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Full-Text Articles in Social and Behavioral Sciences
Increasing The Value Of Xai For Users: A Psychological Perspective, Robert R. Hoffman, Timothy Miller, Gary Klein, Shane T. Mueller, William J. Clancey
Increasing The Value Of Xai For Users: A Psychological Perspective, Robert R. Hoffman, Timothy Miller, Gary Klein, Shane T. Mueller, William J. Clancey
Michigan Tech Publications
This paper summarizes the psychological insights and related design challenges that have emerged in the field of Explainable AI (XAI). This summary is organized as a set of principles, some of which have recently been instantiated in XAI research. The primary aspects of implementation to which the principles refer are the design and evaluation stages of XAI system development, that is, principles concerning the design of explanations and the design of experiments for evaluating the performance of XAI systems. The principles can serve as guidance, to ensure that AI systems are human-centered and effectively assist people in solving difficult problems.
Evaluating Machine-Generated Explanations: A “Scorecard” Method For Xai Measurement Science, Robert R. Hoffman, Mohammadreza Jalaeian, Connor Tate, Gary Klein, Shane T. Mueller
Evaluating Machine-Generated Explanations: A “Scorecard” Method For Xai Measurement Science, Robert R. Hoffman, Mohammadreza Jalaeian, Connor Tate, Gary Klein, Shane T. Mueller
Michigan Tech Publications
Introduction: Many Explainable AI (XAI) systems provide explanations that are just clues or hints about the computational models-Such things as feature lists, decision trees, or saliency images. However, a user might want answers to deeper questions such as How does it work?, Why did it do that instead of something else? What things can it get wrong? How might XAI system developers evaluate existing XAI systems with regard to the depth of support they provide for the user's sensemaking? How might XAI system developers shape new XAI systems so as to support the user's sensemaking? What might be a useful …