Latent Profiles of AI Risk Perception and Their Differential Association with Community Driving Safety Concerns: A Person-Centered Analysis

TL;DR

This study identifies four distinct latent profiles of AI risk perception among U.S. adults and examines how these profiles relate to community driving safety concerns, providing insights for targeted communication strategies.

Contribution

It introduces a person-centered latent class analysis approach to categorize AI risk perceptions and links these profiles to safety attitudes, advancing understanding beyond variable-centered methods.

Findings

Four AI risk perception classes identified: Moderate Skeptics, Concerned Pragmatists, AI Ambivalent, Extreme Alarm.

All nine driving-safety outcomes significantly differ across the four classes.

Higher AI concern correlates with greater perceived driving-hazard severity.

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.