Rage against the machine? Generative AI exposure, subjective risk, and policy preferences
Bibliographic record
Abstract
How does novel technology change public policy demands? Scholars interested in the effect of automation on policy preferences have commonly argued that exposure to automation technology increases subjective risk, which in turn predicts demand for insurance. Generative AI potentially challenges this dynamic. Based on a pre-registered online experiment with a sample of 1041 UK working-age adults we show that direct exposure to generative AI in realistic work tasks does not increase subjective risk but strengthens support for activating social policy. To understand this constellation of attitudes, we argue that exposure to technology may activate sociotropic preferences to support individuals who might be negatively affected by AI. Text analysis shows cautious optimism and thoughtful engagement with the implications of AI for work and social policy. Our findings suggest that the current uncertainty over the relative winners and losers from AI opens a window of opportunity to expand activating social policies.
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How this classification was reachedexpand
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".