{"schemaVersion":"nuwa-current-record-1","version":"2026-10-29.1","publication":"published","work":{"id":"expert-persuasion","title":"AI systems out-persuade expert humans","aliases":["expert persuasion"],"authors":["Kobi Hackenburg","Caroline Wagner","Luke Hewitt","Ben M. Tappin","Ed Saunders, Hannah Rose Kirk","Helen Margetts, Christopher Summerfield"],"primaryFunction":"discovery","secondaryFunctions":[],"kind":"study","sourceId":"dr-20261028-expert-persuasion","attributionSourceId":"dr-20261028-expert-persuasion","originalUrl":"https://arxiv.org/html/2606.16475","publicationDate":"2026-06-15","updatedOn":"2026-10-28","areas":["disinformation"],"scopeNote":{"en":"Selected original sections; local editorial review. Not an independently replicated experiment.","zh":"选读原始材料；本地编辑审阅稿，不是独立复现实验。"},"protocolIds":[],"contributions":[],"summary":{"en":"Expert-human comparisons and consequential donation outcomes strengthen evidence while retaining experimental limits.","zh":"四项预注册试验区分 18,978 次对话与 6,923 人；捐款研究采用 £1 实验奖金，AI 与专业劝募者之差为奖金份额 10.8 个百分点。"},"note":{"question":null,"method":null,"finding":null,"supports":null,"limits":null,"connections":null},"resources":[],"tags":[],"contentEditionId":"2026-10-28.1"},"reading":{"id":"expert-persuasion","en":{"takeaway":"Expert-human comparisons and consequential donation outcomes strengthen evidence while retaining experimental limits.","question":"Can conversational AI change consequential behaviour relative to expert humans?","design":"Four preregistered experiments cover 18,978 conversations and 6,923 people. Study 4 compares AI with professional canvassers, measuring actual allocation of a £1 experimental bonus.","findings":["In Study 4, the AI-minus-professional contrast in donation allocation was 10.8 percentage points of the £1 bonus, with a 95% confidence interval of 6.4–15.1 points."],"interpretation":"An actual allocation is closer to behaviour than an attitude scale, while retaining a specific experimental and incentive boundary. The expert-human contrast does not establish effects on real political mobilisation.","limits":"The effect is measured in percentage points of the experimental bonus, not national fundraising or the probability of harmful action. Conversations and people have different denominators.","connections":[],"decisionRelevance":"NUWA recommendation: Compare the endpoint, human comparator and persistence with other persuasion studies before generalising to deployment."},"zh":{"takeaway":"四项预注册试验区分 18,978 次对话与 6,923 人；捐款研究采用 £1 实验奖金，AI 与专业劝募者之差为奖金份额 10.8 个百分点。","question":"AI 相对专家人类的优势能否延伸到实际捐款？","design":"四项预注册实验共 18,978 段对话、6,923 名参与者，分开研究说服条件与结果。第四项将 AI 与专业游说者比较，行为终点是实验中 1 英镑奖金的实际捐赠分配。","findings":["第四项实验中，AI 相对专业游说者的实际奖金捐赠分配差异为 10.8 个百分点（占 1 英镑奖金），95% 置信区间为 6.4–15.1 个百分点。"],"interpretation":"实际奖金分配比态度量表更接近行为结果，但其终点仍有明确的实验和激励边界。对专业人员的比较也不自动等于现实政治行动的效果。","limits":"高度组织化的实验与特定英国人群；对话数不等于独立参与者数；奖金分配效应不能外推为任意现实筹款收益。","connections":[],"decisionRelevance":"NUWA 建议：把态度、实际行为、消息长度、速度与重复参与分别编码。"},"assertionPolicy":{"findings":"source-finding","interpretation":"nuwa-interpretation","decisionRelevance":"recommendation"},"observations":[{"id":"expert-conversations","sourceId":"dr-20261028-expert-persuasion","metric":"Study conversations","value":18978,"unit":"conversations","assertionKind":"source-finding","sourceRefs":[{"sourceId":"dr-20261028-expert-persuasion","url":"https://arxiv.org/html/2606.16475","artifactSha256":"991f9cf156212429c7ddb6ce60baeaae7b46b8ac18de2c80ab7f00ea02a7e276","textSha256":"8dca968af4875fc41a16eb46d6d4572aaed570f92df383a40e1bdb55f49aa0e5","locator":"Abstract; 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