13 citations · 31 across the 6 of their papers we have counts for
6 papers
Deceptive Reinforcement Learning in Model-Free Domains
Alan Lewis, Tim Miller
This paper investigates deceptive reinforcement learning for privacy preservation in model-free and continuous action space domains. In reinforcement learning, the reward function…
Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support
Tim Miller
In this paper, we argue for a paradigm shift from the current model of explainable artificial intelligence (XAI), which may be counter-productive to better human decision making. I…
Explaining Model Confidence Using Counterfactuals
Thao Le, Tim Miller, Ronal Singh +1
Displaying confidence scores in human-AI interaction has been shown to help build trust between humans and AI systems. However, most existing research uses only the confidence scor…
Explainable Goal Recognition: A Framework Based on Weight of Evidence
Abeer Alshehri, Tim Miller, Mor Vered
We introduce and evaluate an eXplainable Goal Recognition (XGR) model that uses the Weight of Evidence (WoE) framework to explain goal recognition problems. Our model provides huma…
Are we measuring trust correctly in explainability, interpretability, and transparency research?
Tim Miller
This paper presents an argument for why we are not measuring trust sufficiently in explainability, interpretability, and transparency research. Most studies ask participants to com…
Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence
Thao Le, Tim Miller, Ronal Singh +1
In this paper, we show that counterfactual explanations of confidence scores help users better understand and better trust an AI model's prediction in human-subject studies. Showin…