Towards Reducing Biases in Combining Multiple Experts Online

TL;DR

This paper proposes an online decision-making algorithm that balances fairness, specifically equalized odds, with regret minimization, and demonstrates its effectiveness on real-world datasets.

Contribution

It introduces a novel online algorithm that achieves approximate equalized odds fairness without significant regret loss in a multi-expert setting.

Findings

Achieves fairness with minimal regret trade-off.

Performs well on standard fairness datasets.

Provides theoretical guarantees for fairness and regret.

Abstract

In many real life situations, including job and loan applications, gatekeepers must make justified and fair real-time decisions about a person's fitness for a particular opportunity. In this paper, we aim to accomplish approximate group fairness in an online stochastic decision-making process, where the fairness metric we consider is equalized odds. Our work follows from the classical learning-from-experts scheme, assuming a finite set of classifiers (human experts, rules, options, etc) that cannot be modified. We run separate instances of the algorithm for each label class as well as sensitive groups, where the probability of choosing each instance is optimized for both fairness and regret. Our theoretical results show that approximately equalized odds can be achieved without sacrificing much regret. We also demonstrate the performance of the algorithm on real data sets commonly used by the fairness community.