Negativity trumps source: exploring the perceptions of AI-advice among public officials

The advancement of artificial intelligence (AI) may renew our understanding of evidence-based policymaking. Grounded in motivated reasoning and negativity bias literature, this study adopts a 2 × 2 survey experiment to explore how advice source and valence affect public officials’ trust and policy preference. Findings demonstrate that advice containing negative result exerts disproportionate influence. Moreover, when advice aligns with public officials’ prior policy beliefs, negativity bias amplifies their preference. These findings highlight how cognitive biases impede the public officials’ interpretation to policy advice.