BBL Speaker Series: Towards a Science of Human-AI Decision Making: Empirical Understandings, Computational Models, and Intervention Designs
Abstract: Artificial intelligence (AI) technologies have been increasingly integrated into human workflows. For example, the usage of AI-based decision aids in human decision-making processes has resulted in a new paradigm of human-AI decision making—that is, the AI-based decision aid provides a decision recommendation to the human decision makers, while humans make the final decision. The increasing prevalence of human-AI collaborative decision making highlights the need to understand how humans and AI collaborate with each other in these decision-making processes, and how to promote the effectiveness of these collaborations. In this talk, I’ll discuss a few research projects that my group carries out on empirically understanding how humans trust the AI model via human-subject experiments, quantitatively modeling humans’ adoption of AI recommendations, and designing interventions to influence the human-AI collaboration outcomes (e.g., improve human-AI joint decision-making performance).
Bio: Ming Yin is an Assistant Professor in the Department of Computer Science, Purdue University. Her current research interests include human-AI interaction, crowdsourcing and human computation, and computational social sciences. She completed her Ph.D. in Computer Science at Harvard University and received her bachelor’s degree from Tsinghua University. Ming was the Conference Co-Chair of AAAI HCOMP 2022. Her work was recognized with multiple best paper (CHI 2022, CSCW 2022, HCOMP 2020) and best paper honorable mention awards (CHI 2019, CHI 2016).