Jennifer M. Logg, Ph.D., is an Assistant Professor of Management at Georgetown University's McDonough School of Business. Prior to joining Georgetown, she was a Post-Doctoral Fellow at Harvard University. Dr. Logg received her Ph.D. from the University of California, Berkeley’s Haas School of Business.
Her research examines why people fail to view themselves and their work realistically. It focuses on how individuals can assess themselves and the world more accurately by using advice and feedback produced by algorithms (scripts for mathematical calculations).
She calls her primary line of research Theory of Machine. It uses a psychological perspective to examine how people respond to the increasing prevalence of information produced by algorithms. Broadly, this work examines how people expect algorithmic and human judgment to differ. Read more in her book chapter, The Psychology of Big Data: Developing a “Theory of Machine” to Examine Perceptions of Algorithms.
The F.B.I., U.S. Senate, Air Force, and Navy have invited her to speak on the topic of algorithms with decision-makers. During her Ph.D., she was a collaborator on the Good Judgment Project, funded by IARPA, Intelligence of Advanced Research Projects Activity, the US intelligence community’s equivalent of DARPA. Currently, she is a Faculty Fellow at Georgetown University's AI, Analytics, and the Future of Work Initiative. She is also also a member of the "Theory of AI Practice" working group, funded by the Rockefeller Foundation through Stanford University's Center for Advanced Study in the Behavioral Sciences.
Her paper, "Algorithm Appreciation," ranked #1 in 2021 on the list of "Most Cited Organizational Behavior and Human Decision Processes Articles Since 2018."
She received the 2019 Early Career Award for the paper "Is overconfidence a motivated bias?" as judged by the Journal of Experimental Psychology's editors (from five sections).
Poets & Quants listed her as one of the Top 50 Undergraduate Business Professors and she received the Georgetown Career Champion Award (student nominated) in 2022.
Personal Website
Apply to Logg Lab here (accepting applications for fall 2025).