Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: General Fair Empirical Risk Minimization   We tackle the problem of algorithmic fairness, where the goal is to avoid the
unfairly influence of sensitive information, in the general context of
regression with possible continuous sensitive attributes. We extend the
framework of fair empirical risk minimization to this general scenario,
covering in this way the whole standard supervised learning setting. Our
generalized fairness measure reduces to well known notions of fairness
available in literature. We derive learning guarantees for our method, that
imply in particular its statistical consistency, both in terms of the risk and
the fairness measure. We then specialize our approach to kernel methods and
propose a convex fair estimator in that setting. We test the estimator on a
commonly used benchmark dataset (Communities and Crime) and on a new dataset
collected at the University of Genova, containing the information of the
academic career of five thousand students. The latter dataset provides a
challenging real case scenario of unfair behaviour of standard regression
methods that benefits from our methodology. The experimental results show that
our estimator is effective at mitigating the trade-off between accuracy and
fairness requirements.

31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn