<i>Tessling</i> on My Brain: The Future of Lie Detection and Brain Privacy in the Criminal Justice System
Bibliographic record
Abstract
The criminal justice system requires reliable means of detecting truth and lies. A battery of emerging neuro-imaging technologies makes it possible to gauge and monitor brain activity without the need to penetrate the cranium. Bypassing external physiological indicators of dishonesty relied upon by previous lie detection techniques, some neuro-imaging experts believe in the possibility of reliable brain-scan lie-detection systems in the criminal justice system. Because future generations of neuro-technology will become smaller and sleeker, will have greater read ranges, and could one day interface with implantable microchips, some of those experts also believe in the possibility of remote, surreptitious brain surveillance. In this article, the authors examine such possibilities and assert that Canadian courts’ current approach to protecting privacy cannot easily accommodate the challenges caused by these emerging technologies. The article commences with an examination of the “reasonable expectation of privacy” standard adopted by the Supreme Court of Canada, arguing that various courts across Canada have misunderstood and misapplied the R. v. Tessling decision by way of an inappropriate analogy. After a description of brain-scan lie-detection systems, the authors then examine the courts’ use of the Tessling analogy in the context of brain privacy. In addition to demonstrating the danger in a generalized judicial proposition that there is no reasonable expectation of privacy in information emanating from a private place into a public space, the authors conclude that a more robust account of brain privacy is required and speculate about possible sources of law from which this might derive.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".