Global Public Opinion on Artificial Intelligence (GPO-AI)
Bibliographic record
Abstract
In October and November 2023, researchers at the Schwartz Reisman Institute for Technology and Society and the Policy, Elections and Representation Lab at the Munk School of Global Affairs and Public Policy at the University of Toronto completed a survey on public perceptions of and attitudes toward AI. The survey was administered to over 1,000 people in each of 21 countries, for a total of 23,882 surveys conducted in 12 languages. The combined populations of the countries sampled represent a majority of the world's population. Countries: Argentina, Australia, Brazil, Canada, Chile, China, France, Germany, India, Indonesia, Italy, Japan, Kenya, Mexico, Pakistan, Poland, Portugal, South Africa, Spain, United Kingdom, United States of America Languages: Chinese (Simplified), English, French, German, Indonesian, Italian, Japanese, Polish, Portuguese (Portugal), Portuguese (Brazil), Spanish (Spain), Spanish (Latin America). The survey explored general knowledge of and attitudes toward AI. Topics included concerns about AI, safety, regulation, autonomous vehicles and AI's effect on jobs now and in the future. Participants were asked whether they are interested in or trust applications of AI for clothes, travel, grocery shopping, dating or finance. Respondents were asked about their attitudes toward the use of emerging technologies in education, the justice system, health care and immigration. Respondents were also asked about their knowledge of and experience with ChatGPT and deepfakes.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.018 |
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".