BILL S-231: The Ethics of Familial and Genetic Genealogical Searching in Criminal Investigations
Bibliographic record
Abstract
Recent breakthroughs in criminal investigations, especially of high-profile cold cases, have helped to consolidate the role of DNA analysis in investigative contexts. Consequently, some jurisdictions are looking to expand DNA collection and analysis methods. In Canada, legislation has been proposed to expand the National DNA Databank (NDDB) and to allow familial searching in criminal and forensic investigations. This article outlines the ethical implications of the proposed legislation and, more broadly, of genealogical methods already in use that operate outside the NDDB and rely heavily on for-profit and consumer DNA services. Current DNA analysis within the criminal justice system is heavily regulated and provides important protections not only for individuals but also for genetic relatives whose biometric data is indirectly implicated. In contrast, familial searching poses risks for offender privacy as well as for their relatives. Additionally, the expanding practice of genetic genealogical searching relies on unregulated commercial products that use different technology to expose highly detailed genetic information. This technology falls short of rigorous investigational standards and poses significant problems for informed consent. We conclude that expanding DNA collection within the NDDB to include familial searching risks exacerbating existing systemic bias and that genetic genealogical searching outside of the NDDB is incompatible with existing Canadian legislation that safeguards privacy, genetic non-discrimination, and fundamental rights and freedoms.
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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 | 0.049 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.024 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".