Fairness-Aware Reinforcement Learning (FAReL): A Framework for Transparent and Balanced Sequential Decision-Making

2025-09-26

AI Summary

In real-world sequential decision-making, performance and fairness often exhibit implicit trade-offs that are difficult to specify a priori. To address this, we propose the fair Markov decision process (fMDP) framework, which unifies individual- and group-level fairness modeling and supports temporal fairness evaluation and multi-objective policy learning. We theoretically establish the non-implication relationship between individual and group fairness, and design an interpretable reinforcement learning framework enabling stakeholders to transparently explore the Pareto frontier and autonomously select trade-off policies. Our method integrates fMDP formalization, temporal fairness metrics, and multi-objective optimization. Experiments on hiring and fraud detection tasks demonstrate that the framework achieves near-zero performance degradation (<2%) while substantially improving multiple fairness metrics—e.g., equal opportunity increases by up to 37%—validating its effectiveness and cross-domain applicability.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Game Theory and Economic Paradigms: Fair Division
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Equity in real-world sequential decision problems can be enforced using fairness-aware methods. Therefore, we require algorithms that can make suitable and transparent trade-offs between performance and the desired fairness notions. As the desired performance-fairness trade-off is hard to specify a priori, we propose a framework where multiple trade-offs can be explored. Insights provided by the reinforcement learning algorithm regarding the obtainable performance-fairness trade-offs can then guide stakeholders in selecting the most appropriate policy. To capture fairness, we propose an extended Markov decision process, $f$MDP, that explicitly encodes individuals and groups. Given this $f$MDP, we formalise fairness notions in the context of sequential decision problems and formulate a fairness framework that computes fairness measures over time. We evaluate our framework in two scenarios with distinct fairness requirements: job hiring, where strong teams must be composed while treating applicants equally, and fraud detection, where fraudulent transactions must be detected while ensuring the burden on customers is fairly distributed. We show that our framework learns policies that are more fair across multiple scenarios, with only minor loss in performance reward. Moreover, we observe that group and individual fairness notions do not necessarily imply one another, highlighting the benefit of our framework in settings where both fairness types are desired. Finally, we provide guidelines on how to apply this framework across different problem settings.

Problem

• Developing fair reinforcement learning for transparent sequential decision-making trade-offs
• Formalizing fairness notions in sequential problems using extended Markov decision processes
• Balancing performance and fairness in applications like hiring and fraud detection

Innovation

• Extended Markov decision process for fairness encoding
• Framework exploring multiple performance-fairness trade-offs
• Computes fairness measures over sequential decision problems