How to answer the question of error margin in forensic signature examination with a Bayesian approach?
Bibliographic record
Abstract
In 2011, a new Criminal Procedure Code was adopted in Switzerland. Since then, forensic handwriting experts more frequently face challenging questions from lawyers. These additional questions especially focus on the degree of certainty regarding the conclusions of the expert, and on the error margin of the signature analysis. The authors seek to address such issues in a scientific manner, in agreement with laws of probability. On the basis of a case, they present the likelihood ratio approach they have followed to evaluate their results, and a full Bayesian approach designed to explain the question of error. The questions of interest were solved with this framework and the use of numbers in a case specific way. The proposed answer aims to explain to the Court what is the probability of being wrong in case at hand. The approach followed is robust, logic and transparent and represents a practical way of addressing the error margin question.
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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 | 0.045 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".