Public Trust in Generative AI: Risk Perceptions, Regulatory Safeguards, and the Acceptance of Deepfake Technology

Generative artificial intelligence (GenAI) now produces synthetic text, images, audio, and video at a quality and cost that place convincing synthetic fabrication within reach of non-specialist users. Deepfakes, synthetic media that alter a person’s appearance, voice, or behavior through machine learning (ML), are one of the most contested applications of this capability, yet public willingness to accept them under regulation remains less understood. This study examines how perceived benefits, perceived risks, privacy concerns, and demographic characteristics relate to trust in deepfake technology under regulatory safeguards. Survey data from 924 respondents from several countries were analyzed using descriptive statistics, independent-samples t-tests, analysis of variance, multiple regression, and thematic analysis of open-ended responses. Respondents recognized the potential benefits of deepfake technology for digital content creation and education while expressing widespread concern about misinformation, privacy violations, and criminal misuse. When respondents evaluated deepfake technology under an assumed privacy-protecting regulatory scenario, perceived risks did not independently predict trust, while perceived benefits were the strongest predictors. The findings indicate that institutional confidence may contribute to public acceptance of beneficial applications of generative AI, although the cross-sectional design does not establish a causal effect of regulation.