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Publication Detailed Description
Journal Title
Human Behavior and Emerging Technologies
Year (definitive publication)
2023
Language
English
Country
United States of America
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Abstract
Explainable artificial intelligence (XAI), known to produce explanations so that predictions from AI models can be understood, is commonly used to mitigate possible AI mistrust. The underlying premise is that the explanations of the XAI models enhance AI trust. However, such an increase may depend on many factors. This article examined how trust in an AI recommendation system is affected by the presence of explanations, the performance of the system, and the level of risk. Our experimental study, conducted with 215 participants, has shown that the presence of explanations increases AI trust, but only in certain conditions. AI trust was higher when explanations with feature importance were provided than with counterfactual explanations. Moreover, when the system performance is not guaranteed, the use of explanations seems to lead to an overreliance on the system. Lastly, system performance had a stronger impact on trust, compared to the effects of other factors (explanation and risk).
Acknowledgements
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Keywords
Artificial intelligence (AI),Trust,Explainable AI,Risk
Fields of Science and Technology Classification
- Computer and Information Sciences - Natural Sciences
- Psychology - Social Sciences
Funding Records
| Funding Reference | Funding Entity |
|---|---|
| LA/P/0083/2020 | Fundação para a Ciência e a Tecnologia |
| UIDB/50021/ 2020 | Fundação para a Ciência e a Tecnologia |
| UIDP/50009/2020 | Fundação para a Ciência e a Tecnologia |
| H2020-ICT-48-2020/952026 | Comissão Europeia |
| TAILORH2020-ICT-48-2020/952215 | Comissão Europeia |
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