Titlebook: Ethics in Artificial Intelligence: Bias, Fairness and Beyond; Animesh Mukherjee,Juhi Kulshrestha,Srijan Kumar Book 2023 The Institution of

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1860-949X oftwaretesting/debugging and in multi stakeholder platforms. This segment ends with a chapter on interpretability of machine learning models which is another very important and emerging topic in this area..978-981-99-7186-2978-981-99-7184-8Series ISSN 1860-949X Series E-ISSN 1860-9503

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Multi-Object Tracking Analysis,are additional constraints on the feasible allocations, which may occur due to connectivity requirements, or incompatibility between certain item pairs. A large body of recent work has focused on defining appropriate fairness notions, providing existence guarantees, and handling computational issues

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,Testing, Debugging, and?Repairing Individual Discrimination in?Machine Learning Models,f algorithmic bias is a crucial attribute for an AI system to be considered trustworthy. In this book chapter, we address various problems related to the detection and mitigation of algorithmic bias in machine learning models, specifically individual discrimination. A model shows individual discrimi

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Temporal Fairness in Online Decision-Making,ple, one might wish to screen resumes in real-time as they are submitted; in this setting, the feedback used to make the screening decisions (such as ultimate hiring decisions on past resumes) might be provided over time as well. While fairness concerns have received significant attention in offline

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No AI After Auschwitz? Bridging AI and Memory Ethics in the Context of Information Retrieval of Genage. By facilitating organisation and retrieval of large volumes of heritage-related content, AI-driven IR systems inform users about a broad range of historical phenomena, including genocides (e.g. the Holocaust). However, it is currently unclear to what degree IR systems are capable of dealing wit

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