Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: What About Applied Fairness?   Machine learning practitioners are often ambivalent about the ethical aspects
of their products. We believe anything that gets us from that current state to
one in which our systems are achieving some degree of fairness is an
improvement that should be welcomed. This is true even when that progress does
not get us 100% of the way to the goal of "complete" fairness or perfectly
align with our personal belief on which measure of fairness is used. Some
measure of fairness being built would still put us in a better position than
the status quo. Impediments to getting fairness and ethical concerns applied in
real applications, whether they are abstruse philosophical debates or technical
overhead such as the introduction of ever more hyper-parameters, should be
avoided. In this paper we further elaborate on our argument for this viewpoint
and its importance.

17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an