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Robust Counterfactual Explanations in Machine Learning: A Survey

Computer Science

Robust Counterfactual Explanations in Machine Learning: A Survey

J. Jiang, F. Leofante, et al.

Counterfactual explanations promise actionable algorithmic recourse but recent work highlights serious robustness failures. This survey reviews the fast-growing literature on robust CEs, analyzes different notions of robustness, and discusses existing solutions and limitations — research conducted by Junqi Jiang, Francesco Leofante, Antonio Rago, and Francesca Toni.

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~3 min • Beginner • English
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