Anthropic 研究：专家会采纳 AI 智能体的随机意见，并认为是自己的判断

Anthropic had 50 experts use an AI agent to work through four analytical tasks with no clearly right answer. The agent's preference for one of two valid options was set by coin toss. Participants followed the agent's preference in up to 86 percent of cases, and most believed the final result was their own. Experience, expertise, and trust in AI made no difference. Participants did work faster and made fewer serious errors with AI. The authors warn that if many teams use the same model, they could create a false consensus in research and weaken AI safety checks. They suggest agents should argue both sides or ask users for their view first. An experiment by Anthropic with 50 experts found that people often followed their AI agent's randomly assigned preference on questions with no clearly right answer, and most thought the final result was their own. Handing a data analysis to an AI agent saves time and manual work, but it raises the question of who actually makes the decisions that shape the final recommendation. An Anthropic study suggests the agent makes most of them, often without the experts noticing. In the experiment , 50 experts in causal inference worked on a realistic analysis task where they examined observational data from a fictional farming cooperative and estimated the productivity gains from a voluntary farm advisory program. They then had to recommend whether the cooperative should expand the program to all farms or end it, though participants or the agent could also suggest extending it for one year. Ad What participants didn't know was that a coin toss had set the agent's preference for one of two valid options at each decision point. The agent couldn't lie or hide data; it only had to make the strongest honest case for its assigned side. Ad Participants adopted the agent's preference in 86 percent of the four analytical decisions, such as how to measure productivity or which statistical method to use. That's far above the 50 percent you'd expect if people chose at random. The authors call the agent's net influence the "flip rate," which ranged from 62 to 82 percent depending on the task. Already having an opinion offered little protection. When participants made a choice before the agent stated its preference, an opposing view made them 53 percentage points more likely to switch sides than an agreeing one. Ad The authors tested possible safeguards, including years of analysis experience, self-rated expertise, education, test scores, and trust in AI. None had a statistically reliable link to the results after correcting for multiple tests. Age stood out before that correction, though the sample of 50 gave the study little power to detect such effects. The agent's assigned preference had only a small direct effect on the final policy recommendation to the cooperative's board, slightly shifting votes without reaching statistical significance. Some experts even overruled the agent when its advice didn't match their calculated estimate of productivity. Ad That may sound reassuring, as though AI makes suggestions and people make decisions. But the researchers say the final result doesn't show that participants kept control. The four earlier analytical decisions had a major effect on the estimated productivity gain, which in turn shaped the board recommendation. Ad