Avoiding Resentment Via Monotonic Fairness

TL;DR

This paper proposes using monotonically constrained models to achieve demographically balanced and individually fair classifiers, avoiding resentment caused by traditional fairness approaches that can produce counter-intuitive or controversial outcomes.

Contribution

It introduces a novel fairness framework that ensures individual fairness and demographic balance simultaneously through monotonic constraints.

Findings

Monotonically constrained models prevent individual resentment.

The approach achieves demographic balance without sacrificing individual fairness.

Experimental results demonstrate improved fairness metrics.

Abstract

Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of individual fairness, i.e. individuals with similar qualifications will receive different outcomes. Individually and group fair decision criteria can produce counter-intuitive results, e.g. that the optimal constrained boundary may reject intuitively better candidates due to demographic imbalance in similar candidates. Both approaches can be seen as introducing individual resentment, where some individuals would have received a better outcome if they either belonged to a different demographic class and had the same qualifications, or if they remained in the same class but had objectively worse qualifications (e.g. lower test scores). We show that both forms of resentment can be avoided by using monotonically constrained machine learning models to create individually fair, demographically balanced classifiers.