Bracing for Impact - The AI Challenge - Opening Remarks - AI & Industry
Bibliographic record
Abstract
Bracing for Impact: The Artificial Intelligence Challenge (A Roadmap for AI Governance in Canada)\nConference organized by IP Osgoode in collaboration with Aviv Gaon, Ian Stedman and the Zvi Meitar Institute for Legal Implications of Emerging Technologies at IDC Herzliya.\nOPENING REMARKSGiuseppina D’Agostino Founder & Director, IP Osgoode\nAI & INDUSTRYThe Path of Law, as Justice Holmes articulated in his seminal paper, is in constant development – like the development of a planet – each generation taking the necessary step forward. Advancements in AI promise to change our society in the years to come and will drastically affect every aspect of our legal norms. It is therefore crucial for us to confront the legal and ethical issues that these advance- ments will doubtless give rise to and to aspire to create guidelines to help us navigate the inevitable changes to our society. In this regard, we hope that Canada can provide a road map for the legal treatment of AI issues in several key areas.\nSESSION CHAIR:Giuseppina D’Agostino Founder & Director, IP Osgoode\nPANELLISTS:Ian Kerr Professor and Canada Research Chair in Ethics Law and Technology, University of OttawaRyan Calo Lane Powell and D. Wayne Gittinger Associate Professor at the University of Washington School of LawRonald Cohn Chief Pediatrician, The Hospital for Sick ChildrenDeirdre K. Mulligan Associate Professor, School of Information, UC Berkeley
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.029 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.026 | 0.025 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".