Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence
arxiv(2024)
摘要
Artificial intelligence (AI) can improve human decision-making in various
application areas. Ideally, collaboration between humans and AI should lead to
complementary team performance (CTP) – a level of performance that neither of
them can attain individually. So far, however, CTP has rarely been observed,
suggesting an insufficient understanding of the complementary constituents in
human-AI collaboration that can contribute to CTP in decision-making. This work
establishes a holistic theoretical foundation for understanding and developing
human-AI complementarity. We conceptualize complementarity by introducing and
formalizing the notion of complementarity potential and its realization.
Moreover, we identify and outline sources that explain CTP. We illustrate our
conceptualization by applying it in two empirical studies exploring two
different sources of complementarity potential. In the first study, we focus on
information asymmetry as a source and, in a real estate appraisal use case,
demonstrate that humans can leverage unique contextual information to achieve
CTP. In the second study, we focus on capability asymmetry as an alternative
source, demonstrating how heterogeneous capabilities can help achieve CTP. Our
work provides researchers with a theoretical foundation of complementarity in
human-AI decision-making and demonstrates that leveraging sources of
complementarity potential constitutes a viable pathway toward effective
human-AI collaboration.
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