Metric-Free Individual Fairness with Cooperative Contextual Bandits

TL;DR

This paper introduces a novel metric-free approach to individual fairness using cooperative contextual bandits, aiming to reduce bias in decision-making algorithms without relying on predefined similarity metrics.

Contribution

The paper proposes a new cooperative contextual bandits algorithm that treats fairness as a reward, enabling metric-free individual fairness in bias mitigation.

Findings

Effective bias reduction demonstrated on real-world datasets

Achieves both individual and group fairness

Does not require differentiable fairness metrics

Abstract

Data mining algorithms are increasingly used in automated decision making across all walks of daily life. Unfortunately, as reported in several studies these algorithms inject bias from data and environment leading to inequitable and unfair solutions. To mitigate bias in machine learning, different formalizations of fairness have been proposed that can be categorized into group fairness and individual fairness. Group fairness requires that different groups should be treated similarly which might be unfair to some individuals within a group. On the other hand, individual fairness requires that similar individuals be treated similarly. However, individual fairness remains understudied due to its reliance on problem-specific similarity metrics. We propose a metric-free individual fairness and a cooperative contextual bandits (CCB) algorithm. The CCB algorithm utilizes fairness as a reward and attempts to maximize it. The advantage of treating fairness as a reward is that the fairness criterion does not need to be differentiable. The proposed algorithm is tested on multiple real-world benchmark datasets. The results show the effectiveness of the proposed algorithm at mitigating bias and at achieving both individual and group fairness.