A Study on AI‐Recommended News Suggestions Promoting Selective Exposure and Its Effect on Cognition

ABSTRACT Artificial Intelligence (AI) recommended news has the ability to promote selective exposure to news consumers. Selective exposure refers to the phenomenon where individuals tend to seek out and prefer information that confirms their pre‐existing beliefs and opinions while avoiding information that contradicts them. In this empirical investigation, the study focused on the exploration of AI‐recommended news in promoting selective exposure among readers in the context of news consumption. First, this paper discussed the AI function process application and how it could cause to hamper diverse viewpoints of the news consumer. This study aimed to investigate the potential risks and benefits of AI in recommending news. The study was conducted through quantitative methods. The quantitative phase involved an online survey to examine the relationship between news recommendation algorithms, selective exposure, perception, and patterns of news consumption. The study indicated that AI‐based news was promoting selective exposure, but at the same time, news consumers were also satisfied with the content. This study also revealed that respondents believed that they did not know whether it was affecting their critical thinking. As this study was exploratory to take an overview of the phenomenon, it is crucial to evaluate the impact of AI‐based content suggestions and algorithms on news consumption patterns and the potential for confirmation bias and misinformation.