When combinations of humans and AI are useful: A systematic review and meta-analysis

• URL: http://arxiv.org/abs/2405.06087v2
• Date: Tue, 29 Oct 2024 14:45:26 GMT
• Title: When combinations of humans and AI are useful: A systematic review and meta-analysis
• Authors: Michelle Vaccaro, Abdullah Almaatouq, Thomas Malone,
• Abstract summary: We conducted a meta-analysis of over 100 recent studies reporting over 300 effect sizes. We found that, on average, human-AI combinations performed significantly worse than the best of humans or AI alone.
• Score: 0.0
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• Abstract: Inspired by the increasing use of AI to augment humans, researchers have studied human-AI systems involving different tasks, systems, and populations. Despite such a large body of work, we lack a broad conceptual understanding of when combinations of humans and AI are better than either alone. Here, we addressed this question by conducting a meta-analysis of over 100 recent experimental studies reporting over 300 effect sizes. First, we found that, on average, human-AI combinations performed significantly worse than the best of humans or AI alone. Second, we found performance losses in tasks that involved making decisions and significantly greater gains in tasks that involved creating content. Finally, when humans outperformed AI alone, we found performance gains in the combination, but when the AI outperformed humans alone we found losses. These findings highlight the heterogeneity of the effects of human-AI collaboration and point to promising avenues for improving human-AI systems.

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