AI-authored news articles are becoming increasingly relevant, but little is known about how recipients evaluate news credibility attributed to an AI (artificial intelligence) author compared to a human journalist author. Referring to the machine heuristic ( Sundar, 2008 ) and Expectancy Violations Theory ( Burgoon, 2015 ), this study compared articles with AI-author labels to articles with human-author labels and without author labels. It further included AI trust and attitudinal congruence as relevant aspects to impact credibility evaluations of a news article about cannabis legalization. Based on a 2 (direction of arguments: pro vs contra) x 3 (author label: AI vs human vs none) between-subjects experiment with 844 German respondents, a MANCOVA was conducted. While controlling for people’s attitudinal congruence with the article, we found AI-authored articles’ sources evaluated as less credible than the human-authored articles’ sources, but no significant differences compared to those without author labels. News author labels did not affect message credibility evaluations, and AI trust did not have the expected interaction effect with the author labels. However, it directly affected credibility evaluations of the message and source. Furthermore, attitudinal congruence strongly impacted credibility evaluations. Results are discussed and contextualized regarding theoretical and practical implications.
Leuppert et al. (Thu,) studied this question.