Confidence Intervals for Testing Disparate Impact in Fair Learning
Machine Learning
2018-07-18 v1 Machine Learning
Statistics Theory
Statistics Theory
Abstract
We provide the asymptotic distribution of the major indexes used in the statistical literature to quantify disparate treatment in machine learning. We aim at promoting the use of confidence intervals when testing the so-called group disparate impact. We illustrate on some examples the importance of using confidence intervals and not a single value.
Cite
@article{arxiv.1807.06362,
title = {Confidence Intervals for Testing Disparate Impact in Fair Learning},
author = {Philippe Besse and Eustasio del Barrio and Paula Gordaliza and Jean-Michel Loubes},
journal= {arXiv preprint arXiv:1807.06362},
year = {2018}
}