People prefer AI to human advice in multi-turn conversations but neither changes incentivised behaviour
Maximilian Maier, Tsz Kwan Emmy Wong, Jiahao Eric Yang, Falk Lieder, Vanessa Cheung
原文摘要
People increasingly ask AI chatbots for advice, yet large language models (LLMs) are sycophantic, overconfident, and can amplify human cognitive biases. In this article, we investigate how people perceive advice from humans versus LLMs and whether AI advice reshapes incentive-compatible donation decisions. Using Carrier, a new experimental platform, we designed an experiment where participants made a donation decision after discussing it with an AI chatbot advisor, another human, or receiving no advice. Participants judged LLM advisors to be significantly more rational, trustworthy, convincing, and sycophantic than human advisors. Analysis of chatlogs showed that LLMs were more engaged than human advisors: they shared more facts and more often stated their opinion and justified their recommendations. Despite this, donation decisions did not vary across conditions.
cs.HC