TY - JOUR
T1 - Can AI-Attributed News Challenge Partisan News Selection? Evidence from a Conjoint Experiment
AU - Zoizner, Alon
AU - Matthes, Jörg
AU - Corbu, Nicoleta
AU - Vreese, Claes H. de
AU - Esser, Frank
AU - Koc-Michalska, Karolina
AU - Schemer, Christian
AU - Theocharis, Yannis
AU - Zilinsky, Jan
PY - 2025/6
Y1 - 2025/6
N2 - With artificial intelligence (AI) increasingly shaping newsroom practices, scholars debate how citizens perceive news attributed to algorithms versus human journalists. Yet, little is known about these preferences in today’s polarized media environment, where partisan news consumption has surged. The current study explores this issue by providing a comprehensive and systematic examination of how citizens evaluate AI-attributed news compared to human-based news from like-minded and cross-cutting partisan sources. Using a preregistered conjoint experiment in the United States (N = 2,011) that mimics a high-choice media environment, we find that citizens evaluate AI-attributed news as negatively as cross-cutting news sources, both in terms of attitudes (perceived trustworthiness) and behavior (willingness to read the news story), while strongly preferring like-minded sources. These patterns remain stable across polarizing and non-polarizing issues and persist regardless of citizens’ preexisting attitudes toward AI, political extremity, and media trust. Our findings thus challenge more optimistic views about AI’s potential to facilitate exposure to diverse viewpoints. Moreover, they suggest that increased automation of news production faces both public mistrust and substantial reader resistance, raising concerns about the future viability of AI in journalism.
AB - With artificial intelligence (AI) increasingly shaping newsroom practices, scholars debate how citizens perceive news attributed to algorithms versus human journalists. Yet, little is known about these preferences in today’s polarized media environment, where partisan news consumption has surged. The current study explores this issue by providing a comprehensive and systematic examination of how citizens evaluate AI-attributed news compared to human-based news from like-minded and cross-cutting partisan sources. Using a preregistered conjoint experiment in the United States (N = 2,011) that mimics a high-choice media environment, we find that citizens evaluate AI-attributed news as negatively as cross-cutting news sources, both in terms of attitudes (perceived trustworthiness) and behavior (willingness to read the news story), while strongly preferring like-minded sources. These patterns remain stable across polarizing and non-polarizing issues and persist regardless of citizens’ preexisting attitudes toward AI, political extremity, and media trust. Our findings thus challenge more optimistic views about AI’s potential to facilitate exposure to diverse viewpoints. Moreover, they suggest that increased automation of news production faces both public mistrust and substantial reader resistance, raising concerns about the future viability of AI in journalism.
U2 - 10.1177/19401612251342679
DO - 10.1177/19401612251342679
M3 - Article
SN - 1940-1612
SP - 19401612251342679
JO - International Journal of Press/Politics
JF - International Journal of Press/Politics
ER -