Psychological features of dispute content and public acceptance of AI in legal adjudication: evidence for systematic variation beyond individual differences
2026-07-06 • Computers and Society
Computers and Society
AI summaryⓘ
The authors studied how people in Japan feel about using AI instead of humans to make legal decisions. They found that people prefer humans for disputes involving personal relationships but are more okay with AI for problems related to institutions or rules. They also showed that feelings like emotional involvement, how typical a case seems, and personal traits like trust and gender affect these preferences. Overall, the authors suggest that the nature of the dispute itself plays an important role in whether people accept AI in legal decisions.
AI adjudicationlegal decision-makingpublic acceptanceinterpersonal disputesinstitutional disputesemotional involvementprototypicalitytrusttechnology acceptance modelalgorithmic decision-making
Authors
Masahiro Fujita, Eiichiro Watamura
Abstract
Public acceptance of artificial intelligence (AI) in legal decision-making has been primarily explained through individual differences in personality traits and general technology attitudes. However, contextual features of legal disputes themselves may systematically influence preferences for AI versus human adjudicators. Across two studies with Japanese participants (N = 1,384 and N = 596), we examined whether psychological characteristics of dispute content shape acceptability judgments for algorithmic adjudication. Study 1 employed exploratory factor analysis on acceptability ratings across 46 legal dispute vignettes, revealing a two-dimensional structure distinguishing interpersonal-relational disputes (where human adjudicators were strongly preferred) from institutional-procedural disputes (where AI acceptance was comparatively higher). Study 2 replicated this structure in an independent sample and demonstrated that experimentally manipulated contextual features - emotional involvement and prototypicality - systematically modulated acceptability judgments, with effects varying by dispositional trust, AI-specific attitudes, and gender. AI-specific expectations emerged as the strongest predictor (eta2 = 0.252), and a three-way interaction among emotional involvement, gender, and prototypicality indicated that contextual effects are moderated by individual characteristics. These findings suggest that the psychological features of dispute content constitute an overlooked dimension in AI acceptance research, extending beyond technology acceptance models to fundamental questions about how individuals construe social problems and allocate adjudicative authority.