Sarah Lebovitz, Assistant Professor of Information Technology, University of Virginia McIntire School of Commerce
Friday, January 13, 2022
Abstract
Machine learning-based predictive technologies are promising to transform how professionals conduct knowledge work by augmenting their decision-making capabilities. We know little, however, about how human-AI augmentation takes place in practice. Yet, gaining this understanding is particularly important when professionals use AI tools to form judgments on critical decisions. In this talk, I will discuss results from an in-depth field study in a major U.S. hospital where AI tools were used in multiple departments by diagnostic radiologists making breast cancer, lung cancer, and bone age determinations. The study illustrates the hindering effects of opacity that professionals experienced when using AI tools and explores how these professionals grappled with it in practice. In all three departments, this opacity resulted in professionals experiencing increased uncertainty because AI tool results often diverged from their initial judgment without providing underlying reasoning. Only in one department (of the three) did professionals consistently incorporate AI results into their final judgments, achieving what we call engaged augmentation. These professionals invested in AI interrogation practices—practices enacted by human experts to relate their own knowledge claims to AI knowledge claims. Professionals in the other two departments did not enact such practices and did not incorporate AI inputs into their final decisions, which we call unengaged “augmentation.” I will discuss the challenges involved in augmenting professional judgment with powerful, yet opaque, technologies and further early stage studies related to decision making in ethical, fairness-related contexts.
Biography
Sarah Lebovitz received her PhD from New York University Stern School of Business and is currently an assistant professor of Information Technology at the University of Virginia McIntire School of Commerce. Her research uses in-depth qualitative methods to investigate how new technologies are changing organizational processes, occupations, and work practices. In particular, her work explores how machine learning-based predictive tools impact critical decision making in medical diagnosis and contexts with strong fairness implications. Her research has been published in Management Information Systems Quarterly, Organization Science, and Academy of Management Journal, as well as Harvard Business Review and Sloan Management Review, and has received the 2022 MISQ Best Paper award and 2022 AIS Senior Scholars award. Sarah is currently serving as the representative-at-large for the CTO division of Academy of Management and received the 2022 Award for Best Associate Editor for the CTO division.