Abstract:

This presentation is based on multiple published and unpublished working papers, it highlights results from last decade of research on the complexities of human-AI collaboration, emphasizing the need to move beyond a sole focus on short-term productivity gains to a synergistic partnership that values complementarity and unique human knowledge. The Borgs Paradox refers to over-reliance on AI tools to gain productivity; while there is gain in productivity, it can lead to a reduction in complementary unique human knowledge that refers to the distinct and specialized knowledge, undiscoverable by deep-learning, which humans possess. The homogenization of responses due to inappropriate use of AI, termed the "Borgs effect," diminishes the diversity of perspectives, which is crucial for creativity, innovation, and the wisdom of crowds, and ultimately contributes in improving future generations of AI solutions.  The research explores challenges on the human side, in particular, humans’ lack of metaknowledge -- the ability to accurately assess their own capabilities – that leads to poor delegation decisions to improper advice mechanisms by AI that can create over-reliance on AI advice. We challenge the assumption that AI should simply replace humans and propose a more nuanced approach where AI augments human capabilities, automates routine tasks, and allows humans to focus on more challenging problems, often by redeploying freed resources.  We design a strategic task allocation framework that considers automation, where AI substitutes for humans, and augmentation, where AI supports human decision-making. This framework also emphasizes the reallocation benefit, where human resources freed up by automation are reallocated to more complex tasks and leverage the wisdom of crowds.  We show that benefits from different types of allocations can be accrued based on the degree of within- and between-task complementarities that exist when humans and AI work together in different work environments.

About The Speaker:

Alok Gupta, holds Curtis L. Carlson Schoolwide Chair in Information Management at the Carlson School of Management, University of Minnesota. He served as a Senior Associate Dean of Faculty, Research and Administration at Carlson School in the past. From 2006 to 2014, he served as the Department chair of Information and Decision Sciences department at Carlson School. He started his academic career in 1996 as a visiting Assistant Professor at Dept. of OPIM, University of Connecticut. He received his Ph.D. in Management Science and Information from the University of Texas, Austin in 1996. His research focuses on Economic Engineering of systems – where system design explicitly considers incentives of participants – as applied to a variety of transactional systems from Internet, real-time databases, B2B systems to e-commerce and to his current focus -- Human-AI collaboration. He has published over 90 journal articles in various information systems, economics, and computer science journals. Over 45 of these articles are published in premier journals -- Management Science, ISR, and MIS Quarterly. He was awarded a prestigious NSF CAREER Award for his research on dynamic pricing mechanisms on the internet.

He is Emeritus Editor-in-Chief, Information Systems Research (ISR), INFORMS Fellow, AIS Fellow, and ISS Distinguished Fellow. He served multiple terms as an Associate Editor for Management Science and as Senior/Associate Editor for ISR. He guest edited several special issues in Management Science and ISR. He has been serving as Publisher of a premier academic journal MIS Quarterly since 2004. He served two terms as the Editor-in-Chief of the premier IS journal, Information Systems Research, from 2017-2022. His service contributions to the field were recognized with INFORMS ISS President’s Service Award in 2021. He has been an engaged scholar working closely with external stakeholders for both his research and for creating experiential learning environments for students. His research has been recognized for its impact and significance on practice through INFORMS Design Science Award three times (2001, 2012, and 2021), AIS Impact Award in 2020, and INFORMS ISS Practical Impacts Award in 2021. He was chosen as INFORMS ISS Distinguished Fellow in 2014 and as AIS Fellow in 2016. He was awarded lifetime achievement award, LEO Award, by AIS in 2021. He was chosen as INFORMS Fellow for lifetime achievement and contributions to Analytics Research and Practice in 2024. He teaches courses in the areas of Human-AI collaboration, computer networking, electronic commerce, decision support, IT infrastructure, and computer programming at the undergraduate, MBA and Ph.D. levels.

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