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Promotes ethical AI by ensuring models are fair, interpretable, and responsible. Addresses biases and builds trust in AI systems.
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 OxEnsemble: Fair Ensembles for Low-Data Classification Jonathan Rystrøm Zihao Fu Chris Russell | |||
 Metric-Fair Prompting: Treating Similar Samples Similarly Jing Wang Jie Shen Xing Niu Tong Zhang Jeremy Weiss | |||
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 Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?Frontiers in Big Data (FBD), 2025 | |||
 Applications and Challenges of Fairness APIs in Machine Learning SoftwareACM Transactions on Software Engineering and Methodology (TOSEM), 2025 | |||
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