研究多AI建议如何影响人决策，发现小团队更准，一致易盲从，差异反致混乱。
More Isn't Always Better: Balancing Decision Accuracy and Conformity Pressures in Multi-AI Advice
- 用不同规模和意见一致性的AI小组做实验，观察人类如何采纳建议。
- 小团队比单个AI更准，但大团队无提升；高度一致导致过度依赖。
- 单独分歧可降低从众压力，人类化呈现提升可信度但不增加盲从。
与咨询多样人类顾问类似，人们如今也可借助多个AI系统做出决策。现有群体决策研究显示，建议聚合会带来从众压力，导致过度依赖。然而，多AI咨询在何种条件下能提升或损害人类决策仍不明确。我们设计了三项任务，让参与者接收由不同规模的AI组成的建议小组的意见，并改变小组内意见一致性程度及呈现方式的人类化程度。结果表明：相较于单一AI，小规模小组能提高决策准确率；更大规模小组未带来进一步提升。组内一致性越高，参与者越倾向于过度依赖建议；仅一个异议即可缓解从众压力；广泛分歧则引发困惑并削弱合理依赖。人类化呈现方式在某些任务中提升了建议的感知有用性和代理感，但未加剧从众压力。这些发现为多AI建议的设计提供了关键指导，可在保持准确性的同时减轻从众风险。
原文摘要 · Abstract (English)
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.
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