2023/02/06 by Ruijiang Gao, Gao, Ruijiang, Maytal Saar‐Tsechansky +3 · 2 citations
Computer Science · Psychology · Social Sciences · #Artificial Intelligence (cs.AI) #Artificial intelligence #Business #Complementarity (molecular biology) #Computer science #Computer security #Counterfactual thinking #Data science #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #Exploit #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Human-Computer Interaction (cs.HC) #Knowledge management #Leverage (statistics) #Machine learning #Psychology #Risk analysis (engineering) #Social psychology
paper · pdf · doi:10.48550/arxiv.2302.02944
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/02/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper tackles the critical challenge of human-AI complementarity in decision-making. Departing from the traditional focus on algorithmic performance in favor of performance of the human-AI team, and moving past the framing of collaboration as classification to focus on decision-making tasks, we introduce a novel approach to policy learning. Specifically, we develop a robust solution for human-AI collaboration when outcomes are only observed under assigned actions. We propose a deferral collaboration approach that maximizes decision rewards by exploiting the distinct strengths of humans and AI, strategically allocating instances among them. Critically, our method is robust to misspecifications in both the human behavior and reward models. Leveraging the insight that performance gains stem from divergent human and AI behavioral patterns, we demonstrate, using synthetic and real human responses, that our proposed method significantly outperforms independent human and algorithmic decision-making. Moreover, we show that substantial performance improvements are achievable by routing only a small fraction of instances to human decision-makers, highlighting the potential for efficient and effective human-AI collaboration in complex management settings.