222 citations · 457 across the 4 of their papers we have counts for
4 papers
Principles of Explanation in Human-AI Systems
Shane T. Mueller, Elizabeth S. Veinott, Robert R. Hoffman +4
Explainable Artificial Intelligence (XAI) has re-emerged in response to the development of modern AI and ML systems. These systems are complex and sometimes biased, but they nevert…
Explaining AI as an Exploratory Process: The Peircean Abduction Model
Robert R. Hoffman, William J. Clancey, Shane T. Mueller
Current discussions of "Explainable AI" (XAI) do not much consider the role of abduction in explanatory reasoning (see Mueller, et al., 2018). It might be worthwhile to pursue this…
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI
Shane T. Mueller, Robert R. Hoffman, William Clancey +2
This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is nec…
Metrics for Explainable AI: Challenges and Prospects
Robert R. Hoffman, Shane T. Mueller, Gary Klein +1
The question addressed in this paper is: If we present to a user an AI system that explains how it works, how do we know whether the explanation works and the user has achieved a p…