Bibliographic record
Abstract
This chapter in the Oxford Handbook of AI Governance synthesizes and discusses research on public opinion toward artificial intelligence (AI). This chapter synthesizes and discusses research on public opinion toward artificial intelligence (AI). Understanding citizens' and consumers' attitudes toward AI is important from a normative standpoint because the public is a major stakeholder in shaping the future of the technology and should have a voice in policy discussions. Furthermore, the research could help us anticipate future political and consumer behavior. Survey data worldwide show that the public is increasingly aware of AI; however, they -- unlike AI researchers -- tend to anthropomorphize AI. Demographic differences correlate with trust in AI in general: those living in East Asia have higher levels of trust in AI, while women and those of lower socioeconomic status across different regions have lower levels of trust. Surveys that focus on particular AI applications, including facial recognition technology, personalization algorithms, lethal autonomous weapons, and workplace automation, add complexity to this research topic. I conclude this chapter by recommending four new topics for future studies: 1) institutional trust in actors building and deploying AI systems, 2) the impact of knowledge and experience on attitudes toward AI, 3) heterogeneity in attitudes toward AI, and 4) the relationship between attitudes and behavior.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".