Visual decision aids: Improving laypeople’s understanding of forensic science evidence
Bibliographic record
Abstract
Forensic science plays an important role in the criminal justice system, however research and miscarriages of justice have demonstrated that laypeople can easily misunderstand the results of forensic tests. Given the importance of these test results, interdisciplinary oversight groups have called for clearer expression of forensic tests’ corresponding error rates. Meanwhile, a large body of research in the medical domain suggests that visual decision aids can improve understanding of statistical information. Seeking to apply decision aids to the forensic domain, we present three preregistered experiments (N = 879) demonstrating that visual decision aids may indeed improve understanding of forensic science evidence. A mini meta-analysis across the three experiments comparing control conditions to visual aids demonstrated a medium effect size of g = 0.35. Therefore, decision aids represent a promising, easy-to-implement way to improve laypeople’s understanding of forensic science evidence, thereby potentially preventing associated miscarriages of justice.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.011 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".