This academic paper presents ACTER (Actionable CounTerfactual Sequences for Explaining Reinforcement Learning Outcomes), a new algorithm for generating counterfactual sequences to help understand and prevent failures in reinforcement learning. The…
Continue readingThe authors discuss the need for explainable AI systems, focusing on counterfactual explanations that are often used post-decision. They argue that these explanations often lack plausibility, hence limiting their value….
Continue readingThe article presents the Structured Counterfactual Diffuser (SCD), a new framework designed to generate counterfactual explanations for black-box neural network models. Counterfactual explanations are a powerful tool for understanding and…
Continue readingThe authors of the paper tackled the problems of image classifiers by generating images that optimize a classifier-derived objective. They analyzed the behavior and decisions of image classifiers by visual…
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