AI in Business and Society
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Paper Number
2054
Paper Type
short
Description
Artificial intelligence (AI) allows organizations to offer new products and services. Despite the potential of AI, they are often black-box algorithms which have plagued the entire field of machine learning. Explainable AI (XAI) allows to shed light on black-box algorithms and is thus a promising approach to enhance the adoption of AI. While previous literature has already started to explore benefits of XAI, little research exists that compares XAI and AI from a users’ perspective. We propose a research model that investigates the relationship between XAI and use behavior mediated by trust and perceived usefulness. An experimental procedure with a one-way between-subjects design is described that uses a state-of-the-art XAI module in an experimental setting. The results of this study are important for theory development in the domain of XAI research. Moreover, organizations can draw important conclusions on the benefits of including XAI components in AI-based information systems.
Recommended Citation
Hamm, Pascal; Wittmann, Hermann Felix; and Klesel, Michael, "Explain it to Me and I will Use it: A proposal on the Impact of Explainable AI on Use Behavior" (2021). ICIS 2021 Proceedings. 9.
https://aisel.aisnet.org/icis2021/ai_business/ai_business/9
Explain it to Me and I will Use it: A proposal on the Impact of Explainable AI on Use Behavior
Artificial intelligence (AI) allows organizations to offer new products and services. Despite the potential of AI, they are often black-box algorithms which have plagued the entire field of machine learning. Explainable AI (XAI) allows to shed light on black-box algorithms and is thus a promising approach to enhance the adoption of AI. While previous literature has already started to explore benefits of XAI, little research exists that compares XAI and AI from a users’ perspective. We propose a research model that investigates the relationship between XAI and use behavior mediated by trust and perceived usefulness. An experimental procedure with a one-way between-subjects design is described that uses a state-of-the-art XAI module in an experimental setting. The results of this study are important for theory development in the domain of XAI research. Moreover, organizations can draw important conclusions on the benefits of including XAI components in AI-based information systems.
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