Envy-Free Classification
Machine Learning
2020-09-25 v2 Computer Science and Game Theory
Machine Learning
Abstract
In classic fair division problems such as cake cutting and rent division, envy-freeness requires that each individual (weakly) prefer his allocation to anyone else's. On a conceptual level, we argue that envy-freeness also provides a compelling notion of fairness for classification tasks. Our technical focus is the generalizability of envy-free classification, i.e., understanding whether a classifier that is envy free on a sample would be almost envy free with respect to the underlying distribution with high probability. Our main result establishes that a small sample is sufficient to achieve such guarantees, when the classifier in question is a mixture of deterministic classifiers that belong to a family of low Natarajan dimension.
Keywords
Cite
@article{arxiv.1809.08700,
title = {Envy-Free Classification},
author = {Maria-Florina Balcan and Travis Dick and Ritesh Noothigattu and Ariel D. Procaccia},
journal= {arXiv preprint arXiv:1809.08700},
year = {2020}
}