2 papers
cs.HC2026
Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations
Tabea E. Röber, Paul Festor, Rob Goedhart +2
Experimental user studies evaluating the effectiveness of different subtypes of post-hoc explanations for black-box models are largely nonexistent. Therefore, the aim of this study…
cs.AI2024
Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop
Hector Kohler, Quentin Delfosse, Paul Festor +1
Embracing the pursuit of intrinsically explainable reinforcement learning raises crucial questions: what distinguishes explainability from interpretability? Should explainable and…