Fingerprint Identification: Potential Sources of Error and the Cause of Wrongful Convictions
Bibliographic record
Abstract
Fingerprint identification has long been used by law enforcement to either identify or eliminate potential suspects in a case. It relies on friction ridges – the upraised skin that forms grooves on fingers – and friction ridge impressions, which form from natural secretions of sweat and other trace components. Latent prints, a common term for friction ridge impressions, have many benefits and advantages as a type of forensic evidence. However, they are not a perfect tool: wrongful convictions identified by post-conviction DNA testing and the re-evaluation of forensic evidence have spawned criticism and investigation into the scientific basis of this branch of forensics. This literature review examines literature in both the scientific and legal fields, and investigates three main themes: the principle of uniqueness assumed in individualization, the presence of cognitive bias and human error in analysis, and the changing role of expert testimony in court. There are arguments both for and against uniqueness, but it is still difficult to prove using statistical models and data analysis. Bias in examiners, on the other hand, undeniably exists in different ways, and should be actively guarded against in fingerprint analysis and expert testimony. Expert witness testimony that misleads, exaggerates, or is scientifically unsupportable has been linked to wrongful convictions in the past, highlighting the importance of careful regulation of how an expert witness is advised to testify. In addition to these topics, the techniques of collecting latent print evidence and the standard procedures of analysis have also been examined and evaluated for potential sources of error. Le maintien de l’ordre public utilise depuis longtemps les empreintes digitales pour identifier et éliminer des suspects d’une affaire criminelle. Les empreintes digitales se ent aux crêtes papillaires — les crêtes et les creux qui formes des rainures sur les doigts — et des empreintes des crêtes papillaires, ce qui se forme par les sécrétions naturelles de transpiration et autres composantes de traces. Les empreintes latentes, un terme courant pour les empreintes digitales, possèdent plusieurs avantages en tant qu’élément médico-légal de preuve. Toutefois, ce n’est pas une ressource able; des condamnations injustifiées identifiées par un test d’ADN post-condamnatoire et la réévaluation de l’évidence médico-légale ont frayé des critiques et des enquêtes de la base des sciences des empreintes digitales. Cette revue examine les textes dans les domaines scientifiques et médico-légaux, et examine trois thèmes : le principe d’unicité assumé par l’individualisation, la présence d’un biais cognitif et l’erreur humaine dans l’analyse, et le rôle changeant de témoignages experts devant la Cour. Il existe des arguments pour et contre l’unicité, mais l’unicité est tout de même difficile à prouver en utilisant les modèles statistiques et l’analyse de données. Un préjugé chez les examinateurs, d’autres parts, existe incontestablement, et devrait être activement évité lors de l’analyse d’empreinte digitale et de témoignages experts. Le témoignage d’expert qui induit en erreur, qui est exagéré ou qui est scientifiquement faux a mené à des condamnations injusti ées dans le passé, ce qui met en évidence l’importance d’une législation prudente sur comment l’expert est conseillé de témoigner. En plus de ces thèmes, les techniques de collecte des empreintes digitales latentes et les procédures normales d’analyse ont aussi été examinés et évalués pour des sources d’erreurs potentielles.
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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 | 0.029 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".