专家采纳了AI agent的随机意见，认为它们是自己的，Anthropic研究发现。

专家采纳了AI agent的随机意见，认为它们是自己的，Anthropic研究发现。

关键点

- Anthropic有50名专家使用AI agent通过四项分析任务工作，没有明显的正确答案。 兹克 1Z对两个有效选项之一的偏好被硬币掷出。

- 参与者在多达86%的案例中遵循了agent的偏好，大多数参与者认为最终结果是他们自己的。 经验、专门知识和对大赦国际的信任没有区别。 参与者工作得比较快，对AI的重犯较少。

- 作者警告说，如果许多团队使用相同的模式，他们可能在研究中产生虚假的共识并削弱AI的安全检查。 他们建议agents应先为双方争吵，或先征求用户的意见。

Anthropic与50位专家进行的一项实验发现，人们经常遵循AI agent在问题上随机分配的偏好，没有明显的正确答案，大多数认为最终结果是自己的。

将数据分析交给一个AI agent可以节省时间和手工工作，但是它融资是实际由谁做出决定来形成最终建议的问题。 Anthropic研究建议agent制造出大部分，往往没有专家注意。

在实验中，50名因果推论专家从事一项现实的分析任务，他们研究了一个虚构的农业合作社的观测数据，并估计了一个自愿农业咨询方案的生产力收益。 然后，他们不得不建议合作社是将方案扩大到所有农场还是结束，尽管参与者或agent也可以建议将其延长一年。

参与者不知道的是，一发硬币掷出agent的偏好是在每个决定点选择两个有效选项之一。 agent不能撒谎或隐藏数据；它只需要为其指定侧面提出最强的诚实理由。

专家在86%的案件中遵循大赦国际的观点

参与者在4个分析决定中的86%采纳了agent的偏好，例如如何衡量生产率或者使用哪种统计方法。 如果人们随意选择，那将远远超过你预期的50%。 作者称agent的"净影响"为"滑行率"，视任务的不同，其影响率从62%到82%不等。

已经得到的意见没有多少保护。 当参与者在agent表示其偏好之前作出选择时，一种相反的观点使得他们比同意的一方更有可能切换出53个百分点。

作者测试了可能的保障措施，包括多年的分析经验、自评的专业知识、教育、测试分数以及对AI的信任。 在更正了多项测试之后，没有任何一个与结果有统计上可靠的联系。 虽然50个样本没有给研究提供探测这种影响的力量，但这一修正之前的年龄就比较高了。

多数专家认为最后的呼唤是他们自己的

agent的指定偏好对合作社董事会的最终政策建议只有很小的直接影响，微调了票数而未达到统计意义。 一些专家甚至否决了agent，因为其建议与他们对生产力的计算估计不符。

这可能听起来令人放心，好像大赦国际提出了建议，人们作出了决定。 但研究者说最终结果并不表明参与者保持了控制。 早先的四项分析决定对估计的生产率收益产生了重大影响，这反过来又形成了董事会的建议。

当被问及后，52%的参与者称的建议比agent的更像他们自己的，另外35%的称主要是或完全属于他们自己的。 与会者正确地回顾了他们对85%决定的选择，然而，77%，即48个决定中的37个，在解释中至少使用了agent的一个论点，有时几乎用同一种语言。 只有15%提到大赦国际。

谈话日志显示，agent在77%的决定中首先表示立场。 然后，它通常只为其偏爱的一方辩护，而没有解释为什么熟练的专家可以选择另一种选择，尽管根据文件，它确实解释了不同的选择会如何影响结果。

参与者的工作速度也快得多，对AI的失误也较少。 没有agent,55%的分析，或者说11分之6，有严重的方法或编码缺陷，中间时间为90分钟。 与AI，只有6%，即50个分析中的3个存在严重缺陷，参与者在65分钟后完成。 在没有AI的情况下工作的小团体选择了不使用这个工具，并且拥有一些较少的专门知识，因此研究者无法确定有多少差距直接来自AI.

错误的共识和安全检查薄弱

研究者说这种静默的敬仰可能在科学中产生广泛的影响。 如果许多研究团队使用具有固定默认偏好的同一种模式，最终可能会产生虚假的共识。

这种风险延伸到AI安全本身。 研究人员已经在使用AI agents测试前沿的安全与对接模式，期望人类审查作为独立的检查。 但是，如果专家不知不觉地采纳agent对无限制的判断呼吁的偏见，那么一个错误的体系就可能最终引导本意为揭露其不协调的研究。

团队呼吁在真实工作上用各种方式衡量对AI agents的尊重。 Agents在说明立场之前，应当为双方提出最佳理由，或者征求专家自己的意见。 各机构需要决定哪些呼吁需要独立的人力判断，并为它们留出时间和资源。

"人与AI之间可能不存在单一的正确分工。 有些决定也许可以移交，另一些决定值得保留。 有了正确的工具，这种分解可能是我们所选择的，而不是我们所选择的，“研究者们写道。

以前的研究指向同一方向

调查结果补充了最近关于AI如何影响人类判断的研究。 有3,132名参与者参与的一项关于虚假信心的研究发现，仅仅看到AI回答几乎抹去了人们拒绝判决的意愿。 在研究的一部分中，愿意说"我不知道"的人的比例从44%下降到了3%.

介绍也很重要。 当AI生产出代码或文件等被抛光的输出时，用户检查事实的频率较低，为3.7个百分点，质疑其推理的频率较低，为3.1分，缺失上下文的频率较低，为5.2分。 逐步修改产出的用户对AI的推理提出质疑，比那些不提的用户多出5.6倍。

波兰在"Lancet Gastroenterology & Hepatology"上发表的对结肠镜的观察研究表明，工作中的常规AI使用可以产生持久效果。 在有经验的医生经常使用人工智能系统之后，他们在无人工智能的考试中发现先天性损伤的比例从28.4%下降到22.4%。 这说明人们不只是采取AI的观点，也可能失去一些自己的认知技能。

瑞士商学院的一项调查发现AI工具的使用和批判性思维之间有着很强的负面联系，主要是在17至25岁人群中。 扎实的教育似乎提供了一些保护，因为学位较高的人对AI的回答更多，对复杂的思维过程的参与更多。

这些发现来自早期的研究和快照，而其他结果则指向不同的方向。 尼日利亚的一个试点项目发现，每周两次使用聊天器作为虚拟辅导员的学生在短短的六个星期内取得了学习上的收益，相当于额外学习了将近两年。

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Experts adopt AI agent's random opinions and think they're their own, Anthropic study finds

Key Points

- 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.

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.

Experts followed the AI's view in up to 86 percent of cases

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.

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.

Most experts thought the final call was their own

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.

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.

When asked afterward, 52 percent of participants said the recommendation felt more like their own than the agent's, and another 35 percent said it was mostly or entirely their own. Participants correctly recalled their choice for 85 percent of decisions, yet 77 percent, or 37 of 48, used at least one of the agent's arguments in their explanations, sometimes in nearly the same words. Only 15 percent mentioned AI at all.

The conversation logs show that the agent stated a position first in 77 percent of decisions. It then usually argued only for its preferred side, without explaining why skilled experts might choose the alternative, though it did explain how a different choice would affect the result, according to the paper.

Participants also worked much faster and made fewer errors with AI. Without an agent, 55 percent of analyses, or 6 of 11, had serious methodological or coding flaws, and the median time was 90 minutes. With AI, just 6 percent, or 3 of 50 analyses, had serious flaws, and participants finished in 65 minutes. The small group that worked without AI had chosen not to use the tool and had somewhat less expertise, so the researchers can't say for sure how much of the gap came directly from AI.

False consensus and weaker safety checks

The researchers say this quiet deference could have wide effects in science. If many research teams use the same model with fixed default preferences, they could end up creating a false consensus.

That risk extends to AI safety itself. Researchers are already using AI agents to test frontier models for safety and alignment, expecting human review to serve as an independent check. But if experts unknowingly adopt an agent's bias on open-ended judgment calls, a misaligned system could end up steering the very research meant to expose its misalignment.

The team calls for ways to measure deference to AI agents in real work. Agents should make the best case for both sides, or ask experts for their own view before stating a position. Institutions need to decide which calls require independent human judgment and set aside time and resources for them.

"There may be no single right division of labor between people and AI. Some decisions may be fine to hand over, and others worth keeping. With the right tools, that split can be something we choose, instead of something we drift into," the researchers write.

Previous studies point in the same direction

The findings add to recent research on how AI affects human judgment. A study on false confidence with 3,132 participants found that simply seeing AI answers nearly erased people's willingness to withhold judgment. In one part of the study, the share of people willing to say "I don't know" fell from 44 to 3 percent.

Presentation matters, too. When AI produced polished outputs such as code or documents, users checked facts less often, by 3.7 percentage points, questioned its reasoning less often, by 3.1 points, and missed missing context more often, by 5.2 points. Users who revised outputs step by step questioned the AI's reasoning 5.6 times more often than those who didn't iterate.

A Polish observational study of colonoscopies published in The Lancet Gastroenterology & Hepatology suggests that routine AI use at work can have lasting effects. After experienced doctors regularly used an AI system, their rate of finding precancerous lesions in exams without AI fell from 28.4 to 22.4 percent. That suggests people don't just adopt AI's perspective but may also lose some of their own cognitive skills.

A survey by Swiss Business School found a strong negative link between AI tool use and critical thinking, mainly among people ages 17 to 25. A solid education appeared to offer some protection, as people with higher degrees questioned AI answers more and engaged more with complex thought processes.

These findings come from early studies and snapshots, and other results point in a different direction. A pilot project in Nigeria found that students who used a chatbot as a virtual tutor twice a week made learning gains in just six weeks equal to nearly two extra years of learning.

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