Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fairness risk measures   Ensuring that classifiers are non-discriminatory or fair with respect to a
sensitive feature (e.g., race or gender) is a topical problem. Progress in this
task requires fixing a definition of fairness, and there have been several
proposals in this regard over the past few years. Several of these, however,
assume either binary sensitive features (thus precluding categorical or
real-valued sensitive groups), or result in non-convex objectives (thus
adversely affecting the optimisation landscape). In this paper, we propose a
new definition of fairness that generalises some existing proposals, while
allowing for generic sensitive features and resulting in a convex objective.
The key idea is to enforce that the expected losses (or risks) across each
subgroup induced by the sensitive feature are commensurate. We show how this
relates to the rich literature on risk measures from mathematical finance. As a
special case, this leads to a new convex fairness-aware objective based on
minimising the conditional value at risk (CVaR).

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The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin