4 citations · 6 across the 4 of their papers we have counts for
4 papers
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…
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…
MIDAS: Deep learning human action intention prediction from natural eye movement patterns
Paul Festor, Ali Shafti, Alex Harston +3
Eye movements have long been studied as a window into the attentional mechanisms of the human brain and made accessible as novelty style human-machine interfaces. However, not ever…
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition
Paul Festor, Giulia Luise, Matthieu Komorowski +1
Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems. However, in high-risk environments such as healthcare, manufacturing, aut…