Problem
Research questions and friction points this paper is trying to address.
AI risk perception
driving safety concerns
latent profiles
person-centered analysis
public attitudes
Latent Profiles of AI Risk Perception and Their Differential Association with Community Driving Safety Concerns: A Person-Centered Analysis
🤖 AI Summary
This study addresses a critical gap in existing research, which often examines public attitudes toward artificial intelligence (AI) and driving safety in isolation, thereby overlooking the within-individual covariance structure of risk perceptions. Employing a person-centered latent class analysis approach, combined with BCH-adjusted distal outcome modeling and weighted multinomial logistic regression, this work identifies four distinct subgroups among U.S. adults—Moderate Skeptics, Concerned Pragmatists, AI Ambivalent, and Extreme Alarm—characterized by monotonic associations between perceived AI-related risks and assessments of driving danger severity. These patterns are primarily driven by trust in AI, except in comparisons between AI and human drivers. The findings provide empirical support for the cultural theory of risk, particularly its worldview hypothesis, by revealing how individual-level cognitive structures co-vary across domains of technological and community safety concerns.
📝 Abstract
Public attitudes toward artificial intelligence (AI) and driving safety are typically studied in isolation using variable-centered methods that assume population homogeneity, yet risk perception theory predicts that these evaluations covary within individuals as expressions of underlying worldviews. This study identifies latent profiles of AI risk perception among U.S. adults and tests whether these profiles are differentially associated with community driving safety concerns. Latent class analysis was applied to nine AI risk-perception indicators from a nationally representative survey (Pew Research Center American Trends Panel Wave 152, n = 5,255); Bolck-Croon-Hagenaars corrected distal outcome analysis tested class differences on nine driving-safety outcomes, and survey-weighted multinomial logistic regression identified demographic and ideological predictors of class membership. Four classes emerged: Moderate Skeptics (17.5%), Concerned Pragmatists (42.8%), AI Ambivalent (10.6%), and Extreme Alarm (29.1%), with all nine driving-safety outcomes significantly differentiated across classes. Higher AI concern mapped monotonically onto greater perceived driving-hazard severity; the exception, comparative evaluation of AI versus human driving, was driven by trust rather than concern level. The cross-domain covariation provides person-level evidence for the worldview-based risk structuring posited by Cultural Theory of Risk and yields a four-class segmentation framework for AV communication that links AI risk orientation to transportation safety attitudes.
Innovation
Methods, ideas, or system contributions that make the work stand out.
latent class analysis
person-centered approach
AI risk perception
driving safety concerns
Cultural Theory of Risk
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