A practical approach to mitigating cognitive bias effects in forensic casework
Bibliographic record
Abstract
Historically, forensic science results have been admitted in court, with minimal scrutiny regarding their scientific validity. However, following the National Academy of Sciences (NAS, 2009) report, the forensic community has undergone a significant transformation. This shift has demonstrated that forensic scientists and laboratories want to ensure the scientific rigor and quality of their results, but that they are often uncertain where to begin when addressing concerns about error and bias. In response to these challenges, the Department of Forensic Sciences in Costa Rica designed and began a pilot program within the Questioned Documents Section of the laboratory. This program incorporates various existing research-based tools, including Linear Sequential Unmasking-Expanded, Blind Verifications, case managers, and other important mitigation strategies to enhance the reliability of and reduce subjectivity in forensic evaluations. This article discusses the journey from initial planning through to implementation and the impact of the strategies that were adopted. The article describes how the Department systematically addressed key barriers to implementation and maintenance after implementation, providing a model to other laboratories for prioritizing resource allocation. This successful pilot program demonstrates that there are feasible and effective changes that can mitigate bias, and this article presents evidence that existing recommendations in the literature can be used within laboratory systems to reduce error and bias in practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.237 | 0.334 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.011 | 0.030 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".