Bracing for Impact - The AI Challenge - IP & Commercialization - Policy Considerations
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.\nFuture developments in the field of AI pose a challenge to intellectual property. The current legal regime does not offer protection for AI creations. Thus, registering AI patents and allocating copy- right protection for AI inventions and works is not yet possible. As a result, AI creations might fall under the public domain. We wish to address these concerns and to offer new insights and sugges- tions for the upcoming era.\nSESSION CHAIR:Carys Craig Associate Dean (Research & Institutional Relations) & Associate Professor, Osgoode Hall Law School\nPANELLISTS:Aviv Gaon PhD Candidate, Osgoode Hall Law SchoolMaya Medeiros Partner, Norton Rose Fulbright, LLPDov Greenbaum Director, Zvi Meitar Institute, IDC HerzliyaAlexandra George Senior Lecturer, UNSW Sydney LawCarole Piovesan Associate, McCarthy Tétrault LLP
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.043 | 0.026 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.037 | 0.021 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".