AI models are far more susceptible to misleading nudges than humans, study shows

Artificial intelligence models are highly susceptible to subtle changes in how options are presented. A recent study published in the Proceedings of the National Academy of Sciences provides evidence that this hypersensitivity leads to easily manipulated, unpredictable outcomes.

Artificial intelligence models are increasingly acting on our behalf to make choices, but they tend to be highly susceptible to subtle changes in how options are presented. A recent study published in the Proceedings of the National Academy of Sciences provides evidence that AI agents overreact to minor cues in their environments, responding to these nudges much more strongly than humans do. This hypersensitivity suggests that relying on current language models for autonomous decisions could lead to unpredictable or easily manipulated outcomes.

People increasingly expect language models to do more than just chat. Software programs powered by large language models, commonly known as LLMs, are being designed to browse the web, operate tools, and make financial or shopping decisions for users. In these situations, the AI acts as an autonomous agent that must navigate sequential choices to achieve a goal.

Scientists do not fully understand how these computer programs actually arrive at their decisions. Behavioral science shows that human decision-making relies heavily on choice architecture. Choice architecture refers to the specific way options are framed or presented to a person.

For example, a default option or a highlighted button can gently steer, or nudge, a person toward a specific choice. Humans have biological constraints on their time and cognitive energy. They use mental shortcuts to balance the cost of gathering information against the reward of making a good choice, a concept researchers refer to as bounded rationality.

Because AI models do not share these human biological limits, their responses to nudges remain somewhat mysterious. Prior research shows that language models can be fragile, changing their answers based on slight formatting shifts or attempts to agree with a user’s stated opinions. However, researchers wanted to test how AI agents handle meaningful, structured nudges designed to mimic real-world decision environments.

“Many applications of AI agents tacitly assume that, under uncertainty, they will react in roughly human-like ways, if not more rationally,” said Manuel Cherep, a doctoral student at the Massachusetts Institute of Technology’s Media Lab and lead