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Preferences for AI Explanations Based on Cognitive Style and Socio-Cultural Factors

Published: 26 April 2024 Publication History

Abstract

Designing AI systems with the capacity to explain their behaviour is paramount to enable human oversight, facilitate trust, promote acceptance of technology and, ultimately, empower users and improve their experience. There are, however, several challenges to explainable AI, one of which is the generation and selection of explanations from the causal history of a given event. Causal attribution, among other cognitive processes, has been found to be influenced by socio-cultural factors, which suggests that there could be systematic differences in preferences for AI explanations between communities of users according to their cognitive style and socio-cultural characteristics. In this paper, we investigate the relationship between preferences in the explanations provided by belief-desire-intention AI agents, cognitive style (holistic vs analytical), and socio-cultural factors, such as gender, education, social class, and political and religious beliefs. We found a relationship between explanation preference, cognitive style and various socio-cultural characteristics. Holistic cognitive style is associated with preference for goal explanations while analytic cognitive style is associated with preference for belief explanations. Socio-cultural variables that affect explanation preference are gender, religious beliefs, educational attainment, some fields of education, and political party affiliation.

Supplemental Material

ZIP File
Supplementary materials: Preferences for AI Explanations Based on Cognitive Style and Socio-Cultural Factors This folder contains three files: 'SM1-scenarios.pdf' provides detail of the closed questions used in Part1 - Preference as Explainee across the 4 scenarios used in the study (diabetes, firefighter, pancake and shopping). 'SM1-coefficients.pdf' provides additional information about the multivariate logistic regression models presented in section 5.1 of the paper (coefficients, standard error, p-value and 95% CI). 'SM3-interactions.pdf' provides details on two-way interaction terms added to fractional regression (Model 2) that appear significant at first sight, but should not be considered as reliable due to low counts per cell.

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  • (2024)Attitude Towards AI: Potential Influence of Conspiracy Belief, XAI Experience and Locus of ControlInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2401249(1-13)Online publication date: 4-Oct-2024

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cover image Proceedings of the ACM on Human-Computer Interaction
Proceedings of the ACM on Human-Computer Interaction  Volume 8, Issue CSCW1
CSCW
April 2024
6294 pages
EISSN:2573-0142
DOI:10.1145/3661497
Issue’s Table of Contents
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Published: 26 April 2024
Published in PACMHCI Volume 8, Issue CSCW1

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  • (2024)Attitude Towards AI: Potential Influence of Conspiracy Belief, XAI Experience and Locus of ControlInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2401249(1-13)Online publication date: 4-Oct-2024

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