Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fairness Beyond Disparate Treatment & Disparate Impact: Learning
  Classification without Disparate Mistreatment   Automated data-driven decision making systems are increasingly being used to
assist, or even replace humans in many settings. These systems function by
learning from historical decisions, often taken by humans. In order to maximize
the utility of these systems (or, classifiers), their training involves
minimizing the errors (or, misclassifications) over the given historical data.
However, it is quite possible that the optimally trained classifier makes
decisions for people belonging to different social groups with different
misclassification rates (e.g., misclassification rates for females are higher
than for males), thereby placing these groups at an unfair disadvantage. To
account for and avoid such unfairness, in this paper, we introduce a new notion
of unfairness, disparate mistreatment, which is defined in terms of
misclassification rates. We then propose intuitive measures of disparate
mistreatment for decision boundary-based classifiers, which can be easily
incorporated into their formulation as convex-concave constraints. Experiments
on synthetic as well as real world datasets show that our methodology is
effective at avoiding disparate mistreatment, often at a small cost in terms of
accuracy.

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The categories of this paper are: bayes-an
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
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc