Evidence map›Paper›PMID 40282097›Full record

ArticleBehavioral sciences (Basel, Switzerland)2025

Public Perceptions of Judges' Use of AI Tools in Courtroom Decision-Making: An Examination of Legitimacy, Fairness, Trust, and Procedural Justice.

Anna Fine, Emily R Berthelot, Shawn Marsh

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Anna FineInterdisciplinary Social Psychology Ph.D. Program, University of Nevada, Reno, NV 89557, USA.ORCID 0000-0002-7277-7324
Emily R BerthelotInterdisciplinary Social Psychology Ph.D. Program, University of Nevada, Reno, NV 89557, USA.ORCID 0000-0002-7340-8029
Shawn MarshInterdisciplinary Social Psychology Ph.D. Program, University of Nevada, Reno, NV 89557, USA.ORCID 0000-0002-7517-1221

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examines the role of artificial intelligence (AI) in judicial decision-making, focusing on bail and sentencing contexts. We examined public perceptions of judges who use AI tools compared to those who rely solely on expertise. Using an experimental design, participants (N = 1800; stratified by race/ethnicity and gender) were presented with vignettes depicting judges using varying levels of AI assistance. Key outcomes included perceptions of judicial legitimacy, procedural justice, and trust in AI, with analyses stratified by racial groups (Black, Hispanic, White). The results revealed that judges relying on expertise were generally rated higher in legitimacy than those using AI; however, significant racial differences emerged. Black participants showed greater trust and perceived fairness in AI-augmented decisions compared to White and Hispanic participants. Open-ended responses further highlighted social psychological themes regarding the symbolic meaning of AI in judicial processes. These findings underscore the complexity of integrating AI in the judiciary, emphasizing the need for transparent and equitable implementation strategies to maintain public trust and fairness. Future research should explore underlying factors influencing these perceptions to inform policies that address racial disparities and enhance trust in AI-assisted legal decision-making.

Indexed as

artificial intelligencejudgeslegitimacyprocedural justicesymbolic interactionism

Identifiers

PMID40282097
PMCPMC12024057

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.