Explaining Women’s Skepticism toward Artificial Intelligence: The Role of Risk Orientation and Risk Exposure
Bibliographic record
Abstract
Rapid advances in artificial intelligence (AI) present substantial economic and social opportunities but also significant risks for different groups in society. This paper examines the gender gap in attitudes toward AI adoption, with a focus on how gender differences in risk orientation and perceptions drive skepticism toward AI’s economic benefits. Using original survey data from approximately 3,000 respon- dents across Canada and the United States, we find that women consistently perceive AI to be riskier than men. We identify two key drivers behind this gender gap: women’s higher general risk aversion and their greater exposure to AI-related risks. To establish a causal relationship between risk and AI attitudes, we further show experimentally that as the perceived benefits of AI become more uncertain, women’s support for companies adopting AI falls more sharply than men’s. Finally, structural topic modeling of open-ended responses confirms that women express greater uncertainty about AI’s benefits and more frequently anticipate little to no benefits. Given AI’s potential to exacerbate existing gender inequalities, our study highlights the critical importance of incorporating women’s perspectives into AI policy-making. Policies that do not address gender-specific risks may not only reinforce existing inequali- ties in employment and income but could also generate political backlash against AI adoption, reshaping political cleavages along gender lines.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".