# Group Fairness in Bandit Arm Selection

arXiv: https://arxiv.org/abs/1912.03802 · HTML: https://mlsanity.com/paper/1912.03802/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 13 of 13)

- [Fairness in Ranking under Uncertainty](https://mlsanity.com/paper/2107.06720/) (2021)
- [Survey on Fair Reinforcement Learning: Theory and Practice](https://mlsanity.com/paper/2205.10032/) (2022)
- [Socially Fair Reinforcement Learning](https://mlsanity.com/paper/2208.12584/) (2022)
- [Uncertainty Quantification for Fairness in Two-Stage Recommender Systems](https://mlsanity.com/paper/2205.15436/) (2022)
- [Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning](https://mlsanity.com/paper/2306.00324/) (2023)
- [Ranked Prioritization of Groups in Combinatorial Bandit Allocation](https://mlsanity.com/paper/2205.05659/) (2022)
- [Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments](https://mlsanity.com/paper/2311.04528/) (2023)
- [Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic Bandits](https://mlsanity.com/paper/2402.05575/) (2024)
- [Merit-based Fair Combinatorial Semi-Bandit with Unrestricted Feedback Delays](https://mlsanity.com/paper/2407.15439/) (2024)
- [How Technology Impacts and Compares to Humans in Socially Consequential Arenas](https://mlsanity.com/paper/2211.03554/) (2022)
- [On Mitigating Affinity Bias through Bandits with Evolving Biased Feedback](https://mlsanity.com/paper/2503.05662/) (2025)
- [Adaptive Bounded Exploration and Intermediate Actions for Data Debiasing](https://mlsanity.com/paper/2504.08151/) (2025)
- [Bayesian Learning to Select Fair Classifiers](https://mlsanity.com/paper/2610.20514/) (2026)

## Abstract (arXiv)

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.