Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcare
This addresses fairness in heterogeneous multi-agent systems for healthcare, where aligning effort with expertise is critical to prevent burnout and inefficiencies, representing an incremental advance with new tools.
The paper tackled the problem of fairness in multi-agent reinforcement learning for healthcare collaboration by proposing a framework that combines workload balance and skill-task alignment, and introduced a customizable simulation environment, showing that workload-only fairness can lead to task-skill mismatches.
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.