Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective
  Learning   Human society had a long history of suffering from cognitive biases leading
to social prejudices and mass injustice. The prevalent existence of cognitive
biases in large volumes of historical data can pose a threat of being
manifested as unethical and seemingly inhuman predictions as outputs of AI
systems trained on such data. To alleviate this problem, we propose a
bias-aware multi-objective learning framework that given a set of identity
attributes (e.g. gender, ethnicity etc.) and a subset of sensitive categories
of the possible classes of prediction outputs, learns to reduce the frequency
of predicting certain combinations of them, e.g. predicting stereotypes such as
`most blacks use abusive language', or `fear is a virtue of women'. Our
experiments conducted on an emotion prediction task with balanced class priors
shows that a set of baseline bias-agnostic models exhibit cognitive biases with
respect to gender, such as women are prone to be afraid whereas men are more
prone to be angry. In contrast, our proposed bias-aware multi-objective
learning methodology is shown to reduce such biases in the predictied emotions.

12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg