Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Empirical Comparison of Bias Reduction Methods on Real-World Problems
  in High-Stakes Policy Settings   Applications of machine learning (ML) to high-stakes policy settings -- such
as education, criminal justice, healthcare, and social service delivery -- have
grown rapidly in recent years, sparking important conversations about how to
ensure fair outcomes from these systems. The machine learning research
community has responded to this challenge with a wide array of proposed
fairness-enhancing strategies for ML models, but despite the large number of
methods that have been developed, little empirical work exists evaluating these
methods in real-world settings. Here, we seek to fill this research gap by
investigating the performance of several methods that operate at different
points in the ML pipeline across four real-world public policy and social good
problems. Across these problems, we find a wide degree of variability and
inconsistency in the ability of many of these methods to improve model
fairness, but post-processing by choosing group-specific score thresholds
consistently removes disparities, with important implications for both the ML
research community and practitioners deploying machine learning to inform
consequential policy decisions.

20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg