Diverse Perspectives on AI: Examining People's Acceptability and Reasoning of Possible AI Use Cases

Jimin Mun, Wei Bin Au Yeong, Wesley Hanwen Deng, Jana Schaich Borg, Maarten Sap

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 different AI use cases to be acceptable or unacceptable remains under-explored. In this work, we investigate the attitudes and reasons that influence people's judgments about AI's development via a survey administered to demographically diverse participants (N=197). We focus on ten distinct professional (e.g., Lawyer AI) and personal (e.g., Digital Medical Advice AI) AI use cases to understand how characteristics of the use cases and the participants' demographics affect acceptability. We explore the relationships between participants' judgments and their rationales such as reasoning approaches (cost-benefit reasoning vs. rule-based). Our empirical findings reveal number of factors that influence acceptance such as general negative acceptance and higher disagreement of professional usage over personal, significant influence of demographics factors such as gender, employment, and education as well as AI literacy level, and reasoning patterns such as rule-based reasoning being used more when use case is unacceptable. Based on these findings, we discuss the key implications for soliciting acceptability and reasoning of AI use cases to collaboratively build consensus. Finally, we shed light on how future FAccT researchers and practitioners can better incorporate diverse perspectives from lay people to better develop AI that aligns with public expectations and needs.