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Title: Fairness in Multi-Agent Systems
Abstract: In this presentation we delve into the field of algorithmic fairness in Multi-Agent Systems (MAS), focusing on the fairness of agents' decision-making processes. We first provide a definition of fairness and we present the reasons why it is relevant for AI-based decisions. Various fairness metrics, e.g., demographic parity, conditional statistical parity and fairness through awareness are discussed. We show how to apply these metrics in multi-agent systems, providing an explanation of the key adaptations. We complete the presentation with an application of the metrics to the Harvest Tree Game, an original configuration of multi-agent systems that are already well-known in the literature.
Biography: Gabriele La Malfa is a PhD student at the Centre for Doctoral Training at the Department of Informatics at King's College London. He works on fairness in multi-agent systems and he is part of the Safe and Trusted AI.