Are Two Heads Better Than One in AI-Assisted Decision Making? Comparing the Behavior and Performance of Groups and Individuals in Human-AI Collaborative Recidivism Risk Assessment

Abstract

With the prevalence of AI assistance in decision making, a more relevant question to ask than the classical question of “are two heads better than one?’’ is how groups’ behavior and performance in AI-assisted decision making compare with those of individuals’. In this paper, we conduct a case study to compare groups and individuals in human-AI collaborative recidivism risk assessment along six aspects, including decision accuracy and confidence, appropriateness of reliance on AI, understanding of AI, decision-making fairness, and willingness to take accountability. Our results highlight that compared to individuals, groups rely on AI models more regardless of their correctness, but they are more confident when they overturn incorrect AI recommendations. We also find that groups make fairer decisions than individuals according to the accuracy equality criterion, and groups are willing to give AI more credit when they make correct decisions. We conclude by discussing the implications of our work.

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