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Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcare
Created by
Haebom
Author
Promise Osaine Ekpo, Brian La, Thomas Wiener, Saesha Agarwal, Arshia Agrawal, Gonzalo Gonzalez-Pumariega, Lekan P. Molu, Angelique Taylor
Outline
This paper addresses fairness in Multi-Agent Reinforcement Learning (MARL). Unlike previous studies that primarily focus on workload balancing, this paper emphasizes the importance of agent expertise and structured collaboration, using the healthcare field as an example. While workload balancing implies assigning equal workloads to all agents regardless of expertise, this paper proposes a framework called "FairSkillMARL" to define a fairness concept that simultaneously considers workload balance and skill-task fit. Furthermore, we develop a simulator called "MARLHospital" that simulates a healthcare environment and analyze the impact of various team compositions and energy constraints on fairness. Experimental results demonstrate that fairness based solely on workload can lead to skill-task mismatch, highlighting the need for more robust metrics to capture skill-task mismatch.
Takeaways, Limitations
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Takeaways:
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In multi-agent systems where agent expertise is important, such as in the medical field, we present a concept of fairness that considers not only workload balance but also technology-task consistency.
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Development of a new simulator MARLHospital that can account for various team compositions and energy constraints.
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Experimentally demonstrating that simple workload balancing can lead to skill-task mismatch.
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The need for a more robust fairness assessment metric that takes into account the expertise of various agents is raised.
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Limitations:
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Further research is needed to determine the generalizability of the MARLHospital simulator.
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Validation of the proposed FairSkillMARL framework's applicability and scalability in real-world healthcare environments is needed.
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Need for application and evaluation in more diverse and complex medical scenarios.
