Generative AI in American and Canadian courts: a ‘training’ approach to regulation
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
Increased usage of generative artificial intelligence (AI) within the legal profession has prompted responses from the judiciary, resulting in the issuance of various practice directives. The spectrum of these directives ranges from conservative recommendations to more radical measures demanding lawyers to disclose their use of generative AI in the preparation of legal documents or certify the absence of such software in their creation. This article explores the development of a comprehensive framework for judicial guidance on generative AI use in legal proceedings in Canada, highlighting key elements of such guidance. The author contemplates a ‘training’ approach to regulating generative AI use in legal proceedings, focusing more on the user than the technology itself. This approach emphasises the need for judicial guidance to balance constructive engagement with generative AI with lawyers’ ethical responsibilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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