Reading the Judicial Mind: Predicting the Courts' Reaction to the Use of Neuroscientific Evidence for Lie Detection
Bibliographic record
Abstract
How will the courts react to the emerging technology of detecting deception using neuroscientific methods such as neuro-imaging? The sociological theory of the autonomy of technology suggests that if neuroscientific techniques come to be seen as reliable for this purpose, other objections will soon be abandoned. The history of the judicial reaction to DNA evidence illustrates this pattern. As DNA evidence came to be seen as highly reliable, the courts rapidly abandoned their concerns that juries would be overwhelmed by the “mystique of science” and that the justice system would be “dehumanized.” The legal justifications for rejecting polygraph evidence are explored in order to illustrate that the judicial resistance to lie detection technologies, including neuro-imaging, can be expected to follow a similar pattern. The key determinant of whether courts are likely to accept neuroscientific evidence for the purpose of lie detection is the degree to which this evidence is considered to be reliable. Competing concerns about the “dehumanization” of the justice system, or the customary judicial attachment to protecting credibility determination as a purely human function, are unlikely to be able to overcome the pressure to adopt reliable neuroscientific technologies for lie detection should such technologies develop. This is because technologies that are widely accepted as reliable cannot be permitted to remain outside the justice system to deliver their own verdicts incompatible with those of the courts. The continued legitimacy of the justice system cannot tolerate this. The rules of evidence and, in particular, the constitutional right to make full answer and defense are the legal mechanisms by which this accommodation would take place.
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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 | 0.033 | 0.334 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".