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

## TL;DR

This paper shows that the nature of legal disputes influences public acceptance of AI in legal decisions, beyond personal traits.

## Contribution

It introduces psychological features of dispute content as a novel factor shaping AI acceptance in legal contexts.

## Key findings

- Interpersonal disputes favor human adjudicators, while institutional disputes show higher AI acceptance.
- Emotional involvement and prototypicality modulate AI acceptance, with effects varying by gender and trust.
- AI-specific expectations are the strongest predictor of acceptance (η² = 0.252).

## Abstract

Public acceptance of artificial intelligence (AI) in legal decision-making has been primarily explained through individual differences in personality traits and general attitudes toward technology. However, emerging evidence suggests that 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—beyond demographics and individual traits—shape acceptability judgments for algorithmic adjudication. Study 1 employed exploratory factor analysis on acceptability ratings across 46 legal dispute vignettes, revealing a robust two-dimensional structure distinguishing interpersonal-relational disputes (where human adjudicators were strongly preferred) from institutional-procedural disputes (where AI acceptance was comparatively higher, though not surpassing human preference in most cases). Study 2 replicated this dimensional 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 of acceptance (η2 = 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. We discuss limitations related to measurement approaches, alternative psychological mechanisms, and directions for future research employing real-world case materials and direct assessment of cognitive processes.