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Detecting Potential Local Adversarial Examples for Human-Interpretable Defense.

, , , , and . Nemesis/UrbReas/SoGood/IWAISe/GDM@PKDD/ECML, volume 11329 of Lecture Notes in Computer Science, page 41-47. Springer, (2018)

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Understanding Prediction Discrepancies in Machine Learning Classifiers., , and . CoRR, (2021)Unjustified Classification Regions and Counterfactual Explanations in Machine Learning., , , , and . ECML/PKDD (2), volume 11907 of Lecture Notes in Computer Science, page 37-54. Springer, (2019)Issues with post-hoc counterfactual explanations: a discussion., , , and . CoRR, (2019)When Mitigating Bias is Unfair: A Comprehensive Study on the Impact of Bias Mitigation Algorithms., , , and . CoRR, (2023)Intégration de connaissances dans les méthodes d'explications post-hoc., , , , and . LFA, Cépaduès, (2022)Achieving Diversity in Counterfactual Explanations: a Review and Discussion., , , , and . FAccT, page 1859-1869. ACM, (2023)Inverse Classification for Comparison-based Interpretability in Machine Learning., , , , and . CoRR, (2017)Unjustified Classification Regions and Counterfactual Explanations In Machine Learning, , , , and . (2019)Comparison-Based Inverse Classification for Interpretability in Machine Learning., , , , and . IPMU (1), volume 853 of Communications in Computer and Information Science, page 100-111. Springer, (2018)How to Choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice., , , , and . PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, page 521-533. Springer, (2021)