Manmohan Aseri Directory Page
Manmohan Aseri
Assistant Professor of Information Systems
Instructor, QUEST Honors Program
PhD, University of Texas at Dallas
Manmohan Aseri is an Assistant Professor of Decision, Operations & Information Technologies at Robert H. Smith School of Business - University of Maryland. He analyzes economic issues related to AI, such as algorithmic fairness, pricing of generative AI, and incentive issues in AI deployment. His work is published in Management Science and Information Systems Research and has won several best paper awards in prestigious conferences, such as Best Paper in CIST 2017, Best Student Paper in CIST 2021, and Best Paper Runner-up in CIST 2016. His paper titled “UnFair Machine Learning Algorithms” was a finalist for the Best Paper in Management Science in Information Systems Track in 2023. He has served as an associate editor for ITM journal and ICIS 2021, 2023. Manmohan received the Management Science Distinguished Service Award for Reviewer in 2023.
Manmohan obtained a Bachelor of Technology in Electrical Engineering from IIT Kanpur in India and worked as a programmer for five years before coming to the US for his Ph.D. He earned his Ph.D. in information systems from UT Dallas. His Ph.D. dissertation received the Nunamaker – Chen Dissertation Runner-Up Award from INFORMS and the Best Dissertation Award from UT Dallas. Before the University of Maryland, he was an assistant professor at the University of Pittsburgh for four years and a visiting assistant professor at CMU for two years.
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Research
Academic Publications
Backfiring AI? AI Deployment in Workplace
Management Science
AI in the workplace has the potential to change the competitive dynamics among employees. The AI system can learn from high-performing employees and make that knowledge available to others. In a competitive environment, this can disincentivize high-performing employees and ultimately backfire, leading to a decline in overall firm productivity. Our results suggest that some ostensibly simple solutions, such as guaranteeing or increasing wages for adversely affected employees, may not effectively solve the problem, and firms would have to judiciously choose optimal AI efficacy levels to achieve better outcomes.
Di Yuan (Assistant Professor, Auburn University), Manmohan Aseri (Assistant professor, University of Maryland), Narayan Ramasubbu (Professor, University of Pittsburgh)