Insufficient 发表于 2025-3-25 05:02:16
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 1860-9503Strength 发表于 2025-3-25 08:23:27
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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 issuesHerbivorous 发表于 2025-3-26 03:16:12
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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等级的上升 发表于 2025-3-26 11:15:01
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 offlineNAVEN 发表于 2025-3-26 13:07:09
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 witomnibus 发表于 2025-3-26 20:11:55
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