Old habits die hard: Precedent, psychology, and the admissibility of forensic evidence
Bibliographic record
Abstract
Forensic evidence, long considered a cornerstone of criminal justice, has faced increasing scrutiny as recent studies and reports expose significant flaws in its scientific foundation. Techniques such as latent fingerprint analysis, microscopic hair comparison, and ballistics matching, which had been widely accepted for decades, are now being challenged for their lack of empirical validation. Yet, despite the growing acknowledgement of widespread issues affecting the reliability and validity of many types of forensic evidence, there are surprisingly few successful challenges to the admissibility of this type of forensic evidence, and, when the evidence is challenged, it is often found to be admissible. In the United States, Daubert and Rule 702 mandate that expert evidence be based on reliable principles and methods, but many courts have failed to rigorously apply these standards, often deferring to precedent rather than conducting a thorough analysis of the scientific validity of forensic techniques. This article argues that cognitive biases play a significant role in the US judicial system’s continued acceptance of unreliable forensic evidence. In particular, judges may rely on precedent as a heuristic – or cognitive shortcut – to admit unreliable forensic evidence, even in the face of new scientific evidence challenging the validity of that evidence. In this way, what appears to be deference to precedent may instead be the impact of various cognitive biases on the judge’s decision making. The discussion also considers how the bias blind spot contributes to judges’ reluctance to reject longstanding, but scientifically flawed, forensic techniques. Notwithstanding these challenges, judicial education on scientific standards, greater diversity on the bench, and a heightened awareness of cognitive biases and debiasing strategies could help mitigate these issues and promote more rigorous evaluation of forensic evidence in the courtroom.
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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 | 0.038 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.066 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".