The Multidimensional Structure of Risk: How Dread and Control Shape Perceptions Toward Artificial Intelligence.
Bibliographic record
Abstract
Studies of public opinion about new and emerging technologies are gaining momentum (Scheufele & Lewenstein, 2005; Cobb, 2005; Druckman & Bolsen, 2011; Zhang & Dafoe, 2019). At the centre of this emerging research agenda is a focus on people’s technological readiness (i.e., Liljander et al., 2006; Mankins, 2009) and evaluation of perceived risks (i.e., Macoubrie, 2004; Priest et al., 2010; Gallego et al., 2022). More recently, scholars have attempted to understand how one’s judgement about the seriousness or pervasiveness of new technologies impacts public acceptance (Renn & Benighaus, 2013), particularly given the salience of ChatGPT, which has raised concerns about academic integrity, personal security (Lund & Wang, 2023), and the spread of misinformation (Hsu & Thompson, 2023). Previous work suggests that individual risk evaluations have become increasingly multidimensional (Nelkin, 1989; Wildavsky & Dake, 1990; Cobb, 2005; Renn & Benighaus, 2013), with beliefs about familiarity and the technology’s degree of danger often serving as primary concerns. However, two recently overlooked dimensions with important theoretical implications for opinions about the extent to which new technologies should get adopted in society include perceived dread and control (Slovic, 1987). Cobb (2005) summarises these two dimensions as beliefs about the perceived magnitude of the risk posed by the new technology (i.e., dread) and its controllability, which refers to the perceived capacity to control the growth and outcome of the technology. Consequently, we leverage original data and a survey experiment fielded in Canada and Japan - the former a significantly understudied context for investigations of attitudes toward technology (Besley, 2013), to examine the following questions: • What is the nature of perceptions of dread and controllability concerns regarding artificial intelligence (A.I.) technology in Canada and Japan? • Who is most susceptible to beliefs about dread and controllability concerns posed by A.I. technology in these contexts? • How do frames showing varying degrees of the perceived magnitude and controllability of technological risks impact public opinion about adopting A.I.-based technology in society? And, does it vary by topic?
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".