Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the impossibility of non-trivial accuracy under fairness constraints   One of the main concerns about fairness in machine learning (ML) is that, in
order to achieve it, one may have to trade off some accuracy. To overcome this
issue, Hardt et al. proposed the notion of equality of opportunity (EO), which
is compatible with maximal accuracy when the target label is deterministic with
respect to the input features.
  In the probabilistic case, however, the issue is more complicated: It has
been shown that under differential privacy constraints, there are data sources
for which EO can only be achieved at the total detriment of accuracy, in the
sense that a classifier that satisfies EO cannot be more accurate than a
trivial (i.e., constant) classifier. In our paper we strengthen this result by
removing the privacy constraint. Namely, we show that for certain data sources,
the most accurate classifier that satisfies EO is a trivial classifier.
Furthermore, we study the trade-off between accuracy and EO loss (opportunity
difference), and provide a sufficient condition on the data source under which
EO and non-trivial accuracy are compatible.

27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph