People defer to AI moral advice, but not blindly

As AI large language models (LLMs) become increasingly embedded in everyday technologies, should we be concerned about their capacity to influence human beliefs - particularly in the moral domain? Being persuaded because one is convinced by the LLM-generated reasons can support the moral and intellectual growth of users, while being persuaded because one defers to the LLM can prevent, or even reverse, growth and understanding. In three studies, we investigate whether and how people revise their moral judgments after receiving advice from LLMs. In Study 1, we find that despite rating human advisors as more trustworthy, participants were equally persuaded by LLMs in everyday moral dilemmas. In Study 2, we used a methodologically realistic paradigm in which participants interacted with a genuine LLM, finding that the LLM's past performance and judged trustworthiness did not have an effect on its persuasiveness in everyday moral dilemmas. In Study 3, participants interacted with an LLM that defended its moral recommendation with good reasons, no reasons, or bad (i.e., patently absurd) reasons. While high-quality reasons do not increase persuasion relative to no reasons, bad reasons may actively undermine it. Our findings suggest that users defer to the LLM on a response-by-response basis, not based on past performance or the presence of high-quality reasons alone. That people defer to AI moral advice, even if not blindly, raises concerns about the effects of AI moral advisors - a heuristic of "this advice seems good enough" is not the way we should approach moral advice.