Technology Management and Information Systems Seminars

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.

As artificial intelligence (AI) transforms society, understanding factors that influence AI receptivity is increasingly important. The current research investigates which types of consumers have greater AI receptivity. Contrary to expectations revealed in four surveys, cross country data and six additional studies find that people with lower AI literacy are typically more receptive to AI. This lower literacy-greater receptivity link is not explained by differences in perceptions of AI’s capability, ethicality, or feared impact on humanity. Instead, this link occurs because people with lower AI literacy are more likely to perceive AI as magical and experience feelings of awe in the face of AI’s execution of tasks that seem to require uniquely human attributes. In line with this theorizing, the lower literacy-higher receptivity link is mediated by perceptions of AI as magical and is moderated among tasks not assumed to require distinctly human attributes. These findings suggest that companies may benefit from shifting their marketing efforts and product development towards consumers with lower AI literacy. Additionally, efforts to demystify AI may inadvertently reduce its appeal, indicating that maintaining an aura of magic around AI could be beneficial for adoption.

In this lecture, I will discuss how Hebrew natural language processing (NLP) applications are enhancing decision-making in law enforcement and healthcare. We will review recent NLP advancements that address the unique challenges of Hebrew language processing. In the first part of the lecture, I will introduce a method for automatically linking crimes by extracting behavioral patterns from Hebrew police reports, significantly reducing the manual workload for investigators. Next, I will present an explainable recommender system for surgical procedures, which integrates clinical text and demographic data to offer personalized surgery recommendations, emphasizing transparency in decision-making. These innovations underscore Hebrew NLP's potential to tackle linguistic complexities and foster progress in public safety and personalized healthcare.