of the pandemic, Amazon fired Costa and Cunningham. The National Labor Relations Board found their firings were illegal, and the company later settled with both women for undisclosed amounts. This case illustrates where executives’ fears lie: the unflinching solidarity of high-income employees who see low-income employees as their comrades.
In this light, we urge researchers and journalists to also center low-income workers’ contributions in running the engine of “AI” and to stop misleading the public with narratives of fully autonomous machines with human-like agency. These machines are built by armies of underpaid laborers around the world. With a clear understanding of the labor exploitation behind the current proliferation of harmful AI systems, the public can advocate for stronger labor protections and real consequences for entities who break them.
| 2022-10-13T00:00:00 |
https://www.noemamag.com/the-exploited-labor-behind-artificial-intelligence
|
[
{
"date": "2022/10/13",
"position": 89,
"query": "AI workers"
},
{
"date": "2022/10/13",
"position": 76,
"query": "artificial intelligence workers"
},
{
"date": "2022/10/13",
"position": 76,
"query": "artificial intelligence workers"
},
{
"date": "2022/10/13",
"position": 84,
"query": "AI workers"
},
{
"date": "2022/10/13",
"position": 80,
"query": "artificial intelligence workers"
},
{
"date": "2022/10/13",
"position": 83,
"query": "artificial intelligence workers"
}
] |
|
AI Developer Salary and Hourly Rates in 2022 - Intersog
|
AI Developer Salary and Hourly Rates in 2022
|
https://intersog.com
|
[] |
According to the latest Indeed report, American AI development specialists earn up to 145,000 dollars annually and 65 dollars hourly. On the ...
|
The demand for artificial intelligence (AI) developers is skyrocketing as the AI domain gains more traction across industries. American companies are willing to pay $100K-180K USD (or even more) per year to AI developers and related roles. Since these jobs require high expertise in complex technologies, such as deep learning, data mining, and natural language processing (NLP). We are breaking down some of the details for AI developer salary and hourly rates.
In this article, we will answer the question: "How much do AI developers earn in the United States?" and provide some information about the entire AI domain.
AI Software Development
Artificial intelligence is a cross-disciplinary term that refers to systems that mimic aspects of human intelligence, such as learning and problem-solving. It is one of the most popular software development domains.
Check out a related article: GenAI Meets Product: How Smarter Teams Are Building Better Software
SUCCESS STORY
84% of technology executives in the United States believe they need to adopt and leverage AI technologies like natural language processing, deep learning, and cognitive computing into their software projects to accelerate business growth. Tech leaders like Google, HP, and Salesforce are trying to increase their market share by integrating AI into their products.
Here are the top 12 companies making the most significant investments in AI software development and related technologies:
Amazon
IBM
Microsoft
Salesforce
Alphabet
NVIDIA
Baidu
SAP SE
Oracle Corporation
Meta
Hewlett-Packard
Apple
Artificial intelligence developers work with data, algorithms, and code to create intelligent systems that can predict, guide, and control. These professionals are at the forefront of a digital revolution, making it possible to develop and adopt AI technologies with in-depth understanding, self-learning capabilities, and high levels of accuracy.
Data scientist, machine learning engineer, AI researcher, and BI developer are some of the most popular AI-related roles in software development.
Hire talented developers from LATAM, Canada, and Europe
Check out a related article: UX for AI Products: Why Developer Experience Starts with Design
AI Developer Salary Overview
The global AI software market will grow in the next few years, reaching around 126 billion U.S. dollars by 2025, as Statista forecasts. This tendency will inevitably lead to an increased demand for AI developers across fast-growing industries, such as healthcare, financial services, automotive, and ed-tech.
Revenues from the AI software market worldwide (2018-2025)
Source: Statista.com
According to Zippia, AI tech will generate around 58 million new jobs worldwide by 2022. Tech giants like Cisco, Amazon, and Samsung are already recruiting thousands of AI-proficient engineers.
As a result, specialists in artificial intelligence-related fields often receive higher salaries than other software development roles.
According to the latest Indeed report, American AI development specialists earn up to 145,000 dollars annually and 65 dollars hourly. On the other hand, traditional software development roles like iOS/Android app developers and PHP web developers, in general, earn around 129,000 dollars in the United States.
SUCCESS STORY SAM Learning web portal development
PayScale and ZipRecruiter report even higher salaries for AI programmers. For example, a senior machine learning (ML) engineer earns as high as $178,000 annually and $93 hourly. Moreover, in leading tech companies like Alphabet, Meta, and Microsoft, the yearly salaries can reach $190,000.
AI Role Salary Comparison Table
The following table shows the median salaries and hourly rates for different AI development roles in the United States (as of October 2022 by Indeed.com):
Would you like to learn more about talent solutions
AI Role Typical Job Requirements Average Salary AI Architect This senior AI role requires in-depth knowledge of ML and deep learning workloads and designing AI architecture, data models, and training pipelines. Skills: Git, containers, Kubernetes, CI/CD, SAS, R, Python, TensorFlow, random forest, other algorithms. $103,000 per year
$65 per hour Machine Learning Engineer ML engineers develop data models and software components that work with minimal human supervision and generate insights for fine-tuning. Skills: Python, SQL, Java, C++, TensorFlow, PyTorch, MATLAB, Apache Kafka, Spark and Hadoop, Google Cloud ML Engine, IBM Watson. $125,641 per year
$54 per hour AI Developer/Engineer AI developers create, test, and deploy code for AI-enabled cloud and on-premise software/models. Skills: C++, Python, Java, Scala, linear algebra, APIs, algorithms, SciPy, TensorFlow, NumPy, shell scripting, and statistical analysis $120,366 per year
$56 per hour Big Data Engineer Big Data engineers design systems for mining and processing massive pre-existing data to obtain valuable insights. Skills: C++, Java, Python, DBMS, Weka, KNIME, ETL/ELT, data warehousing, Hadoop, Spark, Talend, data mining & modeling. $137,707 per year
$59 per hour Data Scientist This role requires you to analyze and interpret data for actionable insights. Skills: Python, R, Scala, MongoDB, MySQL, vector models, calculus, regression techniques, Tableau, Power BI, BeautifulSoup, Pandas, RapidMiner, Spark. $144,737 per year
$62 per hour Research Scientist/ AI Researcher AI research scientists develop and adopt AI methods and approaches and prototype intelligent systems for collecting and analyzing data. Skills: AI models (Gaussian Mixture Models, Naïve Bayes, Hidden Markov Models), intelligent virtual agents, DBMS (MongoDB, PostgreSQL, AWS, Cloudera), ML fundamentals, Java, C++, Python. $78,237 per year $34 per hour ML Ops ML Ops specialists integrate machine learning models into the existing data infrastructure and ensure they are functional in production. Skills: Cloud solutions (AWS, GCP, Azure), ML frameworks (Keras, PyTorch, Tensorflow), Docker, Kubernetes, Linux, Windows, MLOps frameworks (DataRobot, Kubeflow, MLFlow). $126,366 per year
$54 per hour BI Developer Business intelligence developers create BI interfaces and tools that make technical language and complex information simple for non-technical staff to understand. Skills: ETL/ELT, Python, Javascript, PHP, Java, data formatting, DBMS, SQL/NoSQL, APIs, Power BI, Tableau, Oracle Analytics Cloud, Sisense $91,155 per year
$58 per hour
Factors Influencing AI Developer Salaries and Rates
Companies pay for the services of AI developers depending on three factors: skills, location, and project complexity. All of these aspects have a significant impact on the final rate.
1. Skills
Skills are one of the main factors influencing AI development salary rates. The higher and more complex these skills are, the higher the overall salary rate is. Experience is also essential. It enables the specialists to earn more money.
Here are some of the high-paying AI skills:
Programming : Python, Java, R, C++
: Python, Java, R, C++ Big data processing : Hadoop, Storm, Hive, Spark
: Hadoop, Storm, Hive, Spark Machine learning and algorithms: TensorFlow, MXNet, Keras, Theano
2. Location
Location is another critical component of the AI salary calculation process. Employers tend to pay higher wages in major business centers, but this is not always the case. Depending on the state's economic situation, employers may offer higher or lower salaries than the national average. Also, offshore AI developers from Canada and Mexico can earn more money than their American counterparts.
15 U.S. Cities with the Highest AI Developer Pays (Median)
AI Role Average Annual Base Salary Average RPH San Francisco, CA $141,275 $68 San Jose, CA $137,083 $66 New York, NY $133,973 $64 Seattle, WA $132,570 $64 Washington, DC $131,785 $63 Boston, MA $131,498 $63 Los Angeles, CA $128,853 $62 New Haven, CT $126,673 $61 Chicago, IL $126,040 $61 Hartford, CT $125,968 $61 Sacramento, CA $125,339 $60 Denver, CO $124,068 $60 Minneapolis, MN $123,697 $59 Baltimore, MD $123,426 $59
San Diego, CA $122,534 $59 Nationwide $120,366 $56 Source: ZipRecruiter
3. Project Complexity
As artificial intelligence is a complex domain, organizations are willing to spend more money on research and development tasks. AI projects focusing on machine learning and natural language processing are usually complicated and require more time and resources. As a result, AI developers specializing in these technologies tend to get high salaries.
How to Hire Expert AI Developers
If you are looking for artificial intelligence expertise, you have several great resources to hire talented AI developers online, as follows below:
Hire talented developers from LATAM, Canada, and Europe
1. Intersog staffing services
Companies like Tesla, CDW, and Mitsubishi hire Intersog to find highly-qualified and reliable IT specialists. AI developers are no exception. Intersog sources its clients with top AI developer candidates and ensures that each AI development project completes on time. Our delivery centers are in Chicago, Guadalajara, Vancouver, and Odesa.
The weak side for some businesses could be that Intersog only provides long-term contracts (3-6 months and more).
2. Freelance marketplaces: Toptal, Upwork, Turing.com
These recruitment platforms are perfect for short-term and uncomplex projects. Freelance marketplaces publish many job openings and are one-stop shops for hiring self-employed AI developers and other software professionals. The weak side is that you must review many CVs before choosing the best specialist for your project.
3. Job search engines: Indeed, Monster, ZipRecruiter
Job search engines are great resources for finding AI specialists with five-star ratings and many positive reviews. In addition, these websites scan social networks and other sources to help companies find the best developers.
The disadvantage of job boards is that you don't have any control over the recruiting process, and hiring a talented AI specialist is a lengthy process that can take weeks or even months.
4. LinkedIn and other professional networks (Meetup, Xing)
LinkedIn is one of the most effective tools for recruiting artificial intelligence professionals. Each LinkedIn member has a high profile and is known in their field. LinkedIn group pages are also great places to find talent, with active communities that help companies find the right specialist in their area of expertise.
The weak side is that recruiters and potential employers may not showcase their professional experts and interests well. As a result, it might take some time for the two sides to build trust and connections on LinkedIn.
Read this guide to learn more about hiring AI developers.
Will AI Developer Salaries Increase or Decrease in the Next Few Years?
AI software development has become a desirable career path considering that AI-based solutions are in great demand across many areas, such as customer service, healthcare, marketing, and finance. In the nearest future, experts predict that the need for AI engineering specialists will continue growing yearly.
Would you like to learn more about talent solutions
On the supply front, a growing number of STEM graduates will flood the software engineering job market by 8% annually. This tendency will lead to an increased number of AI engineers respectively.
On the demand side, an increasing number of organizations will continue to apply AI technologies. As a result, AI developer salaries will increase over the next 3-5 years.
Tech Industry Layoffs 2022
In June 2022, many tech companies, including Meta, Twitter, and Amazon, started laying off significant numbers of their tech people, including AI developers and engineers. This unexpected event pulls down most of the tech industry, causing a noticeable spike in unemployment and a chill in consumer confidence.
Source: news.crunchbase.com
The unemployment rate remains higher than before the layoff, but it is not getting worse anymore. The growth of the labor market is still lower than that in 2018. The Tech industry is growing more down than before the layoff. And the salary of AI engineers will likely decrease to some extent.
Wrapping Up
In conclusion, you should know that AI development specialists are in high demand. Understanding the starting point of AI developer salary and hourly rates can help. Also, their salaries will definitely increase every year. Given the job market trends, AI roles will be among the highest-paid professionals in the IT industry for the next several years. Leveraging Intersog's AI Solution Services could be a great alternative for your AI efforts.
| 2022-10-19T00:00:00 |
2022/10/19
|
https://intersog.com/blog/development/ai-developer-salary-and-hourly-rates/
|
[
{
"date": "2022/10/19",
"position": 67,
"query": "artificial intelligence wages"
},
{
"date": "2022/10/19",
"position": 57,
"query": "artificial intelligence wages"
}
] |
Smart scheduling: How to solve workforce-planning challenges with AI
|
Smart scheduling: How to solve workforce-planning challenges with AI
|
https://www.mckinsey.com
|
[
"Jorge Amar",
"Sohrab Rahimi",
"Nicolai Von Bismarck",
"Akshar Wunnava"
] |
AI-driven schedule optimizers can alleviate age-old scheduling headaches—reducing employee downtime, improving productivity, and minimizing ...
|
Today, workforce planning has reached a turning point. Traditional workforce management processes, which rely heavily on time-consuming and inconsistent manual steps, can no longer provide the dynamic workforce scheduling needed in the face of ongoing labor market disruptions. The past two years have brought the inefficiencies of traditional processes to the surface more keenly than ever before as the COVID-19 pandemic placed an unforeseen strain on day-to-day operations across many sectors. The challenges of a constrained labor supply and higher wages remain at the fore.
In recent years, advanced data applications and AI have optimized many business processes. Yet until now, workforce planning hasn’t enjoyed the same level of digital transformation. Recent advances in technology and declining costs have made end-to-end, AI-driven schedule optimization a real possibility—and an opportunity. This article explores how it can drive a long-needed transformation by bringing greater speed, flexibility, and intelligence to bear on the problem of optimizing schedules, so companies can deploy the people they need when they need them and unlock new levels of efficiency.
The unsolved optimization challenge
Optimizing schedules is one of the most challenging of all optimization problems. Extreme variability—in workforce types and operations, as well as across sectors and businesses—makes these solutions hard to standardize.
Even within individual businesses, the complexity of workforce planning and the demand for dynamic action make agile decision making difficult. To operate with the greatest efficiency, businesses must deploy the right number of workers to meet demand and minimize employee downtime on any given day. The constantly changing picture and high number of decision variables generate complex traditional computer models that often take a long time to run. Factor in unforeseen changes—such as employees not showing up for work at a moment’s notice or spikes in demand—and the pressures on optimization models become even greater. New schedules must be calculated using fresh inputs very quickly, yet most all-in-one optimization models take hours to deliver updated schedules.
What’s more, existing scheduling tools are not always user friendly and may require a team of data scientists to maintain and update. And to be truly valuable, scheduling models must be integrated with other models, such as demand forecasting. As a result of these challenges, businesses can lose the opportunity to streamline their offerings and provide better service to customers—and thus lose income too.
Would you like to learn more about our Operations Practice
Optimizing workforce management matters now more than ever. Three recent factors have forced it up the strategic corporate agenda. First (and least expected) was the impact of the COVID-19 pandemic on operations globally as abrupt swings in demand stretched spreadsheet-based workforce-scheduling models past their limits. A North American telco illustrates the challenge: amid skyrocketing demand for internet capacity, the organization struggled to reassign its technicians (who were long used to providing on-site installation and repair) to resolve problems remotely. Hampered by old technology, the business couldn’t overcome its problems in workforce management and personnel scheduling. Satisfaction fell among customers and field workers alike, while both customer churn and employee attrition increased.
Second, though COVID-19 may prove to be a one-time event in our lifetimes, more changes in the workforce landscape are expected because of, for example, high inflation, demographic turnover (as large numbers of highly experienced workers retire), and potential policy changes affecting labor terms and conditions. The resulting uncertainty could persistently complicate labor planning.
Proposed regulations may force organizations to change their operational and labor supply strategies. Given the manual nature of current workforce management systems, optimizing and changing day-to-day operations require a lot of time, as well as large teams of planners and capacity managers. Consequently, current scheduling processes are often inconsistent and heavily influenced by human bias, and that raises the potential for error, inefficiency, and regulatory risk. All these factors probably increase in tandem with the complexity of the labor force.
Third, and most encouraging, the advent of new technologies for AI and cloud-based computing has reduced the cost of deploying end-to-end, AI-driven solutions for optimizing schedules. During the past ten years, the organizational appetite for adopting digital solutions for workforce management has consistently increased.
The advent of new technologies for AI and cloud-based computing has reduced the cost of deploying end-to-end, AI-driven solutions for optimizing schedules.
Leveraging AI to manage and schedule the workforce
The current market context makes it more important than ever to optimize schedules. AI-driven tools offer optimal solutions for the range of interdependent constraints and changing demand. These solutions generate schedules that are as efficient as possible, so the right resources reach the right places at the right times.
AI-driven solutions take significantly less time to schedule the workforce than current spreadsheet-based models do and can capture unexpected changes in operations more efficiently. The technology allows for a consistent and systematic approach, eliminating human bias and error, creating fairer planning schedules, and reducing the managerial bandwidth required to oversee the scheduling process.
Exhibit 1, for instance, depicts how smart scheduling could optimize the daily schedules of crew members at a utility service center by streamlining daily activities, reducing travel time, and increasing overall productivity and efficiency in the field. The left-hand side shows how much time crew members have traditionally spent on jobs, travel, and unassigned or nonjob work, such as training sessions. The right-hand side shows an optimized schedule—how smart scheduling could have allocated these employees’ time. Job time increases significantly thanks to geographic optimization and the use of better estimates for job durations. Unassigned times fall.
Turning this promise into reality—especially across different sectors—requires a new way to approach the workforce and demand. Three possible approaches could help smart scheduling succeed through optimization: generalizing schedules across operation types, developing a modular approach, and integrating user-friendly and end-to-end digital solutions.
Generalizing schedules across operation types
Across sectors, generalizing optimization to all types of operations is a major challenge in scaling up solutions to optimize schedules. Some operations, for example, require workers to travel between different locations to finish jobs; others don’t. These two possibilities require significantly different modeling. Part of the solution involves identifying different types of operations and creating an inclusive categorization system. The majority of operations across different sectors can be grouped in five categories:
Job stages. A single job (in construction projects, for example) might comprise multiple stages that cannot start until the earlier ones have finished. These stages can stretch out over multiple days. Other jobs, such as calls answered by call center agents, involve only one stage: the job is done when the call ends.
Crew allocation. A job might require more than one worker and skill type, so workforce managers must ensure that a crew with the right skills is allocated. Decisions about which crew is required for which job depend on skills, availability, and distance to the job’s location. Of course, that becomes more complicated the more crew members are geographically dispersed.
Demand type. In some cases—such as fast-food restaurants or call centers—demand types fluctuate, and the volume of work is not known ahead of time. In other cases, such as mining projects, the amount of work is known in advance, and the scheduling system must address a backlog of work in an optimal way.
Shift type. In some instances, shifts can change from week to week. Managers in a call center, for example, could decide to have a different number of agents on the load in different weeks. In other instances, shifts are fixed.
Mobility. In areas such as field-force operations, workers must travel from one location to another. This adds a level of complexity, since driving times must be factored in.
A single operation can fall into multiple categories. Consider a fast-food-delivery operation. It could have fluctuating demand, be mobility centered, and employ both full- and part-time workers. Depending on the product, the jobs could involve one stage or several stages. Optimization systems must be flexible enough to handle all the different job types relevant to an operation’s needs.
Developing a modular approach
Solving scheduling problems for all operation types requires a set of predesigned modules that can be assembled to address specific scheduling problems. A modular approach helps with run times and computational aspects because it breaks down the optimization into multiple smaller steps.
Four modules can handle a majority of schedule optimization problems.
Demand and supply balancing. This is a core module in most operations. The optimization model is an integer programming problem in which the input is sub-daily demand and the output is required shifts. The module decides on the number of shifts needed and therefore addresses all or a portion of the demand (depending on the user’s setting) while minimizing total costs.
Job-to-work-center allocation. In some cases, the allocation of jobs to work centers must be decided before shifts can be optimized—for example, call centers where calls must be routed to different centers or field-force operations that must distribute jobs in different locations among a number of technician centers.
Heuristic dispatching. Particularly if assigning jobs is complex, a heuristic approach can be successfully applied to dispatching problems. Some jobs might need to be prioritized, for example, or workers may have different competency levels. In these cases, heuristic optimization is the most powerful approach because it can apply all custom rules in a significantly flexible way. As a result of this approach’s iterative nature, the user controls how optimal the response should be. That makes run times and required computational resources more flexible.
The traveling-salesman problem. Mobile workforces such as field service operations can benefit from the module for traveling-salesman problems. Once jobs are assigned to workers based on priority and skill type, this module can work out the right sequence of stops to minimize overall travel time.
When the four optimization modules are combined, the five optimization categories can address a majority of operation types. A modular approach not only adds flexibility but also reduces the required computation time (Exhibit 2).
Integrating user-friendly, end-to-end digital solutions
Optimization must be integrated and provide a user-friendly, end-to-end digital solution through constant updating, accurate forecasting, and a helpful interactive front-end interface.
Constant updating is critical to ensure that incoming data are fresh and relevant for scheduling decisions. Certain data sets must be refreshed and input into the scheduling engine on a daily or weekly basis—for instance, incoming jobs, work booked for the coming two to four weeks, incomplete or carryover work, and upcoming crew availability. This allows the scheduling engine to output relevant schedules and planning calls between the scheduling and field execution teams.
In this way, scheduling teams can procure the right resources with sufficient lead times to prevent unplanned overtime, backlog, and incomplete orders. Short-range forecasts, for example, provide a view of expected work volumes and available crews. Any mismatch between supply and demand can therefore be adjusted ahead of time, preventing last-minute scrambles. Thanks to this approach, a North American telecom provider reached 80 to 85 percent accuracy levels by developing forecasts with daily granularity.
Finally, an effective, interactive front-end interface allows a new scheduling tool to be adopted more quickly and sustainably. Features such as drag-and-drop daily or weekly schedules, preloaded AI-optimized schedules, and metrics dashboards are superior to (and easier to use than) the current spreadsheet-based schedules.
Significant results in the electric and gas sector
The electric and gas utilities sector, by nature, presents a scheduling challenge given the variety of different work types and varying schedule dynamics. Schedules must take into account short-term, long-term, and unplanned emergency jobs, and demand must be matched with resources and supplies such as crews, materials, and equipment.
Smart scheduling has been shown to work to great effect within this sector. A US electric and gas utility, for example, deployed a smart-scheduling solution for six weeks at one of its service centers. It improved the productivity and user experience of schedulers and field crews alike.
People and places: How and where to work next
To deal with the challenges of job prioritization, schedule preparation, and execution, the utility used a machine learning–based schedule optimizer that automated and optimized the creation of schedules, thus improving productivity in the field and reducing rework among schedulers. The service center made significant gains in productivity—break-ins (emergency jobs that disrupt schedules and demand real-time reworking) fell by 75 percent and job delays by 67 percent.
Smart scheduling also identified the optimal crews for emergency jobs, basing its choices on geographic proximity and the importance of the crew members’ current jobs. False truck rolls—when jobs can’t be started or completed on time because crews, equipment, or materials are not available—fell by 80 percent (Exhibit 3). This in turn made employees more available for jobs, and fewer break-ins meant that more work was completed—total on-job time increased by around 29 percent, and total jobs worked on by 6 percent (Exhibit 4). Smart scheduling also ensured that crews, equipment, and materials were available to optimize each job.
Overall, after accounting for seasonality and other confounding variables, the service center, over a period of six weeks, boosted the productivity of field workers by 20 to 30 percent and the productivity of schedulers by 10 to 20 percent. This equaled one to two hours a day—results confirming that smart scheduling is a way for businesses to get ahead.
Workforce optimization has long been among the most challenging problems for businesses. It is even more so now, given ongoing labor disruptions and higher wages. AI-driven schedule optimizers offer solutions that smooth out and speed up workforce management processes. By adopting customized AI-driven schedulers, businesses can optimize across all their spheres of operation, save time and money, and, ultimately, boost their productivity.
| 2022-11-01T00:00:00 |
https://www.mckinsey.com/capabilities/operations/our-insights/smart-scheduling-how-to-solve-workforce-planning-challenges-with-ai
|
[
{
"date": "2022/11/01",
"position": 38,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 40,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 37,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 38,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 37,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 38,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 37,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 37,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 36,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 38,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 37,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 42,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 41,
"query": "machine learning workforce"
},
{
"date": "2022/11/01",
"position": 39,
"query": "machine learning workforce"
}
] |
|
Robots are taking over jobs, but not at the rate you might think says ...
|
Robots are taking over jobs, but not at the rate you might think says BYU research
|
https://news.byu.edu
|
[
"Media Contact",
"Min Read"
] |
Only 14% of workers say their job has been replaced by a robot. Those who have experienced job displacement overstate the effect robot takeover ...
|
The study found that robots aren’t replacing humans at the rate most people think, but people are prone to exaggerate the rate of robot takeover.
Photo by Jaren Wilkey, BYU Photo
It’s easy to believe that robots are stealing jobs from human workers and drastically disrupting the labor market; after all, you’ve likely heard that chatbots make more efficient customer service representatives and that computer programs are tracking and moving packages without the use of human hands.
But there’s no need to panic about a pending robot takeover just yet, says a new study from BYU sociology professor Eric Dahlin. Dahlin’s research found that robots aren’t replacing humans at the rate most people think, but people are prone to severely exaggerate the rate of robot takeover.
The study, recently published in Socius: Sociological Research for a Dynamic World , found that only 14% of workers say they’ve seen their job replaced by a robot. But those who have experienced job displacement due to a robot overstate the effect of robots taking jobs from humans by about three times.
To understand the relationship between job loss and robots, Dahlin surveyed nearly 2,000 individuals about their perceptions of jobs being replaced by robots. Respondents were first asked to estimate the percentage of employees whose employers have replaced jobs with robots. They were then asked whether their employer had ever replaced their job with a robot.
Only 14% of workers say their job has been replaced by a robot. Those who have experienced job displacement overstate the effect robot takeover by about three times.
Photo by Jaren Wilkey, BYU Photo
Those who had been replaced by a robot (about 14%), estimated that 47% of all jobs have been taken over by robots. Similarly, those who hadn’t experienced job replacement still estimated that 29% of jobs have been supplanted by robots.
“Overall, our perceptions of robots taking over is greatly exaggerated,” said Dahlin. “Those who hadn’t lost jobs overestimated by about double, and those who had lost jobs overestimated by about three times.”
Attention-grabbing headlines predicting a dire future of employment have likely overblown the threat of robots taking over jobs, said Dahlin, who noted that humans’ fear of being replaced by automated work processes dates to the early 1800s.
“We expect novel technologies to be adopted without considering all of the relevant contextual impediments such as cultural, economic, and government arrangements that support the manufacturing, sale, and use of the technology,” he said. “But just because a technology can be used for something does not mean that it will be implemented.”
Dahlin says these findings are consistent with previous studies , which suggest that robots aren’t displacing workers. Rather, workplaces are integrating both employees and robots in ways that generate more value for human labor.
“An everyday example is an autonomous, self-propelled machine roaming the isles and cleaning floors at your local grocery store,” says Dahlin. “This robot cleans the floors while employees clean under shelves or other difficult-to-reach places.”
Dahlin says the aviation industry is another good example of robots and humans working together. Airplane manufacturers used robots to paint airplane wings. A robot can administer one coat of paint in 24 minutes – something that would take a human painter hours to accomplish. Humans load and unload the paint while the robot does the painting.
| 2022-11-09T00:00:00 |
2022/11/09
|
https://news.byu.edu/intellect/robots-are-taking-over-jobs-but-not-at-the-rate-you-might-think-says-byu-research
|
[
{
"date": "2022/11/09",
"position": 37,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 37,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 52,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 31,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 30,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 31,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 30,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 32,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 31,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 32,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 30,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 23,
"query": "robotics job displacement"
},
{
"date": "2022/11/09",
"position": 35,
"query": "robotics job displacement"
}
] |
[D] Current Job Market in ML : r/MachineLearning - Reddit
|
The heart of the internet
|
https://www.reddit.com
|
[] |
...
|
Hi,
We all have heard about the layoffs in tech companies. How about ML/AI jobs? Do you observe a decrease in the number of job openings etc?
I am a bit confused because there are so many AI startups now announcing getting funded. Someone in the industry who has more experience can maybe shed some light?
| 2022-11-11T00:00:00 |
https://www.reddit.com/r/MachineLearning/comments/ysc7gs/d_current_job_market_in_ml/
|
[
{
"date": "2022/11/11",
"position": 46,
"query": "machine learning workforce"
}
] |
|
New AI Training Requirement for Certain Federal Government ...
|
New AI Training Requirement for Certain Federal Government Employees
|
https://www.littler.com
|
[
"Executive Director",
"Federal Compliance",
"Tysons Corner"
] |
... government's workforce has knowledge of how artificial intelligence ... workplace policy at the international, national and local levels.
|
On October 17, 2022, President Biden signed into law the AI Training Act (the “Act”). The purported purpose of the Act is to ensure the federal government’s workforce has knowledge of how artificial intelligence (AI) works, AI’s benefits, and AI’s risks.
The Act requires the Office of Management and Budget (OMB) to establish or otherwise provide AI training for federal government agency employees responsible for: program management; planning, research, development, engineering, testing, and evaluation of systems; procurement and contracting; logistics; and cost estimating. The OMB has one year to establish the training program, which must cover the following broad topics:
the science underlying AI, including how AI works;
introductory concepts relating to the technological features of AI systems;
the ways in which AI can benefit the federal government;
the risks posed by AI, including discrimination and risks to privacy;
ways to mitigate the risks, including efforts to create and identify AI that is reliable, safe, and trustworthy; and
future trends in AI, including trends for homeland and national security and innovation.
While this Act creates an additional burden on the federal government employees, the AI training should help reduce the risk that AI will be misused by the federal government. The OMB is required to update the training at least every two years and ensure there is a way to understand and measure the participation of the workforce and to receive and consider feedback from program participants.
The Act imposes no required action on private employers or federal government contractors. To the extent employers sell AI-related products or services to the federal government or otherwise provide staffing services that leverage AI, after the AI training is implemented, these contractors may interact with better-informed federal government employees in bidding on and providing AI-related software, products, and services.
Until the OMB develops its AI training, which could be as late as fall 2023, we do not know the specific content and nuances of the AI training. However, in the interim, contractors should continue to monitor and evaluate AI software and services for discrimination and risks of privacy.
The passage of the Act, considered in the context of the White House’s AI Bill of Rights and the EEOC Guidance on AI and ADA, highlights the federal government’s ongoing commitment to regulating the use of AI in situations that affect employees.
| 2022-11-14T00:00:00 |
https://www.littler.com/news-analysis/asap/new-ai-training-requirement-certain-federal-government-employees
|
[
{
"date": "2022/11/14",
"position": 73,
"query": "government AI workforce policy"
},
{
"date": "2022/11/14",
"position": 74,
"query": "government AI workforce policy"
},
{
"date": "2022/11/14",
"position": 85,
"query": "government AI workforce policy"
},
{
"date": "2022/11/14",
"position": 76,
"query": "government AI workforce policy"
},
{
"date": "2022/11/14",
"position": 72,
"query": "government AI workforce policy"
},
{
"date": "2022/11/14",
"position": 90,
"query": "government AI workforce policy"
},
{
"date": "2022/11/14",
"position": 58,
"query": "government AI workforce policy"
},
{
"date": "2022/11/14",
"position": 68,
"query": "government AI workforce policy"
},
{
"date": "2022/11/14",
"position": 87,
"query": "government AI workforce policy"
}
] |
|
Automation and polarisation | CEPR
|
Automation and polarisation
|
https://cepr.org
|
[] |
Existing evidence shows that automation has impacted jobs in the middle of the pay and skill distribution, causing polarisation in the ...
|
Automation technologies have spread rapidly throughout the advanced economies during the last decades. The number of industrial robots per worker in the US economy increased from 0.38 to 1.8 between 1993 and 2017, while the share of information technology in overall US investment rose from 3.5% to 23% between 1950 and 2020. These developments have caused fears of widespread displacement of workers and sparked a debate about the future role of human labour in the economy (e.g. Mokyr et al. 2015).
A growing body of evidence has documented the consequences of automation. Much of this evidence points to its polarising impact. Autor and Dorn (2013) and Goos et al. (2009), for example, show that information technologies have reduced employment and wages in occupations located in the middle of the wage distribution, both in the US and Europe. Evidence provided in Graetz and Michaels (2018), Acemoglu and Restrepo (2020) and Dauth et al. (2021) suggests that industrial robots have also had their biggest impact on middle-skill occupations.
Why automation has caused this type of polarisation is less fully understood.
Polanyi’s paradox
The leading explanation is proposed by Autor (2014), who suggests that automating middle-skill jobs was easier than automating lower- and higher-skill jobs. This is because middle-skill jobs were characterised by repetitive cognitive tasks, which are particularly suitable for computer-based automation. Jobs at the lower end of the wage or skill distribution, on the other hand, involve a greater share of manual tasks, which humans can perform intuitively without being able to explain exactly how they do it. In this way, Autor suggests that polarising effects of automation are related to Polanyi’s (1966) claim that “we know more than we can tell”. The lack of a precise set of instructions means that computers could not take over these tasks. This view has been highly influential in shaping the debate about the effects of existing automation and its future course (e.g. Frey and Osborne 2013, Arntz et al. 2017).
Low wages
In a new paper (Acemoglu and Loebbing 2022), we revisit why automation has led to the erosion of middle-wage occupations. We propose a general equilibrium model where workers with different levels of skills compete with machines for jobs of various levels of complexity.
Figure 1 illustrates the key condition for interior automation, whereby jobs in the middle of the wage distribution are automated. This condition requires wages at the bottom of the skill distribution to be low relative to the productivity advantage of machines over humans in these jobs.
Figure 1
Source: Acemoglu and Loebbing (2022).
Notes: The mapping from tasks, ordered by complexity, to workers, ordered by skill level. A range of tasks of intermediate complexity is taken over by capital while tasks at the extremes of the distribution are performed by workers.
This condition confirms that Polanyi’s paradox can account for the pattern of automation we have observed so far, as Autor (2014) suggested — the productivity advantage of machines relative to humans may be particularly pronounced in middle-skill jobs. However, it also proposes a new perspective on polarisation. Interior automation and the resulting polarisation may be consequences of the fact that wages are low at the bottom of the wage distribution, making the automation of low-pay jobs unprofitable. Whether wages are low at the bottom is determined by the entire distribution of productivities and skill supplies, as well as institutional factors (e.g. lack of binding minimum wages for low-skill workers).
When automation is interior, further advances in machine productivity create greater labour market polarisation, pushing workers towards the lower and higher ends of the job complexity distribution. Its impact on inequality tends to be nuanced, however. It is workers closest to the middle of the distribution who experienced the largest relative declines in wages. In particular, lowest-skill workers are partially shielded from the adverse effects of automation, because they are far away from jobs that are being automated.
In fact, our framework clarifies that the workers who will experience declines in their real wages are those who are most similar to machines in terms of their comparative advantage. This refines a conjecture by Norbert Wiener (1950), who suggested: “the automatic machine is the precise economic equivalent of slave labour. Any labour which competes with slave must accept the economic consequences of slave labour.” In our model, this is not true for all labour, but only for certain types of labour that are very close to machines. These tend to be workers with middle skills when automation is interior. Other types of workers will generally experience higher earnings, because automation also increases productivity and labour demand for complementary types of labour.
Future automation
Why automation has so far been interior and polarising has implications for the future of automation. If Polanyi’s paradox is the reason, then only major technological breakthroughs that enable machines to expand their reach to jobs that involve a significant manual and non-routine component can expand automation to low-skill, low-pay occupations. If, on the other hand, it is low wages that have slowed down the automation of these jobs, further improvements in machine productivity (declines in the cost of automation capital) can expand automation to lower-pay occupations.
Our framework thus emphasises that the next stage of automation may easily encroach more of low-pay occupations, and also clarifies that if this happens there will be a qualitative difference in its distributional effects. Once we transition to this next phase that involves machines substituting for low-skill workers in low-pay occupations, automation will no longer create polarising effects, but will instead cause a uniform increase in inequality. Specifically, if we transition to low-skill automation, as illustrated by the right-hand panel of Figure 2, low-skill workers will experience earnings decline relative to high-skill workers.
Figure 2
Source: Acemoglu and Loebbing (2022).
Notes: Starting from a situation of interior automation, an increase in the productivity of machines pushes workers further towards the extremes of the task distribution, inducing employment polarisation (left panel). If machine productivity increases sufficiently, machines take over all tasks at the lower end of the complexity distribution and further improvements in machine productivity push all workers towards more complex tasks (right panel).
Conclusion
Automation technologies are likely to continue to spread throughout the labour markets of both industrialised and emerging economies. Why automation so far has led to labour market polarisation has important implications for the future of automation and inequality. Our research suggests that low wages at the bottom of the distribution may have been an important factor encouraging automation to concentrate in the middle of the skill distribution, and if so, the next stage of automation may have qualitatively different implications, distinct from its so far polarising effects.
References
Acemoglu, D and P Restrepo (2020), “Robots and Jobs: Evidence from US Labor Markets,” Journal of Political Economy 128(6): 2188-2244 (see also “Robots and Jobs: Evidence from the US,” VoxEU.org, 10 April 2017).
Acemoglu, D and J Loebbing (2022), “Automation and Polarization,” NBER Working Paper 30528.
Arntz, M, T Gregory and U Zierahn (2017), “Revisiting the Risk of Automation,” Economics Letters 159: 15-160.
Autor, D (2014), “Polanyi’s Paradox and the Shape of Employment Growth,” NBER Working Paper 20485.
Autor, D and D Dorn (2013), “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market,” American Economic Review 103(5): 1553-1597.
Autor, D, L Katz and M Kearney (2006): “The Polarization of the US Labor Market,” American Economic Review 96(2): 189-194.
Dauth, W, S Findeisen, J Suedekum, N Woessner (2021), “The Adjustment of Labor Markets to Robots,” Journal of the European Economic Association, 19(6), 3104-3153 (see also: “The Rise of Robots in the German Labor Market,” VoxEU.org, 19 September 2017).
Frey, C and M Osborne (2013), “The Future of Employment: How Susceptible are Jobs to Computerisation?”, Technological Forecasting and Social Change 114: 254-280.
Goos, M, A Manning and A Salomons (2009), “Job Polarization in Europe,” American Economic Review 99(2): 58-63.
Graetz, G and G Michaels (2018), “Robots at Work,” Review of Economics and Statistics 100(5): 753-768 (see also: “Estimating the Impact of Robots on Productivity and Employment,” VoxEU.org, 18 March 2015).
Mokyr, J, C Vickers and N Ziebarth (2015), “The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different?”, Journal of Economic Perspectives 29(3): 31-50.
Polanyi, M (1966), The Tacit Dimension, Doubleday.
Wiener, N (1950), The Human Use of Human Beings: Cybernetics and Society, Houghton Mifflin.
| 2022-11-23T00:00:00 |
https://cepr.org/voxeu/columns/automation-and-polarisation
|
[
{
"date": "2022/11/23",
"position": 91,
"query": "job automation statistics"
}
] |
|
How AI is Changing our Concept and Reality of Work
|
How AI is Changing our Concept and Reality of Work
|
https://medium.com
|
[
"Courtanae Heslop"
] |
Conclusion. AI is not a threat to jobs. In fact, it's likely to improve our lives and make work more efficient.
|
How AI is Changing our Concept and Reality of Work Courtanae Heslop 7 min read · Dec 1, 2022 -- 1 Listen Share
Artificial intelligence is changing the way we think about work. For example, AI can help us learn more about our jobs and the world around us than ever before. But this technology also raises concerns about how AI will impact our concept and reality of work in the future.
Artificial intelligence (AI) has plenty of potential, but concerns have been raised about its potential impact on jobs.
You may have heard a lot about how AI will change the world. The technology has plenty of potential, but concerns have been raised about its potential impact on jobs. Many jobs are at risk of being replaced by artificial intelligence (AI), including some that don’t seem like they would be:
Accountants and auditors
Chefs and cooks
Cleaners
In addition to these occupations, automated systems can automate tasks previously performed by humans in many industries, including medicine, manufacturing and farming. Even white-collar professions are susceptible; for example, law firms use programs that produce legal documents without human intervention. To keep up with the rapid pace of technological advancement and innovation sweeping through their industry — or even just stay ahead — companies need employees who understand AI technologies such as machine learning algorithms (MLAs) so they can make better business decisions about how best to incorporate this new technology into their existing processes/systems/strategies for long-term success.”
With AI, machines can learn the characteristics of their tasks and can be made to perform such tasks with greater accuracy than humans.
AI is able to learn from experience. It can be programmed to do a specific task, but it can also be programmed to learn from experience. In the same way that a human learns from his or her own mistakes, AI has the ability to learn what works and what doesn’t work by itself.
It can also be taught by other AIs, humans or itself in order to improve its performance at performing specific tasks — this is called machine learning.
Work done by humans and robots will likely become indistinguishable from each other.
If an AI-based process is developed, it will likely be integrated into a workflow. The next step would be to implement that process in your office and ensure your employees have the resources they need to use it effectively. You may choose to hire more people or retrain current employees on how best to use the new technology. Either way, this transition will take time and cost money.
However, once you’ve invested in this change, there are multiple ways your business could benefit from doing so:
More efficient workflows: If you have processes that are currently done manually by humans and can be automated with AI technology (such as data entry), then you should expect them to become much more efficient since robots don’t get tired or distracted like we do! In addition they’re also unlikely ever get sick or go on vacation so during those times too when there’s not enough manpower around (e., holidays) then workers still need something else available which means again less downtime caused by things like illness etc…
AI is already increasing productivity, and this trend is likely to continue.
You’ve probably heard about how AI is already being used to increase productivity. It’s not a myth; the technology has been proven in many different fields, and there’s no reason to think it won’t continue to do so in the future. The potential for AI is so great that it could help us produce more goods and services with less labor, which would mean better standards of living for everyone, even those who aren’t currently employed by factories or other industries where robots perform manual labor tasks.
In fact, if you look at what’s happening right now in manufacturing (where automation has been growing for decades), you’ll see that there are all sorts of ways that AI can be used to improve efficiency:
Robots are starting to take over simple manual tasks from human workers — for example, changing tires on cars or tightening bolts on machinery parts — which allows these people more time for other tasks related directly back into production processes (like troubleshooting problems). This doesn’t just save money because companies don’t have pay employees anymore; it also means workers’ morale won’t suffer when they’re forced into menial jobs like this one!
With AI, humans may have to be comfortable with a job that may not last for decades or even centuries.
You may have heard that artificial intelligence is going to take your job and it’s true. AI is already replacing jobs done by humans in one industry after another.
This trend is likely to continue and the pace of change may accelerate faster than you think. The idea of a job lasting for decades or even centuries could become obsolete if AI becomes part of your job description.
But this doesn’t mean that work will cease to exist; it just means that workers will need to be comfortable with a job that may only last for weeks or months before being replaced by an AI system. With this new reality, humans must focus on how they can add value rather than doing tasks because they’ve always been done this way before — and because we’re used to doing them ourselves!
Robots are replacing jobs done by humans in one industry after another.
As jobs become more automated, it’s possible that the number of available jobs will decrease. This could mean fewer opportunities for people to earn money and provide for themselves and their families. In some cases, automation can benefit society as a whole — for example, by cutting down on pollution caused by human drivers — but there are also times when it results in job losses and economic hardship for millions of individuals.
The challenges posed by automation are complex because they’re not just about machines replacing people: they involve issues like education reform so people can adjust to new job markets; tax policy changes so governments have revenue streams that support these changes; and even infrastructure improvements so citizens have access to information technology (IT) tools like public Wi-Fi networks
On the bright side, AI could improve the lives of many people who otherwise wouldn’t be able to find a job at all.
While it is easy to dwell on the darker side of AI, there are also many ways it could improve the lives of people who otherwise wouldn’t be able to find a job at all.
For example, AI may be able to help those with disabilities or limited mobility work. In some cases this might mean using new technologies like exoskeletons that allow disabled people greater functionality and mobility. In other cases it might mean AI helping them learn valuable skills from home or their own devices, rather than having to go through traditional training programs that require long commutes and rigid schedules (or in some cases even going through traditional university programs). The elderly could also benefit from these technologies; while they might not be as mobile as younger people, they still have a lot of knowledge and experience that could be put towards useful tasks — and because they’re less likely than younger individuals are likely to move around or relocate frequently due to their age alone, they’ll often need less frequent retraining.
As far as developing countries go: robotics has already been implemented in factories in China and India where it has decreased production costs by 30%. This means these factories can offer lower prices while still increasing profits, which lets them compete internationally without taking advantage of workers by paying them minimum wage instead — which will hopefully result in better working conditions for everyone involved down there since more money being earned means fewer people trying out jobs just so they can support their families back home (which often leads into dangerous situations when something goes wrong). It also means more money available for education programs — since many poor countries lack resources needed for proper schooling — thus allowing more children access textbooks/computers etcetera which increases literacy rates significantly over time.”
There’s no way to know what jobs will lose functionality in the future, how much they will be replaced by robots or how much work they will still need to do under new circumstances.
You can’t know for sure what jobs will lose functionality in the future, how much they will be replaced by robots or how much work they will still need to do under new circumstances.
It’s a project that requires asking yourself questions: What is your ideal life? If you had no responsibilities and could only work on one thing, what would it be? What are all the things you want to learn about? What makes you happy? How do we make sure everyone has enough money for food and shelter without spending their weekdays working at a job that doesn’t fulfill them?
Conclusion
AI is not a threat to jobs. In fact, it’s likely to improve our lives and make work more efficient.
| 2022-12-01T00:00:00 |
2022/12/01
|
https://medium.com/@courtanaeheslop/how-ai-is-changing-our-concept-and-reality-of-work-cade40f3e68e
|
[
{
"date": "2022/12/01",
"position": 2,
"query": "AI impact jobs"
},
{
"date": "2022/12/01",
"position": 9,
"query": "AI job losses"
},
{
"date": "2022/12/01",
"position": 3,
"query": "AI replacing workers"
},
{
"date": "2022/12/01",
"position": 6,
"query": "future of work AI"
},
{
"date": "2022/12/01",
"position": 7,
"query": "artificial intelligence workers"
}
] |
How do you think artificial intelligence will affect the GIS ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
And even if AI cant automate your entire job, it will have plenty of impact if it automates 75% of it. Three quarters of your colleagued will get fired, and ...
|
With the rise of AI image and video generators, there’s been a quickly growing conversation among the art community about how AI will inevitably and drastically affect the art industry. Many artists have already begun losing jobs as companies replace roles with AI—and it’s hardly the tip of the iceberg.
When it comes to GIS as a career, what types of jobs and workflows do you forsee being replaced, and what others do you think will hold out? What skills and jobs do you think a human will be able to do the longest before AI catches up?
| 2022-12-01T00:00:00 |
https://www.reddit.com/r/gis/comments/zh2ydk/how_do_you_think_artificial_intelligence_will/
|
[
{
"date": "2022/12/01",
"position": 3,
"query": "AI impact jobs"
}
] |
|
Impact of Job Demands on Employee Learning
|
Impact of Job Demands on Employee Learning: The Moderating Role of Human–Machine Cooperation Relationship
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Wang Sen",
"School Of Management",
"Beijing Union University",
"Beijing",
"Zhu Xiaomei",
"Deng Lin",
"Lubar School Of Business",
"University Of Wisconsin-Milwaukee",
"Milwaukee",
"Wi"
] |
by W Sen · 2022 · Cited by 12 — New artificial intelligence (AI) technologies are applied to work scenarios, which may change job demands and affect employees' learning.
|
New artificial intelligence (AI) technologies are applied to work scenarios, which may change job demands and affect employees' learning. Based on the resource conservation theory, the impact of job demands on employee learning was evaluated in the context of AI. The study further explores the moderating effect of the human–machine cooperation relationship between them. By collecting 500 valid questionnaires, a hierarchical regression for the test was performed. Results indicate that, in the AI application scenario, a U-shaped relationship exists between job demands and employee learning. Second, the human–machine cooperation relationship moderates the U-shaped curvilinear relationship between job demands and employees' learning. In this study, AI is introduced into the field of employee psychology and behavior, enriching the research into the relationship between job demands and employee learning.
1. Introduction
Nowadays, artificial intelligence (AI) is rapidly empowering traditional industries. For example, speech recognition, driverless cars, machine translation, and industrial robots are all widely applied in service, manufacturing, and other industries, thereby changing job demands for employees [1]. Jobs that are highly repetitive and easily simulated by artificial machines are being replaced, and employees are taking on more creative jobs. For instance, a “robot advisor” in the banking industry can automatically adjust financial investment portfolios according to income goals and risk tolerance of customers. Moreover, employees need to provide more humane services and create financial projects with more investment value. Human resource specialists no longer screen complicated resumes of candidates but instead focus more on providing enterprises with flexible and suitable talent training strategies for enterprise development. Changes in job demands pose new challenges to employees. An Oracle survey found that 51% of employees could not adapt to the company's AI development and had negative emotional experiences, which reduced their enthusiasm for learning. Therefore, how to make employees actively adapt to the changes in AI job demands and maintain continuous learning is of important practical significance.
1.1. Reviews of the Effects of AI on Employee Behavior The effects of AI on employees have attracted much attention from researchers in various areas, and most existing research focuses on the macroscopic level. On the one hand, these studies highlighted that AI influences the labor force across industries and sectors [2]. Acemoglu and Restrepo [3] found that AI technologies can increase employment demand in nonsmart sectors by boosting overall economic productivity. Dauth et al. [4] noted that introducing AI technology will generate new jobs and absorb employees. On the other hand, recent studies have highlighted the measurement of the employment substitution risk of AI technology. Frey and Osborne [5] pioneered a method for measuring occupational substitutable risk and predicted that 47% of United States-based jobs face a high substitution risk. Some scholars focus on the effect of AI on employee motivation and satisfaction [6]. Based on the self-determination theory, Arnaud and Chandon [7] found that monitoring systemic extensiveness has a negative effect on employees' intrinsic motivation. Based on social information processing theory, Stanton and Julian [8] discovered that communication with AI fails to convey interpersonal cues. Moreover, employees in the organization develop more task-oriented, instrumental connections [9] than emotional connections [10], which leads to more social undermining [11]. Our study advances this line of research in the context of AI's introduction into firms by determining how job demands influence employee learning.
1.2. The Effect of Job Demands on Employee Learning in the Context of AI With the introduction of AI into enterprises, job demands are characterized by the diversification of cross-border skills, high-level skills, and the complexity of human–computer and interpersonal cooperation skills [12]. According to the job demands resource model (JD-R model), job demands directly change employees' psychological resources [13]. Employees need to establish and adapt emotional and relationship resources for working with intelligent machines. These resources are important sources to stimulate the active learning of individuals [14]. Based on the theory of conservation of resources, individuals will gain or lose resources during their interactions with surrounding environmental elements. Facing the loss of resources, individuals are more inclined to adopt passive, withdrawal-based, and rebellious coping psychology and behavior, whereas acquiring resources makes individuals more inclined to adopt active psychology and behavior [15]. With the introduction of AI into firms, on the one hand, human–machine interaction causes several machines to replace employees' duties. Moreover, the anxiety of “machine substitution” diminishes employees' psychological resources, which is conducive to establishing negative emotions and impairing the formation of relationships [16]. On the other hand, intelligent machines replace employees to complete simple and repetitive tasks [17] and increase employees' work efficiency. Therefore, the establishment of positive emotional relationships between humans and machines is strengthened, and individual learning is promoted. Two diametrically opposed emotions act on employee learning simultaneously, which may lead to a periodic decline or increase in the impact of changes in work requirements on employee learning. This case provides a theoretical basis and a nonlinear perspective for exploring the relationship between job demands and employee learning. In addition, we attempt to determine the boundary conditions whereby job demands affect employee learning. In the application scenario of AI technology, the continuous interaction between employees and intelligent machines has given birth to the human–machine cooperation relationship between employees and intelligent machines. The human–machine cooperation relationship emphasizes that human beings should interact and cooperate with machines to complete tasks [18, 19]. After the introduction of intelligent machines into enterprises, the human–machine cooperation relationship was formed. The daily tasks, such as repeatability, compliance, and system processing, were more often undertaken by machines [20], and the creative, social, and interpersonal tasks were undertaken by employees [17]. The human–machine cooperation relationship has changed employees' knowledge, emotional, and relationship resources [21] and caused psychological and emotional changes in employees. Therefore, the human–machine cooperation relationship may affect the relationship among job demands, competency needs, and employee learning. Based on the self-determination theory, this study reveals the mechanism of impact of job demands on employee learning in the application scenario of AI. Specifically, the study examines how changes in job demand affect employee learning in AI application scenarios. Is the indirect effect of job demands on employee learning affected by the human–machine cooperation relationship? By collecting 500 valid questionnaires from 100 AI application enterprises, the results show that job demands have a nonlinear impact on learning. That is, the improvement of job demands initially declines employees' learning and then increases. Thus, the stronger the human–machine cooperation relationship, the more significant the influence of job demands on employee learning.
1.3. Contribution This study aims to contribute theoretically to the following aspects. First, this study estimates the impact of job demands on employees' psychology under the background of AI and expands the research on the relationship between job demands and employee learning. Previous studies were mostly based on the JD-R model to evaluate the impact of job demands on individual psychology [22]. Studies of the relationship between job demands and employee learning are limited. In addition, previous studies found that a linear relationship exists between them in traditional working scenarios [15]. The present study focuses on AI application scenarios and finds that there exists a nonlinear relationship between job demands and employee learning. Second, this study assesses the impact of job demands on employee learning from the perspective of human–machine cooperation. The existing literature expands on the internal and external variables of the organization, such as job remodeling, organizational factors, and family factors, which can affect the relationship between job demands and employee psychology. This study finds that in the AI scene, a new type of interpersonal relationship has been formed between humans and intelligent machines. This kind of human–computer cooperation relationship plays a regulating role between job demands and employee learning, extending the JD-R model to a certain extent.
| 2022-12-06T00:00:00 |
2022/12/06
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC9747302/
|
[
{
"date": "2022/12/01",
"position": 5,
"query": "AI impact jobs"
},
{
"date": "2022/12/01",
"position": 14,
"query": "machine learning job market"
},
{
"date": "2022/12/01",
"position": 36,
"query": "future of work AI"
},
{
"date": "2022/12/01",
"position": 5,
"query": "workplace AI adoption"
}
] |
The Impact of AI on Job Roles, Workforce, and Employment
|
The Impact of AI on Job Roles, Workforce, and Employment: What You Need to Know
|
https://www.innopharmaeducation.com
|
[] |
According to a report by the World Economic Forum, by 2025, AI will have displaced 75 million jobs globally, but will have created 133 million new jobs. This ...
|
The Impact of AI on Job Roles, Workforce, and Employment: What You Need to Know
Artificial Intelligence (AI) is changing the job market, creating new types of jobs while automating routine tasks. With 20-50 million new jobs expected by 2030, AI is creating and enhancing jobs in healthcare, pharmaceuticals and other industries.
While some industries may experience significant job displacement, the economy is expected to benefit from increased productivity and output. As AI continues to evolve, understanding its impact on employment and the economy is crucial.
AI is rapidly transforming the workforce, with significant changes already apparent in the job market and employment landscape. As AI continues to develop and evolve, businesses and workers must adapt to stay competitive and efficient. In this blog, we will explore how AI is affecting the workforce, how it can help workers and businesses become more effective, and the potential benefits and drawbacks of implementing AI on a larger scale.
Impact of AI on Job Roles
The rise of automation and AI is transforming the workplace, impacting job roles across various industries, including high-tech manufacturing. Thanks to advanced technologies, many manual and repetitive tasks can now be automated, leading to increased efficiency and productivity.
But this shift is also causing job roles to evolve, with some becoming obsolete while new ones emerge. For example, manufacturing workers need to acquire new skills to operate and maintain machines and robots that are taking over manual tasks. Additionally, AI integration into high-tech manufacturing processes is creating new job roles like data analysts, AI programmers, and machine learning specialists.
These emerging job roles require a combination of technical skills and a deep understanding of business processes. The jobs of the future will require a mix of technical skills, creativity, and adaptability to leverage the power of automation and AI effectively.
As AI continues to transform the job market and employment landscape, individuals need to adapt to stay relevant and competitive in their careers. One way to adapt is to focus on developing skills that are in high demand, such as data analytics, machine learning, and programming. This can involve taking courses, like our micro-cred Certificate in Data Visualisation and Analysis, attending workshops, or earning certifications in these fields.
Another way to adapt is to embrace the opportunities presented by AI, such as using it to augment human capabilities and work more efficiently. This may involve learning how to work with AI tools and technologies and collaborating with AI systems to achieve better results.
Additionally, individuals should stay informed about the latest developments in AI and its impact on their industries. This can involve following industry publications, attending conferences, and networking with peers and experts.
Finally, individuals should remain flexible and adaptable, as the job market and employment landscape continues to evolve rapidly in response to AI and other technological advances. By embracing change and continually developing their skills and knowledge, individuals can thrive in a world where AI is transforming the way we work.
Impact of AI on the Workforce
AI’s impact on the workforce is multifaceted. It involves the automation of repetitive and routine tasks, changing skill requirements, and job displacement. This can be beneficial for employees as it frees them up to focus on more complex and creative work, but it can also create concerns about job displacement and changes in the demand for certain types of jobs. However, AI is also creating new job opportunities, especially in data analytics, machine learning, and AI development.
Despite these potential benefits, there are also concerns about the drawbacks of implementing AI on a larger scale in the workforce. One potential concern is job displacement, which can lead to unemployment and the need for reskilling and upskilling. Another concern is the potential for bias and discrimination in algorithms, which can have negative consequences for marginalised individuals and communities.
Privacy and security are also major concerns regarding the impact of AI on the workforce. As AI becomes more advanced, it is important to ensure that personal data is protected, and AI systems are secure against cyberattacks. Nonetheless, AI can also enhance efficiency and productivity, and its advancements may lead to new job opportunities for workers with the right skills and knowledge
Impact of AI on Employment
Artificial Intelligence (AI) is changing the job market, creating new types of jobs and enhancing existing ones. As AI continues to develop and evolve, it is important to understand how it is impacting the job market, the types of new jobs that are emerging, and the potential impact on unemployment rates and the economy as a whole.
According to a report by McKinsey & Company, AI is expected to create 20-50 million new jobs globally by 2030. These new jobs will be in a range of industries, including healthcare, manufacturing, and finance. Some of the new job roles that are emerging as a result of AI include:
AI Trainers and Teachers: These are individuals who are responsible for training and teaching AI systems. They ensure that AI algorithms are accurate and effective, and they also develop new AI applications and systems.
These are individuals who are responsible for training and teaching AI systems. They ensure that AI algorithms are accurate and effective, and they also develop new AI applications and systems. Data Analysts and Scientists: With the increase in data generated by AI systems, there is a growing demand for individuals who can analyse and interpret this data. Data analysts and scientists use AI tools to analyse data and identify patterns and insights that can help businesses make better decisions.
With the increase in data generated by AI systems, there is a growing demand for individuals who can analyse and interpret this data. Data analysts and scientists use AI tools to analyse data and identify patterns and insights that can help businesses make better decisions. Human-Machine Teaming Managers : As AI becomes more integrated into the workplace, there is a growing need for individuals who can manage the interaction between humans and machines. Human-machine teaming managers ensure that AI systems work effectively with human workers, enhancing productivity and efficiency.
: As AI becomes more integrated into the workplace, there is a growing need for individuals who can manage the interaction between humans and machines. Human-machine teaming managers ensure that AI systems work effectively with human workers, enhancing productivity and efficiency. AI Ethics and Policy Specialists: As AI becomes more prevalent, there is a growing need for individuals who can address the ethical and policy implications of AI. AI ethics and policy specialists ensure that AI systems are developed and used in a responsible and ethical manner.
AI is creating new job opportunities that require skills such as critical thinking, creativity, and problem-solving. Artificial Intelligence is also enhancing existing jobs by improving accuracy and precision in many tasks, such as quality control and data analysis. For example, in healthcare, AI is being used to assist doctors and nurses with diagnosis and treatment recommendations, improving patient outcomes and reducing the workload of healthcare professionals.
The impact of AI on unemployment rates and the economy as a whole is a topic of debate. While AI is creating new job opportunities, it is also leading to job displacement, particularly in industries that rely heavily on routine and repetitive tasks.
According to a report by the World Economic Forum, by 2025, AI will have displaced 75 million jobs globally, but will have created 133 million new jobs. This means that there will be a net gain of 58 million jobs globally, but there will still be significant job displacement in certain industries.
The impact of AI on unemployment rates will also vary by region and industry. For example, the manufacturing industry is likely to experience significant job displacement as a result of AI, while the healthcare and education industries are expected to see significant job growth.
In addition to its impact on employment, AI also has the potential to impact the economy as a whole. AI can lead to increased productivity and output, which can stimulate economic growth. However, there are concerns about the potential for AI to widen the wealth gap, as those with the skills and knowledge to work with AI may earn higher salaries than those who do not have these skills.
Final Thoughts
Technology has been evolving at an unprecedented rate, and with it, our job roles. Automation and AI are changing the way we work, and we are beginning to see significant impacts across various industries. While certain job roles are at risk of being automated, others are evolving to include the use of AI.
As the use of AI continues to grow, it is essential that we take a proactive approach in ensuring that the benefits of AI are balanced with the needs of workers and society as a whole. We must ensure that we are adequately prepared to adapt to the changes in the job market and acquire new skills to thrive in the digital age.
Furthermore, it is crucial to address the potential loss of jobs due to automation. We must develop strategies that support workers who are at risk of displacement and ensure that they have access to training and education to equip them with the skills needed to adapt to new job roles.
Despite the challenges, the integration of AI in job roles has the potential to drive innovation, increase efficiency, and improve our quality of life. By leveraging the full potential of AI, we can create new job opportunities, drive economic growth, and make significant strides in addressing some of the world’s most pressing challenges.
The impact of AI on job roles is significant and far-reaching. It is essential to approach this transformation proactively, ensuring that the benefits of AI are balanced with the needs of workers and society. By doing so, we can create a future where AI and human workers can work together seamlessly to achieve shared goals and drive progress.
| 2023-09-29T00:00:00 |
2023/09/29
|
https://www.innopharmaeducation.com/blog/the-impact-of-ai-on-job-roles-workforce-and-employment-what-you-need-to-know
|
[
{
"date": "2022/12/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2022/12/01",
"position": 98,
"query": "job automation statistics"
},
{
"date": "2022/12/01",
"position": 77,
"query": "AI labor market trends"
},
{
"date": "2022/12/01",
"position": 34,
"query": "robotics job displacement"
},
{
"date": "2022/12/01",
"position": 6,
"query": "AI employment"
},
{
"date": "2023/01/01",
"position": 15,
"query": "AI impact jobs"
},
{
"date": "2023/01/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 98,
"query": "job automation statistics"
},
{
"date": "2023/01/01",
"position": 74,
"query": "AI labor market trends"
},
{
"date": "2023/01/01",
"position": 31,
"query": "robotics job displacement"
},
{
"date": "2023/01/01",
"position": 81,
"query": "machine learning workforce"
},
{
"date": "2023/01/01",
"position": 7,
"query": "AI employment"
},
{
"date": "2023/01/01",
"position": 67,
"query": "artificial intelligence workers"
},
{
"date": "2023/02/01",
"position": 18,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 95,
"query": "job automation statistics"
},
{
"date": "2023/02/01",
"position": 71,
"query": "AI economic disruption"
},
{
"date": "2023/02/01",
"position": 33,
"query": "robotics job displacement"
},
{
"date": "2023/02/01",
"position": 78,
"query": "machine learning workforce"
},
{
"date": "2023/02/01",
"position": 6,
"query": "AI employment"
},
{
"date": "2023/02/01",
"position": 66,
"query": "artificial intelligence workers"
},
{
"date": "2023/03/01",
"position": 14,
"query": "AI impact jobs"
},
{
"date": "2023/04/01",
"position": 3,
"query": "AI employment"
},
{
"date": "2023/04/01",
"position": 84,
"query": "AI labor market trends"
},
{
"date": "2023/04/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2023/04/01",
"position": 68,
"query": "artificial intelligence workers"
},
{
"date": "2023/04/01",
"position": 32,
"query": "automation job displacement"
},
{
"date": "2023/04/01",
"position": 74,
"query": "job automation statistics"
},
{
"date": "2023/05/20",
"position": 3,
"query": "AI economic disruption"
},
{
"date": "2023/05/20",
"position": 11,
"query": "AI employers"
},
{
"date": "2023/05/01",
"position": 2,
"query": "AI employment"
},
{
"date": "2023/05/20",
"position": 7,
"query": "AI impact jobs"
},
{
"date": "2023/05/20",
"position": 27,
"query": "AI job creation vs elimination"
},
{
"date": "2023/05/20",
"position": 1,
"query": "AI labor market trends"
},
{
"date": "2023/05/20",
"position": 15,
"query": "AI replacing workers"
},
{
"date": "2023/05/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2023/05/20",
"position": 9,
"query": "artificial intelligence workers"
},
{
"date": "2023/05/20",
"position": 4,
"query": "automation job displacement"
},
{
"date": "2023/05/20",
"position": 2,
"query": "machine learning job market"
},
{
"date": "2023/05/20",
"position": 1,
"query": "machine learning workforce"
},
{
"date": "2023/05/01",
"position": 97,
"query": "reskilling AI automation"
},
{
"date": "2023/05/20",
"position": 1,
"query": "robotics job displacement"
},
{
"date": "2023/06/01",
"position": 2,
"query": "AI employment"
},
{
"date": "2023/06/01",
"position": 80,
"query": "AI labor market trends"
},
{
"date": "2023/06/01",
"position": 26,
"query": "artificial intelligence employers"
},
{
"date": "2023/06/01",
"position": 93,
"query": "job automation statistics"
},
{
"date": "2023/07/01",
"position": 82,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 20,
"query": "AI impact jobs"
},
{
"date": "2023/08/01",
"position": 5,
"query": "AI employment"
},
{
"date": "2023/08/01",
"position": 15,
"query": "AI impact jobs"
},
{
"date": "2023/08/01",
"position": 83,
"query": "machine learning workforce"
},
{
"date": "2023/09/01",
"position": 27,
"query": "AI impact jobs"
},
{
"date": "2023/09/01",
"position": 99,
"query": "AI workforce transformation"
},
{
"date": "2023/09/01",
"position": 30,
"query": "artificial intelligence employers"
},
{
"date": "2023/09/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2023/09/01",
"position": 66,
"query": "artificial intelligence workers"
},
{
"date": "2023/09/01",
"position": 44,
"query": "automation job displacement"
},
{
"date": "2023/09/01",
"position": 94,
"query": "job automation statistics"
},
{
"date": "2023/09/01",
"position": 82,
"query": "machine learning workforce"
},
{
"date": "2023/09/01",
"position": 33,
"query": "robotics job displacement"
},
{
"date": "2023/10/01",
"position": 79,
"query": "AI labor market trends"
},
{
"date": "2023/10/01",
"position": 29,
"query": "artificial intelligence employers"
},
{
"date": "2023/10/01",
"position": 5,
"query": "artificial intelligence employment"
},
{
"date": "2023/10/01",
"position": 36,
"query": "automation job displacement"
},
{
"date": "2023/10/01",
"position": 82,
"query": "machine learning workforce"
},
{
"date": "2023/10/01",
"position": 76,
"query": "reskilling AI automation"
},
{
"date": "2023/10/01",
"position": 31,
"query": "robotics job displacement"
},
{
"date": "2023/11/01",
"position": 5,
"query": "AI employment"
},
{
"date": "2023/11/01",
"position": 15,
"query": "AI impact jobs"
},
{
"date": "2023/11/01",
"position": 83,
"query": "AI labor market trends"
},
{
"date": "2023/11/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2023/12/01",
"position": 3,
"query": "AI employment"
},
{
"date": "2023/12/01",
"position": 18,
"query": "AI impact jobs"
},
{
"date": "2023/12/01",
"position": 99,
"query": "AI workforce transformation"
},
{
"date": "2023/12/01",
"position": 40,
"query": "artificial intelligence employers"
},
{
"date": "2023/12/01",
"position": 50,
"query": "automation job displacement"
},
{
"date": "2023/12/01",
"position": 93,
"query": "job automation statistics"
},
{
"date": "2024/01/01",
"position": 5,
"query": "AI employment"
},
{
"date": "2024/01/01",
"position": 95,
"query": "AI workforce transformation"
},
{
"date": "2024/02/01",
"position": 7,
"query": "AI employment"
},
{
"date": "2024/02/01",
"position": 16,
"query": "AI impact jobs"
},
{
"date": "2024/02/01",
"position": 46,
"query": "AI job creation vs elimination"
},
{
"date": "2024/02/01",
"position": 91,
"query": "AI labor market trends"
},
{
"date": "2024/02/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2024/02/01",
"position": 47,
"query": "automation job displacement"
},
{
"date": "2024/02/01",
"position": 84,
"query": "job automation statistics"
},
{
"date": "2024/03/01",
"position": 80,
"query": "AI labor market trends"
},
{
"date": "2024/03/01",
"position": 33,
"query": "artificial intelligence employers"
},
{
"date": "2024/03/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2024/03/01",
"position": 34,
"query": "automation job displacement"
},
{
"date": "2024/03/01",
"position": 82,
"query": "machine learning workforce"
},
{
"date": "2024/03/01",
"position": 34,
"query": "robotics job displacement"
},
{
"date": "2024/04/01",
"position": 22,
"query": "AI impact jobs"
},
{
"date": "2024/04/01",
"position": 41,
"query": "automation job displacement"
},
{
"date": "2024/04/01",
"position": 93,
"query": "job automation statistics"
},
{
"date": "2024/04/01",
"position": 81,
"query": "machine learning workforce"
},
{
"date": "2024/04/01",
"position": 35,
"query": "robotics job displacement"
},
{
"date": "2024/05/01",
"position": 18,
"query": "AI impact jobs"
},
{
"date": "2024/05/01",
"position": 83,
"query": "AI labor market trends"
},
{
"date": "2024/05/01",
"position": 41,
"query": "automation job displacement"
},
{
"date": "2024/05/01",
"position": 40,
"query": "robotics job displacement"
},
{
"date": "2024/06/01",
"position": 2,
"query": "AI employment"
},
{
"date": "2024/06/01",
"position": 80,
"query": "AI labor market trends"
},
{
"date": "2024/06/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2024/06/01",
"position": 41,
"query": "automation job displacement"
},
{
"date": "2024/06/01",
"position": 79,
"query": "machine learning workforce"
},
{
"date": "2024/06/01",
"position": 33,
"query": "robotics job displacement"
},
{
"date": "2024/07/01",
"position": 2,
"query": "AI employment"
},
{
"date": "2024/07/01",
"position": 67,
"query": "artificial intelligence workers"
},
{
"date": "2024/07/01",
"position": 39,
"query": "automation job displacement"
},
{
"date": "2024/08/01",
"position": 77,
"query": "AI labor market trends"
},
{
"date": "2024/08/01",
"position": 96,
"query": "AI workforce transformation"
},
{
"date": "2024/08/01",
"position": 19,
"query": "artificial intelligence employers"
},
{
"date": "2024/08/01",
"position": 33,
"query": "automation job displacement"
},
{
"date": "2024/09/01",
"position": 15,
"query": "AI impact jobs"
},
{
"date": "2024/09/01",
"position": 82,
"query": "AI labor market trends"
},
{
"date": "2024/09/01",
"position": 93,
"query": "AI workforce transformation"
},
{
"date": "2024/09/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2024/09/01",
"position": 94,
"query": "job automation statistics"
},
{
"date": "2024/09/01",
"position": 33,
"query": "robotics job displacement"
},
{
"date": "2024/10/01",
"position": 18,
"query": "AI impact jobs"
},
{
"date": "2024/10/01",
"position": 94,
"query": "AI workforce transformation"
},
{
"date": "2024/10/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2024/10/01",
"position": 40,
"query": "automation job displacement"
},
{
"date": "2024/10/01",
"position": 74,
"query": "job automation statistics"
},
{
"date": "2024/10/01",
"position": 81,
"query": "machine learning workforce"
},
{
"date": "2024/11/01",
"position": 2,
"query": "AI employment"
},
{
"date": "2024/11/01",
"position": 19,
"query": "AI impact jobs"
},
{
"date": "2024/11/01",
"position": 88,
"query": "AI labor market trends"
},
{
"date": "2024/11/01",
"position": 27,
"query": "artificial intelligence employers"
},
{
"date": "2024/11/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2024/11/01",
"position": 91,
"query": "job automation statistics"
},
{
"date": "2024/12/01",
"position": 85,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 25,
"query": "AI workforce transformation"
},
{
"date": "2024/12/01",
"position": 25,
"query": "artificial intelligence employers"
},
{
"date": "2024/12/01",
"position": 35,
"query": "automation job displacement"
},
{
"date": "2024/12/01",
"position": 81,
"query": "machine learning workforce"
},
{
"date": "2024/12/01",
"position": 33,
"query": "robotics job displacement"
},
{
"date": "2025/01/01",
"position": 84,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 13,
"query": "AI employment"
},
{
"date": "2025/01/01",
"position": 42,
"query": "automation job displacement"
},
{
"date": "2025/01/01",
"position": 83,
"query": "machine learning workforce"
},
{
"date": "2025/01/01",
"position": 32,
"query": "robotics job displacement"
},
{
"date": "2025/02/01",
"position": 5,
"query": "AI employment"
},
{
"date": "2025/02/01",
"position": 53,
"query": "automation job displacement"
},
{
"date": "2025/02/01",
"position": 80,
"query": "job automation statistics"
},
{
"date": "2025/02/01",
"position": 99,
"query": "machine learning workforce"
},
{
"date": "2025/03/01",
"position": 81,
"query": "AI economic disruption"
},
{
"date": "2025/03/01",
"position": 5,
"query": "AI employment"
},
{
"date": "2025/03/01",
"position": 99,
"query": "AI workforce transformation"
},
{
"date": "2025/03/01",
"position": 30,
"query": "artificial intelligence employers"
},
{
"date": "2025/03/01",
"position": 2,
"query": "artificial intelligence employment"
},
{
"date": "2025/03/01",
"position": 50,
"query": "automation job displacement"
},
{
"date": "2025/03/01",
"position": 84,
"query": "job automation statistics"
},
{
"date": "2025/03/01",
"position": 98,
"query": "machine learning workforce"
},
{
"date": "2025/04/01",
"position": 61,
"query": "AI economic disruption"
},
{
"date": "2025/04/01",
"position": 21,
"query": "artificial intelligence employers"
},
{
"date": "2025/04/01",
"position": 86,
"query": "job automation statistics"
},
{
"date": "2025/04/01",
"position": 98,
"query": "machine learning workforce"
},
{
"date": "2025/04/01",
"position": 37,
"query": "robotics job displacement"
},
{
"date": "2025/05/01",
"position": 5,
"query": "AI employment"
},
{
"date": "2025/05/01",
"position": 18,
"query": "AI impact jobs"
},
{
"date": "2025/05/01",
"position": 88,
"query": "AI labor market trends"
},
{
"date": "2025/05/01",
"position": 1,
"query": "artificial intelligence employment"
},
{
"date": "2025/05/01",
"position": 66,
"query": "artificial intelligence workers"
},
{
"date": "2025/05/01",
"position": 85,
"query": "job automation statistics"
},
{
"date": "2025/05/01",
"position": 26,
"query": "robotics job displacement"
},
{
"date": "2025/06/01",
"position": 62,
"query": "AI economic disruption"
},
{
"date": "2025/06/01",
"position": 2,
"query": "AI employment"
},
{
"date": "2025/06/01",
"position": 69,
"query": "artificial intelligence workers"
}
] |
Incorporating AI impacts in BLS employment projections
|
Incorporating AI impacts in BLS employment projections: occupational case studies : Monthly Labor Review : U.S. Bureau of Labor Statistics
|
https://www.bls.gov
|
[
"Christine Machovec",
"Michael J. Rieley",
"Emily Rolen"
] |
Case studies of AI-related occupational employment impacts. The 2023–33 BLS employment projections incorporate AI-related impacts for several occupations for ...
|
Discussions about advances in artificial intelligence (AI) have become commonplace. In recent years, such advances have received constant news coverage, especially since the deployment of Open AI’s ChatGPT in November 2022.[1] ChatGPT, a natural language processing tool driven by AI technology that allows users to have humanlike conversations, was the first to offer many users the opportunity to directly experience the potential power of AI in their lives. Millions of Americans have since used ChatGPT.[2] Although AI and chatbots are not new, ChatGPT and similar tools have opened a new portal into the world of large language models (LLMs) and their wide-ranging applications.[3] Generative AI (GenAI) tools are becoming increasingly powerful in creating prose, images, videos, and sound. Because of the sheer potential of these tools, many people have been asking how they might affect our future, including that of workers and employment.
The U.S. Bureau of Labor Statistics (BLS) Employment Projections (EP) program approaches AI in the same way as it does other technologies.[4] Established technologies and other structural changes to the labor market have impacts that register in the historical data.[5] BLS projection methods are designed to measure and reflect structural technological changes, and these changes and their employment impacts tend to occur gradually.[6] There have been many claims about new technologies displacing jobs, and although such displacement has occurred in the past, it tends to take longer than technologists typically expect. Various technologies have had occupational impacts throughout recent history, but many affected occupations have still seen employment growth. Although it is always possible that future developments will deviate from historical patterns, BLS projection methods are not designed to capture extremely rapid technological change and, therefore, assume that the overall pace of technological change will be consistent with past experience.
Projecting future employment involves substantial uncertainty, especially in the case of evaluating the future impacts of a developing technology. BLS acknowledges this uncertainty in analyzing the relative likelihood of employment impacts across occupations. In this article, we use case studies to illustrate how BLS considers various factors of uncertainty in its employment projections. The next section provides more details, along with illustrative examples, on how BLS has approached this type of projections work in the past. The section after that presents several case studies, based on EP research done for the 2023–33 projections cycle, examining the potential employment impacts of AI technological advancements on selected occupations in the computer, legal, business and financial, and architecture and engineering occupational groups. The final section concludes, summarizing our results and noting EP’s plans for future research.
Background and research considerations
EP research connects the potential employment impacts of a new technology with data trends. The goal of this approach is to determine whether these impacts are likely to continue as the new technology grows in adoption and maturation or whether they are likely to diminish as the benefits of the technology are fully realized. For an emerging technology that is not yet reflected in historical data, EP aims to determine whether there is sufficient evidence to support a conclusion about the direction and magnitude of the technology’s future impact on the labor market.
One illustrative example of how this research has affected BLS projections involves photographic process workers. To show a decline in labor demand for this occupation in the early 2000s, when digital cameras were on the verge of displacing most film cameras, EP adjusted its models accordingly. Digital cameras improved on an already-existing technology, and the path to integrating them into business operations and consumer lives was clear. Digital cameras were already replacing film cameras, and the employment impacts of this shift were directly implied by the speed and maturation of technological change. Despite the absence of historical data showing employment declines for photographic process workers, EP projected that employment in the occupation would decline 23.6 percent from 2004 to 2014. Indeed, employment in the occupation started to fall precipitously in the early 2000s, declining from a peak of 86,300 in 2004 to 28,800 in 2014, with a further decline to only 9,200 in 2023.[7]
By contrast, EP did not make any adjustments to its models for another occupation, truck drivers, despite speculations about autonomous vehicles potentially affecting this occupation in the 2010s. At the time, EP determined that the potential impacts of autonomous vehicles were too uncertain, judging that any such impacts were not likely to be felt in the short-to-medium term. Specifically, EP deemed that incorporating the new technology into actual work would likely take time because of regulatory and public safety concerns. So far, history has shown this assumption to have been correct: autonomous trucking remains a developing technology and has not yet had any meaningful employment impacts. Employment of heavy and tractor-trailer truck drivers was about 1.7 million in 2012 and had grown to over 2.2 million by 2023.[8]
The timing and scale of many potential impacts of GenAI are too uncertain to be reflected in BLS projections. New technologies such as autonomous vehicles or AI are harder to assess than technologies that constitute incremental improvements. The lack of relevant historical data on AI technology necessitates assumptions about both the time frame and scale of technological impacts. Brand-new technologies inherently present many sources of uncertainty, including those related to degree of usefulness, developmental roadblocks, regulatory constraints, and pace and cost of adoption. EP generally makes adjustments to its models only if its research on a new technology provides clear expectations for the technology’s employment impacts.
Each occupation involves a set of tasks. Even when a new technology advances rapidly, it takes time for employers and workers to determine how to best incorporate the technology into their work. Although new technologies may change the composition or weighting of tasks performed by workers in an occupation, sometimes dramatically, they may still have no employment impacts. For example, the relatively recent ubiquitousness of smartphones has likely changed the operation of many workplaces, possibly affecting occupational tasks. However, it is unlikely that many workers have lost their jobs to smartphones. In the case of AI, it is important to keep in mind that BLS projections are not about the overall impact of the new technology, but rather about its potential employment impacts.
Case studies of AI-related occupational employment impacts
The 2023–33 BLS employment projections incorporate AI-related impacts for several occupations for which high exposure to automation is deemed likely. These impacts are discussed in a recent Monthly Labor Review article titled “Industry and occupational employment projections overview and highlights, 2023–33.”[9] However, researchers have identified additional occupations potentially susceptible to AI-related impacts,[10] although the employment trajectories of these occupations remain uncertain.
This section discusses some of these occupations, explaining how any potential AI impacts on them are incorporated— or not—in the 2023–33 National Employment Matrix.[11] The occupations featured in this article are concentrated in the computer, legal, business and financial, and architecture and engineering occupational groups. The employment projections outlined below reflect research based on EP’s interpretation of information available in June 2024, when the program was finalizing its 2023–33 projections. Other interpretations and conclusions derived from existing information are also possible. AI is a new and dynamic technology, and new information about its development and potential uses is continually being released. As such information becomes available, EP will continue to assess the potential employment impacts of AI tools and make appropriate updates to its future sets of projections.
Computer occupations
Workers in many computer-related occupations use AI in their day-to-day work. Programming is one of many work activities in which new LLMs and GenAI are well suited to augment worker efforts and increase productivity.[12] Software developers can use GenAI to develop, test, and document code; improve data quality; and build user stories that articulate how a software feature will provide value.[13] The effects of AI proliferation on this occupation are highly uncertain. On the one hand, AI is well suited for the occupation’s tasks; on the other hand, increased productivity from the use of AI may lower prices and increase demand for software products, thus boosting employment demand for software developers. In addition, AI itself may lead to increased demand for software developers, who may be needed to develop AI-based business solutions and maintain AI systems. Thus, despite its exposure to GenAI applications, this occupation is unlikely to experience a decline in employment, because robust software needs are expected to support continued demand for its workers. Although it is always possible that AI-induced productivity improvements will outweigh continued labor demand, there is no clear evidence to support this conjecture. Given these considerations, BLS projects employment of software developers to increase 17.9 percent between 2023 and 2033, much faster than the average for all occupations (4.0 percent). (See table 1.)
Database administrators (DBAs) and database architects are other examples of computer occupations susceptible to potential AI impacts. With ever-growing volumes of data comes the need for data maintenance and security. Increased labor demand for DBAs and database architects is expected to stem from growing demand for cloud computing and data infrastructure. According to one report on the potential applications of GenAI across industries, superannuated data infrastructure is among the greatest obstacles to business implementation of the technology: “As organisations adopt GenAI, tech, cloud and data infrastructure will need to be set up effectively—both the technology ecosystem and the capabilities within it. For example, data in many organisations is spread across multiple systems and data infrastructure will need to be set up in the right way to be able to benefit from GenAI.”[14] Consistent with this observation, a common refrain heard by BLS staff from other researchers, economists, and professors on the subject of AI is the claim that insufficient data infrastructure is the greatest obstacle to AI usage.[15] Greater adoption of AI is expected to lead to greater database complexity, which could support additional demand for DBAs and database architects.[16] As businesses race to integrate AI solutions into their workflows, they will need DBAs and database architects to navigate the obstacles to such integration.
However, AI tools also have the potential to perform many DBA tasks, such as generating code, predictive analysis, and system integration.[17] Survey results documented in a 2024 industry report by Redgate suggest that, at the time of the survey, more than half of respondents were either already using AI to improve the productivity of database management work or considering doing so in the near future.[18] As more AI tools are integrated into systems management, the tasks of DBAs and database architects will likely evolve.[19] Although some people may see these productivity enhancements as potentially leading to slower job growth in these occupations, their employment effects are projected to be outweighed by those of strong business demand for database management and data infrastructure solutions. Indeed, integration of AI into business operations is likely to spur even more demand for these workers. In the 2023–33 projections, employment of DBAs is projected to grow 8.2 percent, faster than average, and employment of database architects is projected to grow 10.8 percent, much faster than average. (See table 1.)
Table 1. Employment projections for selected computer occupations susceptible to potential artificial intelligence impacts, 2023–33 2023 NEM occupation title 2023 NEM occupation code Employment, 2023
(thousands) Employment, 2033
(thousands) Numeric change, 2023–33
(thousands) Percent change, 2023–33 Total, all occupations 00-0000 167,849.8 174,589.0 6,739.2 4.0 Computer occupations 15-1200 5,021.8 5,608.5 586.8 11.7 Database administrators 15-1242 80.5 87.1 6.6 8.2 Database architects 15-1243 61.4 68.0 6.6 10.8 Software developers 15-1252 1,692.1 1,995.7 303.7 17.9
Legal occupations
GenAI, particularly LLMs, can potentially greatly enhance productivity in the legal services industry. This technology can sift through massive amounts of information and synthesize findings, thereby reducing the time lawyers and paralegals spend on various tasks related to document review. Among the legal, tax, and accounting professionals surveyed for one study in 2023, 67 percent “forecasted the emergence of AI and generative AI to have either a transformational or high-impact change to their profession over the next five years.”[20] Improved productivity was the top priority for 3 in 4 law firms surveyed, followed by improved internal efficiency.[21] Similarly, another 2023 study found that the “legal profession had the second highest [AI] exposure, with an estimated 44% of tasks susceptible to automation.”[22]
In recent years, several companies have started to provide machine learning or GenAI services to law firms for tasks such as contract review, pretrial discovery, or research.[23] For example, some existing AI tools can comb legal libraries during the discovery process, while others can serve as study guides or encyclopedias of the law.[24] Researchers at Stanford Law School confirm these developments, noting that “a dizzying number of legal technology startups and law firms are now advertising and leveraging LLM-based tools for a variety of tasks.”[25] Some of these tools are already in use to some degree, and more law firms are expected to adopt them over the 2023–33 projections decade. Partly for this reason, employment growth in the legal services industry is projected to be slower (1.6 percent) than growth for the total economy (4.0 percent).[26]
Among legal occupations, paralegals and legal assistants are expected to see the strongest employment impact from the productivity gains afforded by GenAI.[27] Although the new technology is also likely to increase productivity for lawyers, workers in this occupation will still need to review output from LLMs and to conduct tasks for which clients may prefer human interaction (e.g., advising services). Despite GenAI’s great potential for enhancing the efficiency of producing legal briefs and other documents, that output will still require detailed review by lawyers because it may contain errors or biases.[28] Given that accuracy is very important in legal settings and existing AI tools cannot provide the legal context a human can, there will continue to be a need for human reviewers to understand ambiguities, identify model hallucinations or other errors, and ensure that true intent is captured.[29] In addition, the efficiency gains from using LLMs may allow lawyers to spend more time on networking, trial preparation, and other aspects of legal work.[30]
These productivity gains can potentially lower costs for clients by reducing billable hours, allowing law firms to offer more competitive pricing. Because of continued demand for legal services, employment of lawyers is projected to grow 5.2 percent through 2033, about as fast as the average for all occupations. (See table 2.) This projected growth rate, which is slower than that in the two previous projections sets, reflects an expectation of modest productivity gains against a backdrop of strong overall demand (reflected in the projection for the legal services industry, which employs more than half of all lawyers).
Table 2. Employment projections for selected legal occupations susceptible to potential artificial intelligence impacts, 2023–33 2023 NEM occupation title 2023 NEM occupation code Employment, 2023
(thousands) Employment, 2033
(thousands) Numeric change, 2023–33
(thousands) Percent change, 2023–33 Total, all occupations 00-0000 167,849.8 174,589.0 6,739.2 4.0 Legal occupations 23-0000 1,394.4 1,446.2 51.8 3.7 Lawyers 23-1011 859.0 903.3 44.2 5.2 Paralegals and legal assistants 23-2011 366.2 370.5 4.3 1.2
Business and financial operations occupations
The business and financial operations occupational group also may be affected by AI. The new technology is largely expected to improve productivity growth for certain occupations within the group, thus moderating or reducing (but not eliminating) employment demand for them.
Among the occupations included in this group are two insurance-related occupations: claims adjusters, examiners, and investigators; and insurance appraisers, auto damage. Claims adjusting and examining involve assessing damage to property (often a house or a car) in order to estimate the required payouts insurance companies must pay to their customers. In recent years, insurance companies have deployed drones to take aerial photographs of sites, without sending a human examiner into the field.[31] In the future, AI is expected to work in tandem with drone technology to further bolster productivity. Once photographs are taken, the analysis and initial payout estimates, traditionally prepared by an adjuster, can be autogenerated by AI.[32] In addition, AI can speed up other tasks performed by claims adjusters, examiners, and investigators, including summarizing “policies, documents and other unstructured forms of content.”[33] Insurance appraisers, auto damage—a smaller occupation—are also expected to be affected by this technology because they can use the same damage-assessment software for cars and trucks.[34] These developments mean that more insurance-related work can be done with fewer employees, reducing employment demand. Over the 2023–33 projections period, employment of claims adjusters, examiners, and investigators is projected to decline 4.4 percent, and employment of insurance appraisers, auto damage, is projected to decline 9.2 percent. (See table 3.)
Personal financial advisors, another occupation in the business and financial operations occupational group, have already begun to see job competition from AI. Specifically, app-based “robo-advisors” have started to compete with human advisors by providing automated financial advice on how much users should spend, save, and invest and how they should allocate their investments. Robo-advisors are especially popular with younger people, who tend to be more comfortable with newer technology and have simpler financial planning needs.[35] However, given that the share of older age groups in the U.S. population is expected to increase over the projections period, the underlying demand for human financial advisors is likely to remain strong.[36] Because older people are closer to or past retirement age, they have more accumulated savings and more complex investment advisory needs. A 2020 study found that the uptake of robo-advisors among older adults is “very limited,” reportedly because these individuals are less likely to trust the technology.[37] In addition, a 2023 survey found that the vast majority of robo-advisor users are in their twenties, thirties, and forties, with only 5.9 percent in their sixties or older.[38] These population and usage patterns suggest that the demographic subset more likely to prefer human advisors will have more weight in the personal financial advisory market than the younger, app-friendly subset. Therefore, although AI technology can compete with personal financial advisors at their core tasks, demand for human advisors is still expected to remain very strong over the projections decade. Employment of personal financial advisors is projected to grow 17.1 percent from 2023 to 2033, much faster than average. (See table 3.)
Several “analyst” occupations within the business and financial operations occupational group are also susceptible to potential impacts from AI automation. Because these occupations largely involve desk work in which computer software is already a primary tool of the trade, further software advances driven by AI could raise the productivity of many analysts but are unlikely to eliminate employment demand for them. For example, budget analysts, who are predominantly employed by government agencies, prepare and review budgets and perform socially oriented tasks such as making presentations and answering questions from stakeholders. Although AI will likely speed up the budgeting review process and even offer data visualization tools that can be used in presentations, the communication and customer service tasks of budget analysts (e.g., discussing the nuances and alternative paths of proposed budgets) will likely continue to require conversations between humans and cannot be easily replaced by AI. Given the expectation for a strong floor of demand for the core tasks performed by these workers, employment of budget analysts is projected to grow 3.9 percent from 2023 to 2033, about as fast as the average for all occupations. (See table 3.)
Credit analysts also are experiencing the effects of automation. These workers analyze financial data and prepare reports used by lending firms to determine whether credit can be extended to individuals or businesses. AI can synthesize large amounts of data and reach big-picture conclusions, and, indeed, these tasks are the essence of a credit rating, which combines a range of financial information on a potential borrower into an overall score. As AI improves, the speed and accuracy of producing credit scores and reports will increase.[39] Therefore, credit analysts are likely to see decreasing employment demand, and their employment is projected to decline 3.9 percent from 2023 to 2033. (See table 3.)
By comparison, financial and investment analysts are more protected from the effects of AI. Like personal financial advisors, these workers perform tasks with varying tolerance for automation, depending on the employer. Some financial firms lean heavily on automated trading with algorithm-based sales and purchases throughout a day, whereas others focus on long-term investments based on deliberate decisions made by a team of analysts who consider a wide range of variables.[40] Although the latter investment strategy may benefit from software improvements (such as those providing faster and more useful numeric comparisons across securities),[41] the final investment decisions associated with it will still be made by humans. Therefore, a sizeable share of institutional investment will still rely on financial and investment analysts. Employment of these workers is projected to grow 9.5 percent from 2023 to 2033, much faster than average. (See table 3.)
Table 3. Employment projections for selected business and financial operations occupations susceptible to potential artificial intelligence impacts, 2023–33 2023 NEM occupation title 2023 NEM occupation code Employment, 2023
(thousands) Employment, 2033
(thousands) Numeric change, 2023–33
(thousands) Percent change, 2023–33 Total, all occupations 00-0000 167,849.8 174,589.0 6,739.2 4.0 Business and financial operations occupations 13-0000 10,977.2 11,738.5 761.3 6.9 Claims adjusters, examiners, and investigators 13-1031 345.2 330.0 -15.2 -4.4 Insurance appraisers, auto damage 13-1032 10.5 9.5 -1.0 -9.2 Budget analysts 13-2031 50.8 52.7 2.0 3.9 Credit analysts 13-2041 73.7 70.8 -2.8 -3.9 Financial and investment analysts 13-2051 347.4 380.5 33.1 9.5 Personal financial advisors 13-2052 321.0 375.9 55.0 17.1
Architecture and engineering occupations
GenAI can support many tasks involved in architecture and engineering occupations, potentially increasing worker productivity. In fact, many engineering fields are already harnessing the power of various AI tools. Although these developments may affect labor demand, the unique technical expertise of engineering professionals and existing regulatory requirements create uncertainty about the extent and employment impact of AI adoption. For this reason, underlying demand for engineering services is expected to remain strong, resulting in employment growth for most engineering occupations over the 2023–33 decade.
For example, civil engineers can use AI to account for specific building codes in designing complex mechanical, electrical, and plumbing systems, thus reducing the incidence of errors and design revisions and accelerating the design process . Yet, government-mandated quality-control regulations still require civil and other professional engineers to review and approve any work completed with the use of emerging technologies. Although open-source LLMs and small language models (SLMs) can complete specialized tasks when trained on high-quality data,[42] the level of technical knowledge needed to navigate the intricacies of civil engineering work, such as those involving complex calculations or adherence to codes, will keep civil engineers in demand.[43] As a result, the magnitude of productivity enhancements offered by various AI tools remains unclear, and strong underlying demand for civil engineering services is expected to offset the potential employment impacts of efficiency gains. Employment of civil engineers is projected to grow 6.5 percent from 2023 to 2033, faster than the average for all occupations. (See table 4.)
Two other engineering occupations—aerospace engineers and aerospace engineering and operations technologists and technicians—may also see some of their specific tasks completed or aided by GenAI. Aerospace engineers can leverage GenAI in aircraft design, prescriptive analytics, and predictive maintenance in order to increase productivity and efficiency.[44] Likewise, aircraft and avionics equipment mechanics and technicians can use GenAI tools to perform various aircraft maintenance tasks, achieving more efficient and timely maintenance processes.[45] Despite these enhancements, demand for workers in these occupations is expected to be strong because of the need to comply with federal regulations around quality control for passenger aircraft, as well as continued interest in and funding for commercial air transportation. Therefore, although both occupations are expected to see productivity improvements from GenAI, they are still projected to add jobs over the projections period. Employment of aerospace engineers is projected to grow 6.0 percent from 2023 to 2033, faster than average, and employment of aerospace engineering and operations technologists and technicians is projected to grow 7.9 percent, also faster than average. (See table 4.)
Electrical and electronic engineering occupations may be even more insulated from any employment impacts stemming from the increased use of GenAI. This condition is due to the vast need for electrical and electronic circuitry and infrastructure modernization to support grid updates, electric-vehicle (EV) manufacturing, and other activities in industries reliant on electrical systems. So far, companies have released GenAI tools to more efficiently complete semiconductor chip and electrical circuit design tasks and related activities.[46] For computer hardware engineers, the greatest productivity enhancements are estimated to come from increased efficiency in debugging tasks aided by LLMs.[47] However, despite the rising use of GenAI and LLMs in electrical and electronic device engineering activities, employment of the workers performing these activities is still expected to grow. Over the next decade, the need for energy-efficient electronic features in EVs and electronic control units across products is likely to be robust,[48] driving up labor demand. As a result, between 2023 and 2033, employment is expected to grow for electrical and electronics engineers (9.1 percent), electrical and electronic engineering technologists and technicians (3.0 percent), and computer hardware engineers (7.2 percent). (See table 4.)
Table 4. Employment projections for selected architecture and engineering occupations susceptible to potential artificial intelligence impacts, 2023–33 2023 NEM occupation title 2023 NEM occupation code Employment, 2023
(thousands) Employment, 2033
(thousands) Numeric change, 2023–33
(thousands) Percent change, 2023–33 Total, all occupations 00-0000 167,849.8 174,589.0 6,739.2 4.0 Architecture and engineering occupations 17-0000 2,639.7 2,819.7 180.0 6.8 Aerospace engineers 17-2011 68.9 73.0 4.1 6.0 Civil engineers 17-2051 341.8 363.9 22.1 6.5 Electrical engineers 17-2071 189.1 206.3 17.2 9.1 Electronics engineers, except computer 17-2072 98.7 107.6 8.9 9.1 Aerospace engineering and operations technologists and technicians 17-3021 11.0 11.9 0.9 7.9 Electrical and electronic engineering technologists and technicians 17-3023 99.6 102.6 3.0 3.0
Conclusion
The potential for AI, particularly GenAI, to disrupt future employment has been a prominent topic in recent economic commentary. BLS employment projections consider and reflect the productivity-enhancing effects of automation and a wide range of technologies, including AI, on occupations and industries. Over the 2023–33 projections period, AI is expected to primarily affect occupations whose core tasks can be most easily replicated by GenAI in its current form. These occupations include medical transcriptionists and customer service representatives, whose employment is projected to decline by 4.7 and 5.0 percent, respectively, through 2033.[49]
Other occupations also may see AI impacts, although not to the same extent. For instance, computer occupations may see productivity impacts from AI, but the need to implement and maintain AI infrastructure could in actuality boost demand for some occupations in this group. Among legal occupations, paralegals and legal assistants are likely to experience lower employment demand because of LLM adoption, while lawyers are expected to be less affected. Within business and financial operations occupations, insurance adjusters and appraisers are expected to see reduced employment demand, with AI being able to quickly produce monetary estimates of property damage. Meanwhile, other occupations in this group, such as personal financial advisors, will likely continue to see strong employment growth because demand for human counsel in complex financial matters will persist, particularly for older clients. Architecture and engineering occupations may see some productivity gains from GenAI, but these gains will likely be in line with those afforded by software and other technological advancements in prior decades. As a result, occupations in this group are not expected to see substantial GenAI-driven reductions in employment demand over the projections period.
Each year, BLS issues new 10-year projections that incorporate new data, research, and analysis. EP continually conducts research on new technologies, including AI and LLMs, to better understand their potential employment and economic impacts over the projections period. Besides reflecting these findings in its 10-year projections, the program also evaluates previous projections, explores shifts among skills that are in demand, and considers the potential for technology to create new types of jobs. EP will continue to monitor AI as it evolves, ensuring that BLS projections reflect an updated assessment of the latest developments in this technology and its likely employment impacts.
| 2022-12-01T00:00:00 |
https://www.bls.gov/opub/mlr/2025/article/incorporating-ai-impacts-in-bls-employment-projections.htm
|
[
{
"date": "2022/12/01",
"position": 6,
"query": "artificial intelligence employment"
},
{
"date": "2022/12/01",
"position": 21,
"query": "job automation statistics"
},
{
"date": "2022/12/01",
"position": 35,
"query": "AI labor market trends"
},
{
"date": "2022/12/01",
"position": 23,
"query": "ChatGPT employment impact"
},
{
"date": "2022/12/01",
"position": 90,
"query": "AI employers"
},
{
"date": "2022/12/01",
"position": 11,
"query": "AI employment"
},
{
"date": "2023/01/01",
"position": 20,
"query": "AI impact jobs"
},
{
"date": "2023/01/01",
"position": 6,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 19,
"query": "job automation statistics"
},
{
"date": "2023/01/01",
"position": 33,
"query": "AI labor market trends"
},
{
"date": "2023/01/01",
"position": 23,
"query": "ChatGPT employment impact"
},
{
"date": "2023/01/01",
"position": 92,
"query": "AI employers"
},
{
"date": "2023/01/01",
"position": 9,
"query": "AI employment"
},
{
"date": "2023/02/01",
"position": 20,
"query": "AI impact jobs"
},
{
"date": "2023/02/01",
"position": 6,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 19,
"query": "AI unemployment rate"
},
{
"date": "2023/02/01",
"position": 21,
"query": "job automation statistics"
},
{
"date": "2023/02/01",
"position": 64,
"query": "AI job creation vs elimination"
},
{
"date": "2023/02/01",
"position": 92,
"query": "AI employers"
},
{
"date": "2023/02/01",
"position": 9,
"query": "AI employment"
},
{
"date": "2023/03/01",
"position": 19,
"query": "AI impact jobs"
},
{
"date": "2023/04/01",
"position": 9,
"query": "AI employment"
},
{
"date": "2023/04/01",
"position": 46,
"query": "AI labor market trends"
},
{
"date": "2023/04/01",
"position": 22,
"query": "AI unemployment rate"
},
{
"date": "2023/04/01",
"position": 39,
"query": "AI wages"
},
{
"date": "2023/04/01",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2023/04/01",
"position": 20,
"query": "job automation statistics"
},
{
"date": "2023/05/01",
"position": 8,
"query": "AI employment"
},
{
"date": "2023/05/01",
"position": 14,
"query": "AI unemployment rate"
},
{
"date": "2023/05/01",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2023/05/01",
"position": 18,
"query": "job automation statistics"
},
{
"date": "2023/06/01",
"position": 8,
"query": "AI employment"
},
{
"date": "2023/06/01",
"position": 35,
"query": "AI labor market trends"
},
{
"date": "2023/06/01",
"position": 19,
"query": "job automation statistics"
},
{
"date": "2023/07/01",
"position": 19,
"query": "AI impact jobs"
},
{
"date": "2023/07/01",
"position": 15,
"query": "AI unemployment rate"
},
{
"date": "2023/07/01",
"position": 23,
"query": "ChatGPT employment impact"
},
{
"date": "2023/08/01",
"position": 7,
"query": "AI employment"
},
{
"date": "2023/08/01",
"position": 23,
"query": "AI impact jobs"
},
{
"date": "2023/08/01",
"position": 81,
"query": "AI job creation vs elimination"
},
{
"date": "2023/08/01",
"position": 14,
"query": "AI unemployment rate"
},
{
"date": "2023/08/01",
"position": 60,
"query": "AI wages"
},
{
"date": "2023/08/01",
"position": 22,
"query": "ChatGPT employment impact"
},
{
"date": "2023/08/01",
"position": 20,
"query": "job automation statistics"
},
{
"date": "2023/09/01",
"position": 14,
"query": "AI impact jobs"
},
{
"date": "2023/09/01",
"position": 15,
"query": "AI unemployment rate"
},
{
"date": "2023/09/01",
"position": 22,
"query": "ChatGPT employment impact"
},
{
"date": "2023/09/01",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2023/09/01",
"position": 19,
"query": "job automation statistics"
},
{
"date": "2023/10/01",
"position": 38,
"query": "AI labor market trends"
},
{
"date": "2023/10/01",
"position": 23,
"query": "ChatGPT employment impact"
},
{
"date": "2023/10/01",
"position": 11,
"query": "artificial intelligence employment"
},
{
"date": "2023/11/01",
"position": 10,
"query": "AI employment"
},
{
"date": "2023/11/01",
"position": 22,
"query": "AI impact jobs"
},
{
"date": "2023/11/01",
"position": 38,
"query": "AI labor market trends"
},
{
"date": "2023/11/01",
"position": 15,
"query": "AI unemployment rate"
},
{
"date": "2023/11/01",
"position": 6,
"query": "artificial intelligence employment"
},
{
"date": "2023/12/01",
"position": 8,
"query": "AI employment"
},
{
"date": "2023/12/01",
"position": 19,
"query": "AI impact jobs"
},
{
"date": "2023/12/01",
"position": 14,
"query": "AI unemployment rate"
},
{
"date": "2023/12/01",
"position": 24,
"query": "ChatGPT employment impact"
},
{
"date": "2023/12/01",
"position": 21,
"query": "job automation statistics"
},
{
"date": "2024/01/01",
"position": 7,
"query": "AI employment"
},
{
"date": "2024/01/01",
"position": 72,
"query": "AI wages"
},
{
"date": "2024/02/01",
"position": 9,
"query": "AI employment"
},
{
"date": "2024/02/01",
"position": 22,
"query": "AI impact jobs"
},
{
"date": "2024/02/01",
"position": 68,
"query": "AI job creation vs elimination"
},
{
"date": "2024/02/01",
"position": 34,
"query": "AI labor market trends"
},
{
"date": "2024/02/01",
"position": 8,
"query": "artificial intelligence employment"
},
{
"date": "2024/02/01",
"position": 25,
"query": "job automation statistics"
},
{
"date": "2024/03/01",
"position": 81,
"query": "AI job creation vs elimination"
},
{
"date": "2024/03/01",
"position": 32,
"query": "AI labor market trends"
},
{
"date": "2024/03/01",
"position": 73,
"query": "AI wages"
},
{
"date": "2024/03/01",
"position": 6,
"query": "artificial intelligence employment"
},
{
"date": "2024/04/01",
"position": 97,
"query": "AI employers"
},
{
"date": "2024/04/01",
"position": 16,
"query": "AI impact jobs"
},
{
"date": "2024/04/01",
"position": 18,
"query": "job automation statistics"
},
{
"date": "2024/05/01",
"position": 20,
"query": "AI impact jobs"
},
{
"date": "2024/05/01",
"position": 38,
"query": "AI labor market trends"
},
{
"date": "2024/05/01",
"position": 15,
"query": "AI unemployment rate"
},
{
"date": "2024/05/01",
"position": 62,
"query": "AI wages"
},
{
"date": "2024/05/01",
"position": 22,
"query": "ChatGPT employment impact"
},
{
"date": "2024/06/01",
"position": 8,
"query": "AI employment"
},
{
"date": "2024/06/01",
"position": 37,
"query": "AI labor market trends"
},
{
"date": "2024/06/01",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2024/07/01",
"position": 7,
"query": "AI employment"
},
{
"date": "2024/07/01",
"position": 41,
"query": "AI wages"
},
{
"date": "2024/07/01",
"position": 23,
"query": "ChatGPT employment impact"
},
{
"date": "2024/08/01",
"position": 81,
"query": "AI job creation vs elimination"
},
{
"date": "2024/08/01",
"position": 36,
"query": "AI labor market trends"
},
{
"date": "2024/08/01",
"position": 14,
"query": "AI unemployment rate"
},
{
"date": "2024/08/01",
"position": 22,
"query": "ChatGPT employment impact"
},
{
"date": "2024/09/01",
"position": 22,
"query": "AI impact jobs"
},
{
"date": "2024/09/01",
"position": 40,
"query": "AI labor market trends"
},
{
"date": "2024/09/01",
"position": 15,
"query": "AI unemployment rate"
},
{
"date": "2024/09/01",
"position": 33,
"query": "AI wages"
},
{
"date": "2024/09/01",
"position": 6,
"query": "artificial intelligence employment"
},
{
"date": "2024/09/01",
"position": 20,
"query": "job automation statistics"
},
{
"date": "2024/10/01",
"position": 22,
"query": "AI impact jobs"
},
{
"date": "2024/10/01",
"position": 6,
"query": "artificial intelligence employment"
},
{
"date": "2024/10/01",
"position": 20,
"query": "job automation statistics"
},
{
"date": "2024/11/01",
"position": 89,
"query": "AI employers"
},
{
"date": "2024/11/01",
"position": 8,
"query": "AI employment"
},
{
"date": "2024/11/01",
"position": 23,
"query": "AI impact jobs"
},
{
"date": "2024/11/01",
"position": 44,
"query": "AI labor market trends"
},
{
"date": "2024/11/01",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2024/11/01",
"position": 21,
"query": "job automation statistics"
},
{
"date": "2024/12/01",
"position": 87,
"query": "AI employers"
},
{
"date": "2024/12/01",
"position": 23,
"query": "ChatGPT employment impact"
},
{
"date": "2025/01/01",
"position": 16,
"query": "AI employment"
},
{
"date": "2025/02/10",
"position": 10,
"query": "AI economic disruption"
},
{
"date": "2025/02/01",
"position": 9,
"query": "AI employment"
},
{
"date": "2025/02/10",
"position": 1,
"query": "AI impact jobs"
},
{
"date": "2025/02/01",
"position": 59,
"query": "AI job creation vs elimination"
},
{
"date": "2025/02/10",
"position": 4,
"query": "AI labor market trends"
},
{
"date": "2025/02/10",
"position": 5,
"query": "AI unemployment rate"
},
{
"date": "2025/02/10",
"position": 9,
"query": "AI wages"
},
{
"date": "2025/02/01",
"position": 21,
"query": "ChatGPT employment impact"
},
{
"date": "2025/02/10",
"position": 10,
"query": "artificial intelligence employers"
},
{
"date": "2025/02/10",
"position": 1,
"query": "artificial intelligence employment"
},
{
"date": "2025/02/10",
"position": 7,
"query": "artificial intelligence workers"
},
{
"date": "2025/03/01",
"position": 8,
"query": "AI employment"
},
{
"date": "2025/03/01",
"position": 8,
"query": "artificial intelligence employment"
},
{
"date": "2025/04/01",
"position": 88,
"query": "AI employers"
},
{
"date": "2025/04/01",
"position": 16,
"query": "AI unemployment rate"
},
{
"date": "2025/04/01",
"position": 46,
"query": "artificial intelligence wages"
},
{
"date": "2025/05/01",
"position": 10,
"query": "AI employment"
},
{
"date": "2025/05/01",
"position": 9,
"query": "AI impact jobs"
},
{
"date": "2025/05/01",
"position": 60,
"query": "AI job creation vs elimination"
},
{
"date": "2025/05/01",
"position": 7,
"query": "AI labor market trends"
},
{
"date": "2025/05/01",
"position": 21,
"query": "ChatGPT employment impact"
},
{
"date": "2025/05/01",
"position": 22,
"query": "artificial intelligence employment"
},
{
"date": "2025/06/01",
"position": 8,
"query": "AI employment"
},
{
"date": "2025/06/01",
"position": 8,
"query": "AI impact jobs"
},
{
"date": "2025/06/01",
"position": 60,
"query": "AI job creation vs elimination"
}
] |
|
Artificial Intelligence and Employment: New Cross-Country Evidence
|
Artificial Intelligence and Employment: New Cross-Country Evidence
|
https://pmc.ncbi.nlm.nih.gov
|
[
"Alexandre Georgieff",
"Organisation For Economic Co-Operation",
"Development",
"Paris",
"Raphaela Hyee"
] |
They find that exposure to AI leads to higher employment stability and higher wages, and that this effect is stronger for higher educated and more experienced ...
|
1This publication contributes to the OECD's Artificial Intelligence in Work, Innovation, Productivity and Skills (AI-WIPS) programme, which provides policymakers with new evidence and analysis to keep abreast of the fast-evolving changes in AI capabilities and diffusion and their implications for the world of work. The programme aims to help ensure that adoption of AI in the world of work is effective, beneficial to all, people-centred and accepted by the population at large. AI-WIPS is supported by the German Federal Ministry of Labour and Social Affairs (BMAS) and will complement the work of the German AI Observatory in the Ministry's Policy Lab Digital, Work & Society. For more information, visit https://oecd.ai/work-innovation-productivity-skills and https://denkfabrik-bmas.de/.
2AI may however be used in robotics (“smart robots”), which blurs the line between the two technologies (Raj and Seamans, 2019). For example, AI has improved the vision of robots, enabling them to identify and sort unorganised objects such as harvested fruit. AI can also be used to transfer knowledge between robots, such as the layout of hospital rooms between cleaning robots (Nolan, 2021).
3This can only be the case if an occupation is only partially automated, but depending on the price elasticity of demand for a given product or service, the productivity effect can be strong. For example, during the nineteenth century, 98% of the tasks required to weave fabric were automated, decreasing the price of fabric. Because of highly price elastic demand for fabric, the demand for fabric increased as did the number of weavers (Bessen, 2016).
4Education directly increases task-specific human capital as well as the rate of learning-by-doing on the job, at least some of which is task-specific (Gibbons and Waldman, 2004, 2006). This can be seen by looking at the likelihood of lateral moves within the same firm: lateral moves have a direct productivity cost to the firm as workers cannot utilise their entire task-specific human capital stock in another area (e.g., when moving from marketing to logistics). However, accumulating at least some task-specific human capital in a lateral position makes sense if a worker is scheduled to be promoted to a position that oversees both areas. If a worker's task-specific human capital is sufficiently high, however, the immediate productivity loss associated with a lateral move is higher than any expected productivity gain from the lateral move following a promotion. For example, in academic settings, Ph.D., economists are not typically moved to the HR department prior to becoming the dean of a department. Using a large employer-employee linked dataset on executives at US corporations, Jin and Waldman (2019) show that workers with 17 years of education were twice as likely to be laterally moved before promotion than workers with 19 years of education.
5An occupation is “exposed” to AI if it has a high intensity in skills that AI can perform, see section What Do These Indicators Measure? for details.
6Fossen and Sorgner (2019) use the occupational impact measure developed by Felten et al. (2018, 2019) and the Suitability for Machine Learning indicator developed by Brynjolfsson and Mitchell (2017) and Brynjolfsson et al. (2018) discussed in Section What Do These Indicators Measure?
7Acemoglu et al. (2020) use data from Brynjolfsson and Mitchell, 2017; Brynjolfsson et al., 2018, Felten et al. (2018, 2019), and (Webb, 2020) to identify tasks compatible with AI capabilities; and data from online job postings to identify firms that use AI, see Section Indicators of Occupational Exposure to AI for details.
8Sectors are available according to the North American Industry classification system (NAICS) for the US and Canada and according to the UK Standard Industrial Classification (SIC) and Singapore Industrial Classification (SSIC) for the UK and Singapore. Occupational codes are available according to the O*NET classification for Canada, SOC for the UK, and the US and SSOC for Singapore. These codes can be converted to ISCO at the one-digit level.
9This paper uses the same list of skills to look at AI job-postings, see Footnote 44 for the complete list of skills.
10To measure importance of skills in job ads, the authors use the Revealed Comparative Advantage (RCA) measure, loaned from trade economics, that weighs the importance of a skill in a job posting up if the number of skills for this specific posting is low, and weighs it down if the skill is ubiquitous in all job adds. That is, the skill “team work” will be generally less important given its ubiquity in all job ads, but its importance in an individual job posting would increase if only few other skills were required for that job.
11“Artificial Intelligence,” “Machine Learning,” “Data Science,” “Data Mining,” and “Big Data”.
12The indicator is calculated at the division level (19 industries) according to the Australian and New Zealand Standard Industrial Classification Level (ANZSIC).
13Abstract strategy games, real-time video games, image recognition, visual question answering, image generation, reading comprehension, language modelling, translation, and speech recognition. Abstract strategy games, for example are defined as “the ability to play abstract games involving sometimes complex strategy and reasoning ability, such as chess, go, or checkers, at a high level.” While the EFF tracks progress on 16 applications, AI has not made any progress on 7 of these over the relevant time period (Felten et al., 2021).
14The background of the gig workers is not known and so they may not necessarily be AI experts. This could be a potential weakness of this indicator. In contrast (Tolan et al., 2021) rely on expert assessments for the link between AI applications and worker abilities (Tolan et al., 2021).
15At the six digit SOC 2010 occupational level, this can be aggregated across sectors and geographical regions, see Felten et al. (2021).
16The abilities are chosen from Hernández-Orallo (2017) to be at an intermediate level of detail, excluding very general abilities that would influence all others, such as general intelligence, and too specific abilities and skills, such as being able to drive a car or music skills. They also exclude any personality traits that do not apply to machines. The abilities are: Memory processing, Sensorimotor interaction, Visual processing, Auditory processing, Attention and search, Planning, sequential decision-making and acting, Comprehension and expression, Communication, Emotion and self-control, Navigation, Conceptualisation, learning and abstraction, Quantitative and logical reasoning, Mind modelling and social interaction, and Metacognition and confidence assessment.
17Free and open repository of machine learning code and results, which includes data from several repositories (including EFF, NLPD progress etc.).
18An archive kept by the by the Association for the Advancement of Artificial Intelligence (AAI).
19AI-related technical skills are identified based on the list provided in Acemoglu et al. (2020), and detailed in Footnote 44.
20As with occupations, the industry-level scores are derived using the average frequency with which workers in each industry perform a set of 33 tasks, separately for each country.
21The United Kingdom and the United States are the only countries in the sample analysed (see Section Construction of the AI Occupational Exposure Measure) with 2012 Burning Glass Technologies data available, thereby allowing for the examination of trends over the past decade.
22The standard deviation of exposure to AI is 0.083 in the United-Kingdom and 0.075 in the United-States. These values are multiplied by the slopes of the linear relationships displayed in Figure 1: 3.90 and 4.95, respectively. The average share of job postings that require AI skills was 0.14% in the United-Kingdom and 0.26% in the United-States in 2012, and this has increased to 0.67 and 0.94%, respectively, in 2019.
23The 23 countries are Austria, Belgium, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Lithuania, Mexico, the Netherlands, Norway, Poland, Slovenia, the Slovak Republic, Spain, Sweden, United Kingdom, and the United States.
24This paper aims to explore the links between employment and AI deployment in the economy, rather than the direct employment increase due to AI development. Two occupations are particularly likely to be involved in AI development: IT technology professionals and IT technicians. These two occupations both have high levels of exposure to AI and some of the highest employment growth over this paper's observation period, which may be partly related to increased activity in AI development. These occupations may bias the analysis and they are therefore excluded from the sample. Nevertheless, the results are not sensitive to the inclusion of IT technology professionals and IT technicians in the analysis.
25A few occupation/country cells are missing due to data unavailability for the construction of the indicator of occupational exposure to AI: Skilled forestry, fishery, hunting workers in Belgium and Germany; Assemblers in Greece; Agricultural, forestry, fishery labourers in Austria and France, and Food preparation assistants in the United Kingdom.
26This paper uses BGT data for additional results for the countries for which they are available.
27While the three task-based indicators point to the same relationships between exposure to AI and employment, the results are less clearcut for the relationship between exposure to AI and average working hours.
28The 33 tasks were then grouped into 12 broad categories to address differences in data availability between types of task. For example, “read letters,” “read bills,” and “write letters” were grouped into one category (“literacy–business”), so that this type of task does not weight more in the final score than tasks types associated with a single PIAAC task (e.g., “dexterity” or “management”). For each ability and each occupation, 12 measures were constructed to reflect the frequency with which workers use the ability in the occupation to perform tasks under the 12 broad task categories. This was done by taking, within each category of tasks, the sum of the frequencies of the tasks assigned to the ability divided by the total number of tasks in the category. Finally, the frequency with which workers use the ability at the two-digit ISCO-08 level and by country was obtained by taking the sum of these 12 measures. The methodology, including the definition of the broad categories of tasks, is adapted from Fernández-Macías and Bisello (2020) and Tolan et al. (2021).
29The 17 lost abilities are: control prevision, multilimb coordination, response orientation, reaction time, speed of limb movement, explosive strength, extent flexibility, dynamic flexibility, gross body coordination, gross body equilibrium, far vision, night vision, peripheral vision, glare sensitivity, hearing sensitivity, auditory attention, and sound localization.
30Perceptual speed is the ability to quickly and accurately compare similarities and differences among sets of letters, numbers, objects, pictures, or patterns. Speed of closure is the ability to quickly make sense of, combine, and organize information into meaningful patterns. Flexibility of closure is the ability to identify or detect a known pattern (a figure, object, word, or sound) that is hidden in other distracting material.
31Only one psychomotor ability has an intermediate score: rate control, which is the ability to time one's movements or the movement of a piece of equipment in anticipation of changes in the speed and/or direction of a moving object or scene.
32To get results at the ISCO-08 2-digit level, scores were mapped from the SOC 2010 6-digits classification to the ISCO-08 4-digit classification, and aggregated at the 2-digit level by using average scores weighted by the number of full-time equivalent employees in each occupation in the United States, as provided by Webb (2020) and based on American Community Survey 2010 data.
33Averages are unweighted averages across occupations, so that cross-country differences only reflect differences in the ability requirements of occupations between countries, not differences in the occupational composition across countries.
34Although specific data on cleaning robots are not available, data from the International Federation of Robotics show that, in 2012, industrial robots were more prevalent in Finland than in Lithuania in all areas for which data are available.
35Again, as in the rest of the paper, exposure to AI specifically refers to potential automation of tasks, as this is primarily what task-based measures of exposure capture.
36On average across countries, there is no clear relationship between AI exposure and gender and age, see Figures A A.4, A A.5 in the Annex.
37Employment includes all people engaged in productive activities, whether as employees or self-employed. Employment data is taken from the Mexican National Survey of Occupation and Employment (ENOE), the European Union Labour Force Survey (EU-LFS), and the US Current Population Survey (US-CPS). The occupation classification was mapped to ISCO-08 where necessary. More specifically, the ENOE SINCO occupation code was directly mapped to the ISCO-08 classification. The US-CPS occupation census code variable was first mapped to the SOC 2010 classification. Next, it was mapped to the ISCO-08 classification.
38Hours worked refer to the average of individuals' usual weekly hours, which include the number of hours worked during a normal week without any extra-ordinary events (such as leave, public holidays, strikes, sickness, or extra-ordinary overtime).
392012 is available in PIAAC for most countries except Hungary (2017), Lithuania (2014), and Mexico (2017).
40Estimated at the average over the sample (37.7 average usual weekly hours).
41Mexico is excluded from the analysis of working time due to lack of data.
42See Box 1 for more details on Burning Glass Technologies data. The Burning Glass Occupation job classification (derived from SOC 2010) was directly mapped to the ISCO-08 classification.
43United Kingdom and the United States are the only countries in the sample with 2012 Burning Glass Technologies data available, thereby allowing for the examination of trends over the past decade.
44Job postings that require AI-related technical skills are defined as those that include at least one keyword from the following list: Machine Learning, Computer Vision, Machine Vision, Deep Learning, Virtual Agents, Image Recognition, Natural Language Processing, Speech Recognition, Pattern Recognition, Object Recognition, Neural Networks, AI ChatBot, Supervised Learning, Text Mining, Support Vector Machines, Unsupervised Learning, Image Processing, Mahout, Recommender Systems, Support Vector Machines (SVM), Random Forests, Latent Semantic Analysis, Sentiment Analysis/Opinion Mining, Latent Dirichlet Allocation, Predictive Models, Kernel Methods, Keras, Gradient boosting, OpenCV, Xgboost, Libsvm, Word2Vec, Chatbot, Machine Translation, and Sentiment Classification.
45The analysis is performed at the 2-digit level of the International Standard Classification of Occupations 2008 (ISCO-08).
46In a second step, Y ij will stand for the percentage change in average weekly working hours and the percentage change in the share of part-time workers.
47To select software patents, Webb uses an algorithm developed by Bessen and Hunt (2007) which requires one of the keywords “software,” “computer,” or “programme” to be present, but none of the keywords “chip,” “semiconductor,” “bus,” “circuity,” or “circuitry.” To select patents in the field of industrial robots, Webb develops an algorithm that results in the following search criteria: the title and abstract should include “robot” or “manipulate,” and the patent should not fall within the categories: “medical or veterinary science; hygiene” or “physical or chemical processes or apparatus in general”.
48They reverse the sign to measure offshorability instead of non-offshorability.
49Firpo et al. (2011) define “face-to-face contact” as the average value between the O*NET variables “face-to-face discussions,” “establishing and maintaining interpersonal relationships,” “assisting and caring for others,” “performing for or working directly with the public”, and “coaching and developing others.” They define “on-site job” as the average between the O*NET variables “inspecting equipment, structures, or material,” “handling and moving objects,” “operating vehicles, mechanized devices, or equipment,” and the mean of “repairing and maintaining mechanical equipment” and “repairing and maintaining electronic equipment”.
50All three indices are available by occupation based on U.S. Census occupation codes. They were first mapped to the SOC 2010 6-digits classification and then to the ISCO-08 4-digit classification. They were finally aggregated at the 2-digit level using average scores weighted by the number of full-time equivalent employees in each occupation in the United-States, as provided by Webb (2020) and based on American Community Survey 2010 data.
51The tradable sectors considered are agriculture, industry, and financial and insurance activities.
52Partial worker substitution in an occupation may increase worker productivity and employment in the same occupation, but also in other occupations and sectors (Autor and Salomons, 2018). These AI-induced productivity effects are relevant to the present cross-occupation analysis to the extent that they predominantly affect the same occupation where AI substitutes for workers. For example, although AI translation algorithms may substitute for part of the work of translators, they may increase the demand for translators by significantly reducing translation costs.
53Data are from 2012, with the exception of Hungary (2017), Lithuania (2014), and Mexico (2017).
54Low-skill occupations include the ISCO-08 1-digit occupation groups: Services and Sales Workers; and Elementary Occupations. Middle-skill occupations include the groups: Clerical Support Workers; Skilled Agricultural, Forestry, and Fishery Workers; Craft and Related Trades Workers; and Plant and Machine Operators and Assemblers. High-skill occupations include: Managers; Professionals, and Technicians; and Associate Professionals.
55In line with Nedelkoska and Quintini (2018), creative tasks include: problem solving—simple problems, and problem solving—complex problems; and social tasks include: teaching, advising, planning for others, communicating, negotiating, influencing, and selling. For each measure, occupation-country cells are then classified into three categories depending on the average frequency with which these tasks are performed (low, medium, and high). These three categories are calculated by applying terciles across the full sample of occupation-country cells. Data are from 2012, with the exception of Hungary (2017), Lithuania (2014), and Mexico (2017).
56These results are not displayed but are available on request.
57Tables 2, 3 correspond to unweighted regressions, but the results hold when each observation is weighted by the inverse of the number of country observations in the subsample considered, so that each country has the same weight. These results are not displayed but are available on request.
58The standard deviation of exposure to AI is 0.067 among high computer use occupations. Multiplying this by the coefficient in Column 4 gives 0.067*85.73 = 5.74.
59The Webb (2020) indicator is available by occupation based on U.S. Census occupation codes. It was first mapped to the SOC 2010 6-digits classification and then to the ISCO-08 4-digit classification. It was finally aggregated at the 2-digit level by using average scores weighted by the number of full-time equivalent employees in each occupation in the United States, as provided by Webb (2020) and based on American Community Survey 2010 data. The Tolan et al. (2021) indicator is available at the ISCO-08 3-digit level and was aggregated at the 2-digit level by taking average scores.
60Although statistically significant on aggregate, the relationships between employment growth and exposure to AI suggested by Table 2 are not visible for some countries.
61For productivity-enhancing technologies to have a positive effect on product and labour demand, product demand needs to be price elastic (Bessen, 2019).
62The standard deviation of exposure to AI is 0.125 among low computer use occupations. Multiplying this by the coefficient in Column 2 gives 0.125*(−4.823) = −0.60.
63Estimated at the average working hours among low computer use occupations (37.2 h).
64Tables 4, 5 correspond to unweighted regressions, but most of the results hold when each observation is weighted by the inverse of the number of country observations in the subsample considered, so that each country has the same weight. These results are not displayed but are available on request.
65Part-time workers are defined as workers usually working 30 hours or less per week in their main job.
66As an additional robustness exercise, Table A A.4 in the Appendix replicates the analysis using the score of exposure to AI obtained when using O*NET scores of “prevalence” and “importance” of abilities within occupations instead of PIAAC-based measures. The results remain qualitatively unchanged, but the coefficients on exposure to AI are no longer statistically significant on the subsample of occupations where computer use is low, when using working hours as the variable of interest. Tables A A.5, A.6 replicate the analysis using the alternative indicators of exposure to AI constructed by Webb (2020) and Tolan et al. (2021). When using the Webb (2020) indicator, the results hold on the entire sample but are not robust on the subsample of occupations where computer use is low. Using the Tolan et al. (2021) indicator, the results by subgroups hold qualitatively but the coefficients are not statistically significant.
67Involuntary part-time workers are defined as part-time workers (i.e., workers working 30 h or less per week) who report either that they could not find a full-time job or that they would like to work more hours.
68Although statistically significant on aggregate, the relationships between the percentage change in average usual weekly working hours and exposure to AI suggested by Table 4 are not visible for some countries.
69For example, personalised chatbots can partially substitute for travel attendants. Demand forecasting algorithms may facilitate the operation of hotels, including the work of housekeeping supervisors. Travel Attendants and Housekeeping Supervisors both fall into the Personal Service Workers category.
| 2022-05-10T00:00:00 |
2022/05/10
|
https://pmc.ncbi.nlm.nih.gov/articles/PMC9127971/
|
[
{
"date": "2022/12/01",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2022/12/01",
"position": 53,
"query": "AI labor market trends"
},
{
"date": "2023/01/01",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2023/01/01",
"position": 45,
"query": "AI labor market trends"
},
{
"date": "2023/01/01",
"position": 50,
"query": "artificial intelligence workers"
},
{
"date": "2023/02/01",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2023/02/01",
"position": 48,
"query": "artificial intelligence workers"
},
{
"date": "2023/04/01",
"position": 58,
"query": "AI labor market trends"
},
{
"date": "2023/04/01",
"position": 8,
"query": "artificial intelligence employment"
},
{
"date": "2023/04/01",
"position": 48,
"query": "artificial intelligence workers"
},
{
"date": "2023/05/01",
"position": 6,
"query": "artificial intelligence employment"
},
{
"date": "2023/06/01",
"position": 53,
"query": "AI labor market trends"
},
{
"date": "2023/06/01",
"position": 95,
"query": "artificial intelligence employers"
},
{
"date": "2023/09/01",
"position": 94,
"query": "artificial intelligence employers"
},
{
"date": "2023/09/01",
"position": 17,
"query": "artificial intelligence employment"
},
{
"date": "2023/09/01",
"position": 50,
"query": "artificial intelligence workers"
},
{
"date": "2023/10/01",
"position": 55,
"query": "AI labor market trends"
},
{
"date": "2023/10/01",
"position": 14,
"query": "artificial intelligence employment"
},
{
"date": "2023/11/01",
"position": 56,
"query": "AI labor market trends"
},
{
"date": "2023/11/01",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2024/02/01",
"position": 28,
"query": "AI employment"
},
{
"date": "2024/02/01",
"position": 51,
"query": "AI labor market trends"
},
{
"date": "2024/02/01",
"position": 19,
"query": "artificial intelligence employment"
},
{
"date": "2024/03/01",
"position": 52,
"query": "AI labor market trends"
},
{
"date": "2024/03/01",
"position": 96,
"query": "artificial intelligence employers"
},
{
"date": "2024/03/01",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2024/05/01",
"position": 55,
"query": "AI labor market trends"
},
{
"date": "2024/06/01",
"position": 53,
"query": "AI labor market trends"
},
{
"date": "2024/06/01",
"position": 17,
"query": "artificial intelligence employment"
},
{
"date": "2024/07/01",
"position": 50,
"query": "artificial intelligence workers"
},
{
"date": "2024/08/01",
"position": 54,
"query": "AI labor market trends"
},
{
"date": "2024/09/01",
"position": 49,
"query": "AI labor market trends"
},
{
"date": "2024/09/01",
"position": 7,
"query": "artificial intelligence employment"
},
{
"date": "2024/10/01",
"position": 9,
"query": "artificial intelligence employment"
},
{
"date": "2024/11/01",
"position": 53,
"query": "AI labor market trends"
},
{
"date": "2024/11/01",
"position": 17,
"query": "artificial intelligence employment"
},
{
"date": "2024/12/01",
"position": 84,
"query": "artificial intelligence employers"
},
{
"date": "2025/03/01",
"position": 85,
"query": "artificial intelligence employers"
},
{
"date": "2025/03/01",
"position": 23,
"query": "artificial intelligence employment"
},
{
"date": "2025/04/01",
"position": 83,
"query": "artificial intelligence employers"
},
{
"date": "2025/05/01",
"position": 45,
"query": "AI labor market trends"
},
{
"date": "2025/05/01",
"position": 10,
"query": "artificial intelligence employment"
},
{
"date": "2025/05/01",
"position": 51,
"query": "artificial intelligence workers"
},
{
"date": "2025/06/01",
"position": 53,
"query": "artificial intelligence workers"
}
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.