Risk and the Gender Gap in Attitudes toward Artificial Intelligence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The potential for artificial intelligence to disrupt life and work has prompted debates on its regulation. This paper examines the gender gap in attitudes toward AI, focusing on how differences in risk-taking influence support for AI adoption and regulation. Using survey data from 3,000 respondents in Canada and the United States, we find that women are more skeptical of AI’s economic benefits and more likely to emphasize its risks, such as job displacement. This gap appears to be linked to women’s higher general risk aversion and greater exposure to AI-related risks. Experimental evidence shows that as AI’s benefits become more uncertain, women’s support for its adoption drops more sharply than men’s, while their support for government intervention increases. Given AI’s potential to exacerbate gender inequalities, policies that overlook women’s perspectives risk perpetuating workplace and societal disparities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it