Psychological features of dispute content and public acceptance of AI in legal adjudication: evidence for systematic variation beyond individual differences

• URL: http://arxiv.org/abs/2607.04838v1
• Date: Mon, 06 Jul 2026 09:10:43 GMT
• Title: Psychological features of dispute content and public acceptance of AI in legal adjudication: evidence for systematic variation beyond individual differences
• Abstract summary: We examined whether psychological characteristics of dispute content shape acceptability judgments for algorithmic adjudication.<n>We found that experimentally manipulated contextual features - emotional involvement and prototypicality - modulated acceptability judgments.<n>These findings suggest that the psychological features of dispute content constitute an overlooked dimension in AI acceptance research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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