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Preprint
A Convex Framework for Fair Regression
arXiv.org
07 Jun 2017
Abstract
We introduce a flexible family of fairness regularizers for (linear and
logistic) regression problems. These regularizers all enjoy convexity,
permitting fast optimization, and they span the rang from notions of group
fairness to strong individual fairness. By varying the weight on the fairness
regularizer, we can compute the efficient frontier of the accuracy-fairness
trade-off on any given dataset, and we measure the severity of this trade-off
via a numerical quantity we call the Price of Fairness (PoF). The centerpiece
of our results is an extensive comparative study of the PoF across six
different datasets in which fairness is a primary consideration.
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Details
- Title
- A Convex Framework for Fair Regression
- Creators
- Richard BerkHoda HeidariShahin JabbariMatthew JosephMichael KearnsJamie MorgensternSeth NeelAaron Roth
- Publication Details
- arXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Other Identifier
- 991021868724604721