Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Removing biased data to improve fairness and accuracy   Machine learning systems are often trained using data collected from
historical decisions. If past decisions were biased, then automated systems
that learn from historical data will also be biased. We propose a black-box
approach to identify and remove biased training data. Machine learning models
trained on such debiased data (a subset of the original training data) have low
individual discrimination, often 0%. These models also have greater accuracy
and lower statistical disparity than models trained on the full historical
data. We evaluated our methodology in experiments using 6 real-world datasets.
Our approach outperformed seven previous approaches in terms of individual
discrimination and accuracy.

26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con