Title: The case for Value-Based and Personalized AI Partners
Abstract: Recent studies highlight the potential of AI to improve high-stakes human decisions in critical domains like healthcare. Despite these promising prospects, AI systems to advice experts in such contexts often fail to deliver tangible value to organizations. In this talk, I will first argue how key properties of AI-assisted high-stakes decision-making contexts are crucial to inform the development of AI advisors that meaningfully benefit decision-makers and organizations. State-of-the-art AI for advising experts is produced independently of the experts and of the organization they intend to benefit. However, I will demonstrate why idiosyncratic properties of these environments, such as an expert’s decision-making behaviors, the patterns shaping experts’ discretion of AI counsel, and the organization's tolerance of the inherent costs of engagement with AI to improve high-stakes decisions are crucial to inform the development of effective human-AI teams that benefit organizations. I will then present a framework that builds on these understandings to generate personalized and organizationally-aware AI advisors and will share results on its performance. Our results demonstrate not only the opportunity to amplify high-stakes decision-making in high-stakes settings, but also underscore our framework’s effectiveness at producing efficient advisors with the necessary properties to catalyze the widespread adoption of AI-assisted advising in organizations. I will conclude with a proposed AI research agenda in business for advancing impactful human-AI collaboration.
About the Speaker: Maytal Saar-Tsechansky is the Mary John and Ralph Spence Centennial Professor at the McCombs School of Business at the University of Texas at Austin, and a co-founder of Sweetch, an AI-based health company. Her research focuses on advancing AI to improve decision-making and to benefit people, organizations, and society. Her recent work focuses on human-AI collaboration and trustworthy AI with the overarching goal of bringing to bear human, organizational, and societal goals and constraints to catalyze AI systems’ positive impact in the world. Beyond AI, Maytal’s research aspires to make a positive impact in the world and includes challenges such as mitigation of partisan animosity and promoting democratic attitudes. Her work has been supported by government and industry and her research has been published in the top business and science venues, including Science, Journal of Machine Learning Research, Journal of Finance, and Management Science. Maytal has led the University of Texas at Austin’s Translational AI initiative and is an academic board member of the university’s Machine Learning Lab.
Language: English