Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Addressing Fairness in Classification with a Model-Agnostic
  Multi-Objective Algorithm   The goal of fairness in classification is to learn a classifier that does not
discriminate against groups of individuals based on sensitive attributes, such
as race and gender. One approach to designing fair algorithms is to use
relaxations of fairness notions as regularization terms or in a constrained
optimization problem. We observe that the hyperbolic tangent function can
approximate the indicator function. We leverage this property to define a
differentiable relaxation that approximates fairness notions provably better
than existing relaxations. In addition, we propose a model-agnostic
multi-objective architecture that can simultaneously optimize for multiple
fairness notions and multiple sensitive attributes and supports all statistical
parity-based notions of fairness. We use our relaxation with the
multi-objective architecture to learn fair classifiers. Experiments on public
datasets show that our method suffers a significantly lower loss of accuracy
than current debiasing algorithms relative to the unconstrained model.

10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin