🤖 AI Summary
This study demonstrates that AI agents exhibit systematic decision biases when processing identical evidence, depending on how a problem is framed—particularly in high-stakes domains such as medicine, election forensics, and geopolitics. Through controlled experiments that hold evidence constant while varying contextual framing, and by integrating Bayesian inference modeling with cross-domain agent behavior analysis, the research reveals for the first time that AI systems display human-like motivated reasoning: when task framing aligns with their prior beliefs, agents are significantly more likely to endorse corresponding conclusions and adapt their search strategies, analytical norms, and evidence evaluation criteria accordingly. These findings underscore the profound influence of prior beliefs on AI reasoning and offer critical insights into the reliability and transparency of AI systems in high-risk applications.
📝 Abstract
AI agents increasingly perform open-ended tasks in settings where their conclusions can guide consequential decisions. We provide evidence that AI agents draw different conclusions from identical numerical data when the substantive framing changes. We demonstrate this behavior in high-stakes domains in medicine, election forensics, and geopolitical forecasting by holding the evidence fixed while changing the scenario in which the evidence appears. Across twelve agent-domain comparisons, agents' conclusions are strongly influenced by their prior beliefs. They are more likely to reach an affirmative conclusion when it is framed around a proposition they already regard as likely, while the reverse holds when the framing conflicts with their prior. The framing also changes how some agents work: they search more extensively, choose different analytical specifications, and evaluate the same evidence differently. These results identify a particular risk of delegating decision-making to AI agents, as their decisions may depend on prior beliefs that are neither specified in the task nor visible in the decision record.