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  1.  17
    ACROCPoLis: A Descriptive Framework for Making Sense of Fairness.Andrea Aler Tubella, Dimitri Coelho Mollo, Adam Dahlgren, Hannah Devinney, Virginia Dignum, Petter Ericson, Anna Jonsson, Tim Kampik, Tom Lenaerts, Julian Mendez & Juan Carlos Nieves Sanchez - 2023 - Proceedings of the 2023 Acm Conference on Fairness, Accountability, and Transparency:1014-1025.
    Fairness is central to the ethical and responsible development and use of AI systems, with a large number of frameworks and formal notions of algorithmic fairness being available. However, many of the fairness solutions proposed revolve around technical considerations and not the needs of and consequences for the most impacted communities. We therefore want to take the focus away from definitions and allow for the inclusion of societal and relational aspects to represent how the effects of AI systems impact and (...)
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  2.  15
    Dealing with expert bias in collective decision-making.Axel Abels, Tom Lenaerts, Vito Trianni & Ann Nowé - 2023 - Artificial Intelligence 320 (C):103921.
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  3.  67
    An evolutionary game theoretic perspective on learning in multi-agent systems.Karl Tuyls, Ann Nowe, Tom Lenaerts & Bernard Manderick - 2004 - Synthese 139 (2):297 - 330.
    In this paper we revise Reinforcement Learning and adaptiveness in Multi-Agent Systems from an Evolutionary Game Theoretic perspective. More precisely we show there is a triangular relation between the fields of Multi-Agent Systems, Reinforcement Learning and Evolutionary Game Theory. We illustrate how these new insights can contribute to a better understanding of learning in MAS and to new improved learning algorithms. All three fields are introduced in a self-contained manner. Each relation is discussed in detail with the necessary background information (...)
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