Evaluating firearm examiner testimony using large language models: a comparison of standard and knowledge-enhanced AI systems
Why this work is in the frame
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Bibliographic record
Abstract
This study evaluated the decision-making of Large Language Models (LLMs) in interpreting firearm examiner testimony by comparing a standard LLM to one enhanced with forensic science knowledge. The present study is a replication study. We assessed whether LLMs mirrored human decision patterns and if specialised knowledge led to more critical evaluations of forensic claims. We employed a 2 × 2 × 7 between-subjects design with three independent variables: LLM configuration (standard vs. knowledge-enhanced), cross-examination presence (yes vs. no), and conclusion language (seven variations). Each model condition performed 200 repetitions per scenario. This yielded a total of 5,600 measures of binary verdicts, guilt probability ratings, and credibility assessments. LLMs showed low conviction rates (9.4%) across conditions, with logical variations as a function of the way in which the firearm expert’s conclusion was formulated. Cross-examination produced lower guilt assessments and scientific credibility ratings. Importantly, knowledge-enhanced LLMs demonstrated significantly more conservative evaluations of firearm evidence across all match conditions compared to standard LLMs. LLMs, particularly when enhanced with domain-specific knowledge, showed advantages in evaluating complex scientific evidence compared to human jurors in Garrett et al. (2020), suggesting potential applications for AI systems in supporting legal decision-making.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it