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TRUVACE RECORD VERSION
record: TRV-2026-2497
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:43:09.333938Z
status: published
lens: p_space
sector: crime
headline: Artificial intelligence in criminal investigation: effects of large language model consultation on confirmation bias in a simulated investigation game
dek: Objectives Confirmation bias can distort information search and evidence interpretation in criminal investigations. We tested whether consulting a large language model (LLM) could reduce this bias in group decisions.Methods In total, 108 participants formed 36 three-person teams randomly assigned to LLM consultation, theory-based information and debiasing strategies, or unaided control. Teams investigated homicide cases adapted from real cases in a web-based game. Outcomes were investigative cues opened, deliber…
gain_title: (none)
problem_title: In a randomized study of 36 three-person teams in a simulated homicide game, consulting a large language model did not clearly improve accuracy in assigning culpability or cut deliberation time versus unaided controls.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: In a randomized study of 36 three-person teams in a simulated homicide game, consulting a large language model did not clearly improve accuracy in assigning culpability or cut deliberation time versus unaided controls.
problem_evidence: Group assignment had no main effect on overall culpability accuracy or deliberation time. | did not clearly improve accuracy
quick_read: Researchers randomly assigned 108 participants, in 36 three-person teams, to consult a large language model, receive theory-based information and debiasing strategies, or work unaided. Teams investigated homicide cases adapted from real cases in a web-based game.

Group assignment had no main effect on overall accuracy in assigning culpability to true perpetrators versus innocent suspects, and no effect on deliberation time. Teams with the AI consultation opened more investigative cues than controls in wrongful-conviction cases, but that extra searching did not clearly translate into better judgments.

In weighted analyses, controls were less accurate in wrongful-conviction cases than in correct-conviction cases, while the gap was numerically smaller in the LLM and theory groups. That interaction was nonsignificant in unweighted analyses, so the authors urge caution, and no clear advantage of the LLM over theory-based support was established.
limitation: The accuracy interaction appeared only in weighted analyses and was nonsignificant in unweighted analyses, so the authors say it should be interpreted cautiously; no clear advantage of the LLM over theory-based support was established.
tag: Evidence-backed problem
key_points: The study included 108 participants arranged in 36 three-person teams, randomly assigned to one of three conditions. | The three conditions were LLM consultation, theory-based information with debiasing strategies, and an unaided control. | Cases were homicide investigations adapted from real cases and played in a web-based game. | Outcomes measured were investigative cues opened, deliberation time, and culpability assigned to true perpetrators versus innocent suspects. | There was no main effect of group on overall culpability accuracy or deliberation time. | The LLM group opened more cues than controls in wrongful-conviction cases. | Theory-based support showed a numerically smaller weighted accuracy penalty, but the interaction was nonsignificant in unweighted analyses.
rundown: The experiment targeted confirmation bias, which can distort how investigators search for information and interpret evidence. Participants worked in three-person teams on group decisions, since investigations are often collaborative.

Teams were randomly assigned to consult an LLM, to receive theory-based information and debiasing strategies, or to proceed without help. They investigated homicide cases adapted from real cases in a web-based game, with some cases ending in a correct conviction and others in a wrongful one.

Controls were less accurate in wrongful-conviction than correct-conviction cases in weighted analyses, and this difference was numerically smaller in the LLM and theory groups. The authors say this finding should be interpreted cautiously because it disappeared in unweighted analyses.

The authors conclude that LLM consultation increased information search in wrongful-conviction cases but did not clearly improve accuracy, and say the findings inform the design of AI-assisted investigative decision support.
sources:
- peer_reviewed | Psychology, Crime & Law | https://doi.org/10.1080/1068316x.2026.2739982 | 2026-10-13
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