[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82865-en":3,"doc-seo-82865-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82865,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Psychological Features of Dispute Content and Public Acceptance of AI in Legal Adjudication Evidence for Systematic Variation Beyond Individual Differences","Public acceptance of artificial intelligence in legal decision-making is often attributed to individual differences in personality traits and general technology attitudes, yet dispute-specific contextual factors may shape preferences for AI versus human adjudicators. Two Japanese-participant studies (N=1,384; N=596) test whether psychological characteristics of dispute content, beyond demographics and traits, affect acceptability judgments. Study 1 finds a two-dimensional structure separating interpersonal-relational disputes favoring human adjudication from institutional-procedural disputes showing higher AI acceptance. Study 2 replicates the structure and shows that emotion and prototypicality modulate judgments depending on dispositional trust, AI attitudes, and gender; AI-specific expectations are strongest.","TYPE Original Research PUBLISHED 10 March 2026 DOI 10.3389/frai.2026.1716094  \nOPEN ACCESS  \nEDITED BY  \nShozo Ota,  \nMeiji University, Japan  \nREVIEWED BY  \nNadia Ahmad,  \nBarry University, United States Dory Reiling,  \nConcilio Nazionale di Richerche, Italy  \n*CORRESPONDENCE  \nMasahiro Fujita  \n [m.fujita@kansai-u.ac.jp](m.fujita@kansai-u.ac.jp)  \nRECEIVED 30 September 2025  \nREVISED 11 January 2026  \nACCEPTED 20 January 2026  \nPUBLISHED 10 March 2026  \nCITATION  \nFujita M and Watamura E (2026)  \nPsychological features of dispute content and public acceptance of AI in legal adjudication: evidence for systematic variation beyond individual differences.  \nFront. Artif. Intell. 9:1716094 .  \ndoi: 10.3389/frai.2026.1716094  \nCOPYRIGHT  \n© 2026 Fujita and Watamura. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPsychological features of dispute content and public acceptance of AI in legal adjudication: evidence for systematic variation beyond individual differences  \nMasahiro Fujita 1* and Eiichiro Watamura 2  \n1Faculty of Sociology, Kansai University, Suita, Japan, 2Graduate School of Human Sciences, Osaka University, Suita, Japan  \nPublic 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.  \nKEYWORDS  \nartificial intelligence, dispute characteristics, legal decision-making, legal disputes, public acceptance, technology acceptance  \n[Frontiers in Artificial Intelligence 01 frontiersin.org](Frontiers in Artificial Intelligence 01 frontiersin.org)  \n1 Introduction  \n1.1 AI integration in judicial decision-making: current state and challenges  \nAI technologies are in","cbCaie7dIDRvuaga","https://ap.wps.com/l/cbCaie7dIDRvuaga","pdf",1178052,3,1,24,"English","en",105,"# Introduction\n## AI integration in judicial decision-making: current state and challenges\n## Psychological dimensions of legal disputes: theoretical background","[{\"question\":\"What question does the research address about AI acceptance in legal adjudication?\",\"answer\":\"It examines whether psychological characteristics of dispute content influence acceptability judgments for algorithmic adjudication beyond demographics and individual traits.\"},{\"question\":\"What key structure is found in Study 1 regarding legal dispute content?\",\"answer\":\"Exploratory factor analysis reveals a robust two-dimensional structure distinguishing interpersonal-relational disputes that strongly favor human adjudicators from institutional-procedural disputes where AI acceptance is comparatively higher.\"},{\"question\":\"Which factors most strongly predict acceptance in Study 2?\",\"answer\":\"AI-specific expectations emerge as the strongest predictor of acceptance, while experimentally varied contextual features (emotional involvement and prototypicality) interact with dispositional trust, AI-specific attitudes, and gender to modulate 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