Fact-checking in the age of AI: Reducing biases with non-human information sources

Abstract

This study examines the obstacles to the effectiveness of fact-checking, focusing primarily on the pervasive impact of entrenched biases. Fact-checking efforts often face resistance when linked to mistrusted sources, leading to cognitive dissonance and the rejection of messages in favor of pre-existing beliefs, a phenomenon known as motivated reasoning. This resistance hinders organizations’ ability to correct misconceptions surrounding social issues and entities. The research delves into whether non-human entities such as AI can facilitate less biased information processing due to their perceived impartiality. Applying a moderated mediation model in experimental settings, we found that labeling a source as artificial intelligence is pivotal in evaluating fact-checking. AI labels moderate the impact of partisan biases on the persuasive outcomes of fact-checks, such as message credibility and acceptance, compared to the human source. This study offers valuable insights for enhancing the effectiveness of fact-checking in the context of cognitive and psychological biases by highlighting the critical influence of information sources in reducing polarization in public perceptions of scientific issues.