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Record W7104003265 · doi:10.3138/utlj-2024-0095

Old habits die hard: Precedent, psychology, and the admissibility of forensic evidence

2025· article· en· W7104003265 on OpenAlexvenueno aff

Bibliographic record

VenueUniversity of Toronto Law Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicJury Decision Making Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCircumstantial evidenceScientific evidenceScrutinyCognitive biasHarmAcknowledgementDeferenceRigourCognitionDelusion

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.110
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0080.066
Scholarly communication0.0110.012
Open science0.0020.005
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.039
GPT teacher head0.347
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueUniversity of Toronto Law JournalSame topicJury Decision Making ProcessesFrench-language works237,207