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Explanation matters: An experimental study on explainable AI

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Peer-reviewed

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Article

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Authors

Coberger, Patricia 

Abstract

jats:titleAbstract</jats:title>jats:pExplainable artificial intelligence (XAI) is an important advance in the field of machine learning to shed light on black box algorithms and thus a promising approach to improving artificial intelligence (AI) adoption. While previous literature has already addressed the technological benefits of XAI, there has been little research on XAI from the user’s perspective. Building upon the theory of trust, we propose a model that hypothesizes that post hoc explainability (using Shapley Additive Explanations) has a significant impact on use-related variables in this context. To test our model, we designed an experiment using a randomized controlled trial design where participants compare signatures and detect forged signatures. Surprisingly, our study shows that XAI only has a small but significant impact on perceived explainability. Nevertheless, we demonstrate that a high level of perceived explainability has a strong impact on important constructs including trust and perceived usefulness. A post hoc analysis shows that hedonic factors are significantly related to perceived explainability and require more attention in future research. We conclude with important directions for academia and for organizations.</jats:p>

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Keywords

46 Information and Computing Sciences, 3503 Business Systems In Context, 4609 Information Systems, 35 Commerce, Management, Tourism and Services

Journal Title

Electronic Markets

Conference Name

Journal ISSN

1019-6781
1422-8890

Volume Title

Publisher

Springer Science and Business Media LLC