Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcare

TL;DR

This paper introduces a new framework and environment for ensuring fairness in healthcare multi-agent systems by balancing workload and aligning skills, addressing limitations of existing fairness metrics.

Contribution

It proposes FairSkillMARL, a framework combining workload balance and skill alignment, and MARLHospital, a healthcare-inspired environment for studying fairness in multi-agent collaboration.

Findings

Fairness based solely on workload can cause skill mismatches.

Existing fairness metrics may not adequately capture skill-task alignment.

The proposed methods improve understanding of fairness in heterogeneous multi-agent systems.

Abstract

Fairness in multi-agent reinforcement learning (MARL) is often framed as a workload balance problem, overlooking agent expertise and the structured coordination required in real-world domains. In healthcare, equitable task allocation requires workload balance or expertise alignment to prevent burnout and overuse of highly skilled agents. Workload balance refers to distributing an approximately equal number of subtasks or equalised effort across healthcare workers, regardless of their expertise. We make two contributions to address this problem. First, we propose FairSkillMARL, a framework that defines fairness as the dual objective of workload balance and skill-task alignment. Second, we introduce MARLHospital, a customizable healthcare-inspired environment for modeling team compositions and energy-constrained scheduling impacts on fairness, as no existing simulators are well-suited for this problem. We conducted experiments to compare FairSkillMARL in conjunction with four standard MARL methods, and against two state-of-the-art fairness metrics. Our results suggest that fairness based solely on equal workload might lead to task-skill mismatches and highlight the need for more robust metrics that capture skill-task misalignment. Our work provides tools and a foundation for studying fairness in heterogeneous multi-agent systems where aligning effort with expertise is critical.