Reaction Document to Responsible AI: A Policy Framework by the International Technology Law Association
Bibliographic record
Abstract
The Cyberjustice Laboratory is an award-winning research institute that examines and harnesses emerging technology to improve access to the justice system in Canada and beyond. Founded in 2010 by Professor Karim Benyekhlef (Faculty of Law, University of Montreal), the Laboratory brings together an interdisciplinary team of experts who specialize in law, computer science, sociology, science & technology studies, data science, and software engineering in order to reimagine and improve justice systems through technology. The Laboratory’s dual focus aims to analyze the impact of technology on access to justice and develop concrete technological tools that adapt to the reality of the justice community. We believe it is important to recognize the rich potential of artificial intelligence. Indeed, many tout the problem-solving promises of AI and use it as a marketing tool for commercial purposes. While there have been significant advancements in the use of AI for certain functions (e.g. image and audio recognition), there is need for increased improvement in and analysis of the use of AI in other tasks (e.g. analysis of legal texts, legal reasoning, judging). It is critical for legal experts to distinguish between AI-powered software in use that requires legal response and mere AI vaporware, the forward-looking promises of which have yet to be realized. Pulling apart AI initiatives that require normative reflection from those best understood as science fiction is precisely one of the objectives of the ACT Project. For these reasons, the Cyberjustice Laboratory (“Laboratory” or “Lab”) considers it highly beneficial to offer input on the development of Responsible AI: A Global Policy Framework (“the Framework”) published by the International Technology Association (“ITechLaw”). In sum, the Laboratory’s response to the Framework prioritizes the need to concretely apply these principles to national laws. We offer ways forward for the authors of the second iteration of this document to encourage Canadian lawmakers, in particular, to pragmatically implement these principles through such actions as creating an Office of Technological Assessment, enacting or amending legislation in various industries to account for AI, proactively accounting for possibilities of discrimination, and facilitating government regulation rather than industry self-regulation.
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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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.035 | 0.024 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.089 | 0.039 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".