Fostering human learning is crucial for boosting human-AI synergy
Julian Berger, Jason W. Burton, Ralph Hertwig, Thomas Kosch, Ralf H. J. M. Kurvers, Benito Kurzenberger, Christopher Lazik, Linda Onnasch, Tobias Rieger, Anna I. Thoma, Dirk U. Wulff, Stefan M. Herzog

TL;DR
This paper emphasizes the importance of fostering human learning through feedback and explanations to improve human-AI synergy, challenging previous research that overlooked these factors and demonstrating their positive impact on collaboration outcomes.
Contribution
It re-analyzes existing studies to highlight the role of outcome feedback and explanations in enhancing human-AI synergy, proposing a paradigm shift in research focus.
Findings
Outcome feedback improves human-AI synergy
AI explanations are effective only with human feedback
Current literature underestimates human learning's role in collaboration
Abstract
The collaboration between humans and artificial intelligence (AI) holds the promise of achieving superior outcomes compared to either acting alone. Nevertheless, our understanding of the conditions that facilitate such human-AI synergy remains limited. A recent meta-analysis showed that, on average, human-AI combinations do not outperform the better individual agent, indicating overall negative human-AI synergy. We argue that this pessimistic conclusion arises from insufficient attention to human learning in the experimental designs used. To substantiate this claim, we re-analyzed all 74 studies included in the original meta-analysis, which yielded two new findings. First, most previous research overlooked design features that foster human learning, such as providing trial-by-trial outcome feedback to participants. Second, our re-analysis, using robust Bayesian meta-regressions,…
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Taxonomy
TopicsEthics and Social Impacts of AI · AI in Service Interactions · Artificial Intelligence in Healthcare and Education
