Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Debiasing Personal Identities in Toxicity Classification   As Machine Learning models continue to be relied upon for making automated
decisions, the issue of model bias becomes more and more prevalent. In this
paper, we approach training a text classifica-tion model and optimize on bias
minimization by measuring not only the models performance on our dataset as a
whole, but also how it performs across different subgroups. This requires
measuring per-formance independently for different demographic subgroups and
measuring bias by comparing them to results from the rest of our data. We show
how unintended bias can be detected using these metrics and how removing bias
from a dataset completely can result in worse results.

31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph