# Fairness for Robust Log Loss Classification

## TL;DR

This paper introduces a new fairness-aware classification method based on distributional robustness, formulated as a minimax game, which offers theoretical and practical improvements over existing fairness constraints.

## Contribution

The authors derive a novel classifier from first principles of distributional robustness that integrates fairness into a worst-case logarithmic loss minimization framework.

## Key findings

- Convexity and asymptotic convergence of the proposed method.
- Improved fairness and accuracy on benchmark datasets.
- Produces a parametric exponential family distribution resembling truncated logistic regression.

## Abstract

Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming constrained optimizations. We instead re-derive a new classifier from the first principles of distributional robustness that incorporates fairness criteria into a worst-case logarithmic loss minimization. This construction takes the form of a minimax game and produces a parametric exponential family conditional distribution that resembles truncated logistic regression. We present the theoretical benefits of our approach in terms of its convexity and asymptotic convergence. We then demonstrate the practical advantages of our approach on three benchmark fairness datasets.