On the Effect of Information Asymmetry in Human-AI Teams
This addresses the challenge of effective human-AI decision-making in applications where humans have access to different contextual information, though it is incremental as it builds on prior work on explainable AI and complementarity.
The paper tackled the problem of achieving complementary team performance (CTP) in human-AI teams by focusing on information asymmetry as a source of complementarity potential, and through an online experiment, demonstrated that humans can use contextual information to adjust AI decisions to achieve CTP.
Over the last years, the rising capabilities of artificial intelligence (AI) have improved human decision-making in many application areas. Teaming between AI and humans may even lead to complementary team performance (CTP), i.e., a level of performance beyond the ones that can be reached by AI or humans individually. Many researchers have proposed using explainable AI (XAI) to enable humans to rely on AI advice appropriately and thereby reach CTP. However, CTP is rarely demonstrated in previous work as often the focus is on the design of explainability, while a fundamental prerequisite -- the presence of complementarity potential between humans and AI -- is often neglected. Therefore, we focus on the existence of this potential for effective human-AI decision-making. Specifically, we identify information asymmetry as an essential source of complementarity potential, as in many real-world situations, humans have access to different contextual information. By conducting an online experiment, we demonstrate that humans can use such contextual information to adjust the AI's decision, finally resulting in CTP.