更多并不总是更好：在多AI建议中平衡决策准确性和从众压力
More Isn't Always Better: Balancing Decision Accuracy and Conformity Pressures in Multi-AI Advice
AI总结：
研究探讨了多AI建议对人类决策的影响，发现小规模面板提升准确性，但大面板无增益。内部共识影响从众压力，高共识导致过度依赖，单一异议减少从众压力，广泛分歧则引发混淆。人类化呈现提升感知效用和自主性，但不增加从众压力。
摘要：
正如人们通过咨询多样化的人类顾问来提高决策能力，现在也可以通过咨询多个AI系统来实现。先前关于群体决策的研究表明，建议聚合会创造从众压力，导致过度依赖。然而，多AI咨询如何提升或削弱人类决策的条件仍不明确。我们进行了三个任务的实验，参与者接收了AI面板的建议。我们改变了面板大小、面板内共识程度以及呈现的人类化程度。小规模面板相比单个AI在准确性上有所提升；较大面板无明显提升。面板内共识程度影响参与者对AI建议的依赖程度：高共识促进过度依赖；单一异议减少从众压力；广泛分歧造成困惑并削弱适当依赖。人类化呈现在某些任务中提高了感知效用和自主性，但不增加从众压力。这些发现为呈现多AI建议提供了设计启示，旨在在保持准确性的同时减轻从众压力。
英文摘要：
Just as people improve decision-making by consulting diverse human advisors, they can now also consult with multiple AI systems. Prior work on group decision-making shows that advice aggregation creates pressure to conform, leading to overreliance. However, the conditions under which multi-AI consultation improves or undermines human decision-making remain unclear. We conducted experiments with three tasks in which participants received advice from panels of AIs. We varied panel size, within-panel consensus, and the human-likeness of presentation. Accuracy improved for small panels relative to a single AI; larger panels yielded no gains. The level of within-panel consensus affected participants' reliance on AI advice: High consensus fostered overreliance; a single dissent reduced pressure to conform; wide disagreement created confusion and undermined appropriate reliance. Human-like presentations increased perceived usefulness and agency in certain tasks, without raising conformity pressure. These findings yield design implications for presenting multi-AI advice that preserve accuracy while mitigating conformity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
