Impact of Data Processing on Fairness in Supervised Learning
- URL: http://arxiv.org/abs/2102.01867v1
- Date: Wed, 3 Feb 2021 04:11:39 GMT
- Title: Impact of Data Processing on Fairness in Supervised Learning
- Authors: Sajad Khodadadian, AmirEmad Ghassami, Negar Kiyavash
- Abstract summary: We study the impact of pre and post processing for reducing discrimination in data-driven decision makers.
We show that under some mild conditions, pre-processing outperforms post-processing.
- Score: 30.419675300470573
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study the impact of pre and post processing for reducing discrimination in
data-driven decision makers. We first analyze the fundamental trade-off between
fairness and accuracy in a pre-processing approach, and propose a design for a
pre-processing module based on a convex optimization program, which can be
added before the original classifier. This leads to a fundamental lower bound
on attainable discrimination, given any acceptable distortion in the outcome.
Furthermore, we reformulate an existing post-processing method in terms of our
accuracy and fairness measures, which allows comparing post-processing and
pre-processing approaches. We show that under some mild conditions,
pre-processing outperforms post-processing. Finally, we show that by
appropriate choice of the discrimination measure, the optimization problem for
both pre and post processing approaches will reduce to a linear program and
hence can be solved efficiently.
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