Bracing for Impact - The AI Challenge - AI For Social Good
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. Society is in crisis. The gap between the poor and the rich – whether in terms of age, income or skills – keeps widening as inequality grows markedly. Artificial Intelligence holds great potential for help- ing us to lessen these inequalities. While AI is often viewed as a threat to social justice, the opposite may in fact be true. Machine learning in language translation technology can collapse the barriers between third world countries and the West. Algorithmic decision-making can lessen the negative effects that bias has on minority groups. From transportation, healthcare and agriculture to sustaina- bility and governance - the positive applications of AI are unlimited in scope.\nSESSION CHAIR:Bob Tarantino PhD Candidate, Osgoode Hall Law School; Counsel, Dentons Canada LLP\nPANELLISTS:Brandie M. Nonnecke Research & Development Manager for CITRIS, UC Berkeley\nDavid Lepofsky Associate Professor, Osgoode Hall Law School\nJutta Treviranus Director of the Inclusive Design Research Centre (IDRC) and Professor, OCAD University\nGuy Seidman Professor, Radzyner School of Law, IDC Herzliya\nMaura R Grossman Research Professor, David R. Cheriton School of Computer Science, University of Waterloo
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.056 |
| Scholarly communication | 0.041 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".