The multidimensional structure of risk: how dread and controllability shape 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
Artificial intelligence has spurred large-scale innovation, affecting politics, the economy, and society in unpredictable ways. How then do ordinary citizens perceive AI and its risks? We propose that perceived dread and controllability concerns are central to understanding public opinion about AI and its associated risks. This article introduces a theoretical framework synthesizing these dimensions and validates novel measures – the AI Dread and AI Controllability Concern Measures – using original surveys fielded in two distinct cases: Canada and Japan. Our findings reveal a multidimensional structure of AI risk attitudes, with key cross-national predictors of dread and controllability concerns including individuals’ trust in scientists, conspiracy thinking, and beliefs about technological change negatively affecting their job prospects. We encourage researchers to adopt these multi-item measures in their work on AI and its relationship to society, either as explanatory or outcome variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it