Uncovering the dynamics of human-AI hybrid performance: A qualitative meta-analysis of empirical studies

Abstract

• The futile debate on whether humans perform better than AI or vice versa is rooted in the automation approach. Instead, an augmentation approach focuses on hybrid performance. • In order to understand why human-AI hybrid performance is sometimes high sometimes not, we gather performance factors from empirical studies. • We identify 24 factors that affect the human-AI hybrid performance and we group these into four clusters. • The identified factors are interrelated and understanding how they interact is essential for improving the hybrid human-AI performance. • Insights to operationalize HCAI principles, particularly in addressing challenges regarding the two critical factors: transparency and trust. Human-AI collaboration is an increasingly important area of research as AI systems are integrated into everyday workflows and moving beyond mere automation and augmentation to more collaborative roles. However, existing research often overlooks the dynamics and performance aspects of this interaction. Our study addresses this gap through a review of empirical AI studies from 2018–2024, focusing on the key factors influencing human-AI collaboration outcomes within the spectrum of Human-Centered Artificial Intelligence (HCAI). We identify 24 critical performance factors that influence hybrid performance, grouped into four categories using thematic analysis. Then, we uncover and analyze the complex, non-linear interdependencies between these factors. We present these relationships in a factor dependency graph, highlighting the most influential nodes. The graph and specific factor interactions supported by the papers reveal a quite complex web, an interconnectedness of factors. As opposed to being an easy-to-predict combination of inputs, human-AI collaboration in a given context likely leads to a dynamic, evolving system with often non-linear effects on its hybrid performance. Our findings and the previous research on automation technologies suggest that the application of AI tools in collaborative scenarios would benefit from a comprehensive performance framework. Our study intends to contribute to this future line of research with this initial framework.

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