BILL S-231: The Ethics of Familial and Genetic Genealogical Searching in Criminal Investigations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it