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A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets.

, и . Pattern Recognit. Lett., (2022)

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Gaëlle Lisack

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A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets., и . Pattern Recognit. Lett., (2022)Compatibility of Fairness Metrics with EU Non-Discrimination Laws: Demographic Parity & Conditional Demographic Disparity., , , , , и . CoRR, (2023)Métriques d'équité en Apprentissage Automatique et droit de l'Union Europénne en matière de non-discrimination., , , , , и . CNIA, PFIA, (2023)A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets., и . CoRR, (2021)Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps., , , , и . CoRR, (2023)Modeling Implicit Bias with Fuzzy Cognitive Maps., , , , и . CoRR, (2021)Modeling implicit bias with fuzzy cognitive maps., , , , и . Neurocomputing, (2022)Bias Quantification for Protected Features in Pattern Classification Problems., и . CIARP, том 12702 из Lecture Notes in Computer Science, стр. 351-360. Springer, (2021)Compatibility of Fairness Metrics With EU Non-Discrimination Law: A Legal and Technical Case Study., , , , , и . EWAF, том 3442 из CEUR Workshop Proceedings, CEUR-WS.org, (2023)