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arXiv · 2508.09297

Biased AI improves human decision-making but reduces trust

Shiyang Lai·Junsol Kim·Nadav Kunievsky·Yujin Potter·James Evans
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Abstract

Current AI systems minimize risk by enforcing ideological neutrality, yet this may introduce automation bias by suppressing cognitive engagement in human decision-making. We conducted randomized trials with 2,500 participants to test whether culturally biased AI enhances human decision-making. Participants interacted with politically diverse GPT-4o variants on information evaluation tasks. Partisan AI assistants enhanced human performance, increased engagement, and reduced evaluative bias compared to non-biased counterparts, with amplified benefits when participants encountered opposing views. These gains carried a trust penalty: participants underappreciated biased AI and overcredited neutral systems. Exposing participants to two AIs whose biases flanked human perspectives closed the perception-performance gap. These findings complicate conventional wisdom about AI neutrality, suggesting that strategic integration of diverse cultural biases may foster improved and resilient human decision-making.

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Shiyang Lai, Junsol Kim, Nadav Kunievsky, Yujin Potter, James Evans. 2025-08-12. Biased AI improves human decision-making but reduces trust. https://arxiv.org/abs/2508.09297

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