More Isn't Always Better: Balancing Decision Accuracy and Conformity Pressures in Multi-AI Advice

• URL: http://arxiv.org/abs/2603.22152v1
• Date: Mon, 23 Mar 2026 16:17:05 GMT
• Title: More Isn't Always Better: Balancing Decision Accuracy and Conformity Pressures in Multi-AI Advice
• Abstract summary: Multi-AI consultation improves human decision-making, but conditions under which it does so remain unclear.<n>We conducted experiments with three tasks in which participants received advice from panels of AIs.<n>High consensus fostered overreliance; a single dissent reduced pressure to conform.<n>Human-like presentations increased perceived usefulness and agency in certain tasks, without raising conformity pressure.
• Score: 5.269026412088572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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