Why (not) use AI? Analyzing People's Reasoning and Conditions for AI Acceptability

• URL: http://arxiv.org/abs/2502.07287v2
• Date: Fri, 30 May 2025 13:58:39 GMT
• Title: Why (not) use AI? Analyzing People's Reasoning and Conditions for AI Acceptability
• Authors: Jimin Mun, Wei Bin Au Yeong, Wesley Hanwen Deng, Jana Schaich Borg, Maarten Sap,
• Abstract summary: We investigate the demographic and reasoning factors that influence people's judgments about AI's development.<n>We find lower acceptance of labor-replacement usage over personal health.<n>We observe unified reasoning type (e.g., cost-benefit reasoning) leading to higher agreement.
• Score: 17.420096756296896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, there has been a growing recognition of the need to incorporate lay-people's input into the governance and acceptability assessment of AI usage. However, how and why people judge acceptability of different AI use cases remains under-explored, despite it being crucial towards understanding and addressing potential sources of disagreement. In this work, we investigate the demographic and reasoning factors that influence people's judgments about AI's development via a survey administered to demographically diverse participants (N=197). As a way to probe into these decision factors as well as inherent variations of perceptions across use cases, we consider ten distinct labor-replacement (e.g., Lawyer AI) and personal health (e.g., Digital Medical Advice AI) AI use cases. We explore the relationships between participants' judgments and their rationales such as reasoning approaches (cost-benefit reasoning vs. rule-based). Our empirical findings reveal a number of factors that influence acceptance. We find lower acceptance of labor-replacement usage over personal health, significant influence of demographics factors such as gender, employment, education, and AI literacy level, and prevalence of rule-based reasoning for unacceptable use cases. Moreover, we observe unified reasoning type (e.g., cost-benefit reasoning) leading to higher agreement. Based on these findings, we discuss the key implications towards understanding and mitigating disagreements on the acceptability of AI use cases to collaboratively build consensus.

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