Group Fairness in Bandit Arm Selection

TL;DR

This paper introduces a new approach to ensure group fairness in contextual multi-armed bandit algorithms, addressing biased feedback and societal biases to promote equitable decision-making across sensitive groups.

Contribution

It proposes a novel algorithm that accounts for multiple groups and learns societal bias, with theoretical regret bounds and validation on synthetic and real-world data.

Findings

The algorithm effectively mitigates bias in arm selection.

Theoretical regret bounds are established for the proposed method.

Empirical results demonstrate improved fairness across groups.

Abstract

We propose a novel formulation of group fairness with biased feedback in the contextual multi-armed bandit (CMAB) setting. In the CMAB setting, a sequential decision maker must, at each time step, choose an arm to pull from a finite set of arms after observing some context for each of the potential arm pulls. In our model, arms are partitioned into two or more sensitive groups based on some protected feature(s) (e.g., age, race, or socio-economic status). Initial rewards received from pulling an arm may be distorted due to some unknown societal or measurement bias. We assume that in reality these groups are equal despite the biased feedback received by the agent. To alleviate this, we learn a societal bias term which can be used to both find the source of bias and to potentially fix the problem outside of the algorithm. We provide a novel algorithm that can accommodate this notion of fairness for an arbitrary number of groups, and provide a theoretical bound on the regret for our algorithm. We validate our algorithm using synthetic data and two real-world datasets for intervention settings wherein we want to allocate resources fairly across groups.