Artificial Intelligence’s Societal Impacts, Governance, and Ethics: Introduction to the 2019 Summer Institute on AI and Society and its Rapid Outputs
Bibliographic record
Abstract
The works assembled here are the initial outputs of the First International Summer Institute on Artificial Intelligence and Society (SAIS). The Summer Institute was convened from July 21 to 24, 2019 at the Alberta Machine Intelligence Institute (Amii) in Edmonton, in conjunction with the 2019 Deep Learning/Reinforcement Learning Summer School. The Summer Institute was jointly sponsored by the AI Pulse project of the UCLA School of Law (funded by a generous grant from the Open Philanthropy Project) and the Canadian Institute for Advanced Research (CIFAR), and was coorganized by Ted Parson (UCLA School of Law), Alona Fyshe (University of Alberta and Amii), and Dan Lizotte (University of Western Ontario). The Summer Institute brought together a distinguished international group of 80 researchers, professionals, and advanced students from a wide range of disciplines and areas of expertise, for three days of intensive mutual instruction and collaborative work on the societal implications of AI, machine learning, and related technologies. The scope of discussions at the Summer Institute was broad, including all aspects of the societal impacts of AI, lternative approaches to their governance, and associated ethical issues.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 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".