The case for Value-Based and Personalized AI Advisors 

Information Systems and Operations Management

Speaker: Maytal Saar-Tsechansky  from University of Texas at Austin

Room T-006

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. We show how AI advisors can be designed to add value even if the underlying AI model class cannot exceed the human’s overall performance, but, otherwise,  they may diminish value even when the AI exhibits super-human performance. I will discuss the reasons for these outcomes and conclude with a proposed AI research agenda in business for advancing impactful human-AI collaboration.