Mapping public perception of artificial intelligence: Expectations, risk–benefit tradeoffs, and value as determinants for societal acceptance

Public opinion on artificial intelligence (AI) plays a pivotal role in shaping trust and AI alignment, ethical adoption, and the development equitable policy frameworks. This study investigates expectations, risk–benefit tradeoffs, and value assessments as determinants of societal acceptance of AI. Using a nationally representative sample (N = 1100) from Germany, we examined mental models of AI and potential biases. Participants evaluated 71 AI-related scenarios across domains such as autonomous driving, medical care, art, politics, warfare, and societal divides, assessing their expected likelihood, perceived risks, benefits, and overall value. We present ranked evaluations alongside visual mappings illustrating the risk–benefit tradeoffs. Our findings suggest that while many scenarios were considered likely, they were often associated with high risks, limited benefits, and low overall value. Regression analyses revealed that 96.5% ( r 2 = 0 . 965 ) of the variance in value judgments was explained by risks ( β = − 0 . 490 ) and, more strongly, benefits ( β = + 0 . 672 ), with no significant relationship to expected likelihood. Demographics and personality traits, including age, gender, and AI readiness, influenced perceptions, highlighting the need for targeted AI literacy initiatives. These findings offer actionable insights for researchers, developers, and policymakers, highlighting the need to communicate tangible benefits and address public concerns to foster responsible and inclusive AI adoption. Future research should explore cross-cultural differences and longitudinal changes in public perception to inform global AI governance. • Comprehensive Evaluation of AI Perceptions : Analyzed public mental models of AI across 71 scenarios, examining risks, benefits, and value. • Key Predictors of Value : Perceived benefits (＋0.710) and risks (−0.504) strongly determined societal acceptance of AI, explaining 96.4% of value variance. • Impact of Bias : Identified addressable biases stemming from demographics, personality traits, and prior experiences in shaping AI risk–benefit perceptions. • Visual Mapping : Visual risk–benefit tradeoO maps to illustrate public concerns about AI across domains such as healthcare, warfare, and societal divides. • Actionable Insights for Stakeholders : Recommendations for enhancing AI literacy and tailoring communication to address diverse public needs and values.