The Effects of Generative AI in News on Media Credibility and Selectivity: Evidence from a Conjoint Experiment in Chile

As generative artificial intelligence (GenAI) reshapes digital journalism, questions about its consequences for media credibility and audience selectivity have intensified. Drawing on the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and research on algorithm aversion, this study examines how distinct GenAI applications influence users’ evaluations of news organizations. We conducted a pre-registered choice-based conjoint experiment in Chile (N = 2145), a context marked by declining trust in news and increasing AI adoption. The design varied seven domains of GenAI use—including supporting tasks, content creation, personalization, human oversight, and transparency—to estimate their causal effects on perceived media credibility and outlet selection. Results show that users differentiate sharply across GenAI functions. Human oversight and disclosure emerge as the strongest positive predictors of both credibility and selection. In contrast, using GenAI for menial tasks or personalization does not significantly affect evaluations, while automated content production modestly reduces credibility and selection. Attitudes toward AI moderate effects on selection but not credibility. These findings indicate that audience responses are shaped less by the mere presence of GenAI than by the accountability structures governing its use.