Tessling on My Brain: The Future of Lie Detection and Brain Privacy in the Criminal Justice System
Bibliographic record
Abstract
The criminal justice system requires a reliable means of detecting truth and lies. A battery of emerging neuroimaging technologies make 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 neuroimaging experts believe in the possibility of reliable brain scan lie detection systems in the criminal justice system. Although philosophers, psychologists and sociologists have appreciated the complexity of distinguishing truth from lies, our courts are increasingly looking to neuroscience as a means of reducing the search for truth to the existence or non-existence of certain brain states. In this article, the authors assert that Canadian courts' current approach to protecting privacy cannot easily accommodate the challenges caused by these emerging technologies, examine the possibility of remote, surreptitious brain surveillance and address the potential threat to privacy this poses. 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 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. The article suggests that the goal of using brain based lie detection in our criminal justice system will require better developed theories and understanding of privacy.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.075 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".