Biased AI improves human decision-making but reduces trust
This challenges conventional wisdom on AI neutrality, suggesting strategic use of biases could improve decision-making, though it is incremental in exploring human-AI interaction dynamics.
The study tested whether culturally biased AI improves human decision-making, finding that partisan AI assistants enhanced performance and reduced evaluative bias in 2,500 participants, but also decreased trust compared to neutral AI.
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.