Will You Accept the AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making

Internet users make numerous decisions online on a daily basis. With the rapid advances in AI recently, AI-assisted decision making-in which an AI model provides decision recommendations and condence, while the humans make the nal decisions-has emerged as a new paradigm of human-AI collaboration. In this paper, we aim at obtaining a quantitative understanding of whether and when would human decision makers adopt the AI model's recommendations. We dene a space of human behavior models by decomposing the human decision maker's cognitive process in each decision-making task into two components: the utility component (i.e., evaluate the utility of dierent actions) and the selection component (i.e., select an action to take), and we perform a systematic search in the model space to identify the model that ts real-world human behavior data the best. Our results highlight that in AI-assisted decision making, human decision makers' utility evaluation and action selection are inuenced by their own judgement and condence on the decision-making task. Further, human decision makers exhibit a tendency to distort the decision condence in utility evaluations. Finally, we also analyze the dierences in humans' adoption behavior of AI recommendations as the stakes of the decisions vary. CCS CONCEPTS • Human-centered computing ! HCI theory, concepts and models; Empirical studies in HCI.