Gaining Physiological Insight into Satisfaction with XAI Explanations: A Call for Research

Thomas Fischer (), Stefan Faltermaier (), Dominik Stoffels () and Marina Fiedler ()
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Thomas Fischer: University of Passau
Stefan Faltermaier: University of Passau
Dominik Stoffels: University of Passau
Marina Fiedler: University of Passau

A chapter in Information Systems and Neuroscience, 2024, pp 319-331 from Springer

Abstract: Abstract The staggering performance of prediction models based on machine learning (ML) algorithms has led to a boom in research interest in their application, but also led to the question how black box algorithms arrive at their results. To open the black box, explainable AI (XAI) approaches have been developed, which create approximated models that make the results of ML black box models more transparent to human proponents. This transparency is important for a multitude of reasons (e.g., to trust automated decision making), but not all explanations are equally well received by human explainees. Thus far, explanation satisfaction is mainly measured through self-reports, and the application of neurophysiological measures in this specific context is widely lacking. We review the existing research and make suggestions for future research directions, calling for NeuroIS research into measurement approaches that could be applied in this domain.

Keywords: Explainable AI; XAI; Explanation satisfaction; Evaluation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-58396-4_28

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