Human-AI Interaction: Human Behavior Routineness Shapes AI Performance

Tianao Sun, Kai Zhao, Meng Chen

Abstract: A crucial area of research in Human-AI Interaction focuses on understanding how the integration of AI into social systems influences human behavior, for example, how news-feeding algorithms affect people’s voting decisions. But little attention has been paid to how human behavior shapes AI performance. We fill this research gap by introducing routineness to measure human behavior for the AI system, which assesses the degree of routine in a person’s activity based on their past activities. We apply the proposed routineness metric to two extensive human behavior datasets: the human mobility dataset with over 700 million data samples and the social media dataset with over 3.8 million data samples. Our analysis reveals routineness can effectively detect behavioral changes in human activities. The performance of AI algorithms is profoundly determined by human routineness, which provides valuable guidance for the selection of AI algorithms.