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.

翻译：正如人们通过咨询不同的人类顾问来改善决策一样，现在他们也可以同时咨询多个AI系统。关于群体决策的先前研究表明，建议聚合会产生从众压力，导致过度依赖。然而，多AI咨询在何种条件下能够改善或损害人类决策仍不清楚。我们开展了三项实验，让参与者接收来自AI小组的建议。我们改变了小组规模、小组内部共识以及呈现方式的人类相似度。与单个AI相比，小规模小组提高了决策准确性；而更大规模的小组则没有带来更多收益。小组内部共识水平影响了参与者对AI建议的依赖程度：高共识促进了过度依赖；单个异议降低了从众压力；广泛分歧造成困惑并损害了适当依赖。在特定任务中，类人呈现增加了感知有用性和能动性，但并未提升从众压力。这些发现为呈现多AI建议提供了设计启示，既保持准确性又缓解从众压力。