Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Fair Predictions Using Variational Inference In Causal Models   The importance of algorithmic fairness grows with the increasing impact
machine learning has on people's lives. Recent work on fairness metrics shows
the need for causal reasoning in fairness constraints. In this work, a
practical method named FairTrade is proposed for creating flexible prediction
models which integrate fairness constraints on sensitive causal paths. The
method uses recent advances in variational inference in order to account for
unobserved confounders. Further, a method outline is proposed which uses the
causal mechanism estimates to audit black box models. Experiments are conducted
on simulated data and on a real dataset in the context of detecting unlawful
social welfare. This research aims to contribute to machine learning techniques
which honour our ethical and legal boundaries.

2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg