New Paper: A Simple Explanation Reconciles “Algorithm Aversion” and “Algorithm Appreciation”: Hypotheticals vs. Judgments
Book Chapter on Algorithms: The Psychology of Big Data: Developing a “Theory of Machine”
Book Chapter on Algorithms: The Psychology of Big Data: Developing a “Theory of Machine”
Why do people make inaccurate judgments?
When are people willing to use algorithms to improve their accuracy?
What is the optimal way to collaborate with GenAI?
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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 and received her Ph.D. from the University of California, Berkeley’s Haas School of Business.
Accuracy & Algorithms Logg's 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 from algorithms, including Generative AI (GenAI). She studies the psychology of algorithms in her primary line of research, Theory of Machine. It tests how people respond to algorithmic advice. Broadly, this work examines how people expect algorithmic and human judgment to differ. The Psychology of GenAI GenAI needs Psychology. Thus, her recent recent research takes a cost-benefit perspective to AI-human collaboration in order to examine both productivity and psychology. It tests how different strategies, GenAI Collaboration Strategies, impact both productivity and the psychological experience of work. The F.B.I., U.S. Senate, Air Force, and Navy have invited her to speak on the topic of algorithms and predictive accuracy 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. She served on the faculty committee that created the Masters of Science in Business Analytics at Georgetown University's McDonough School of Business. She was 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. Currently, she is a Faculty Fellow at Georgetown University's AI, Analytics, and the Future of Work Initiative. |
Working Papers
The Cost of Generative AI Collaboration Strategies to the Psychological Experience of Work
Generative AI (GenAI) is transforming the way people work by increasing productivity and changing workflows. Yet, little is known about the optimal way to use it in order to balance both productivity and the psychological experience of work (i.e., control, responsibility, and creativity). We examined different strategies, which we call GenAI collaboration strategies, and their associated productivity benefits and psychological costs. In two pre-registered experiments (N = 1,299), we examined two common uses of GenAI, using it to draft versus edit. First, using GenAI, relative to not using it, improved productivity but degraded control, responsibility, and creativity. Second, we identified the optimal GenAI collaboration strategy: using GenAI to edit. Productivity was similar between collaboration strategies, but using GenAI to draft further hurt people's psychological experience of work. Yet, the majority of people chose the suboptimal strategy, including a sample of teachers creating lesson plans for a class. GenAI is a powerful tool to increase productivity, but it is not without costs. Thus, it is not enough for organizations to provide employees with GenAI; how people use GenAI has implications for their psychology.
A Simple Explanation Reconciles “Algorithm Aversion” and “Algorithm Appreciation”: Hypotheticals vs. Judgments
(Top 10 SSRN Download List in 3 Topic Categories within 1 month)
We propose a simple explanation to reconcile research documenting algorithm aversion with research documenting algorithm appreciation: elicitation methods. We compare self-reports and judgments. When making actual judgments, people consistently utilize algorithmic advice more than human advice. In contrast, hypotheticals produce unstable preferences; people sometimes report indifference and sometimes report preferring human judgment. Moreover, people fail to correctly anticipate behavior, utilizing algorithmic advice more than they anticipate. A slight change in the framing of a hypothetical task additionally moderates algorithm aversion. Stated preferences about algorithms are less stable than actual judgments, suggesting that algorithm aversion may be less stable than previous research leads us to believe.
Risk Creep: Learning Under High Uncertainty is Associated with a Creeping Tolerance for Risk
(Top 10 SSRN Download List in 5 Topic Categories within 2 months)
According to The Federal Reserve, uncertainty has reached its “highest levels in decades” based on economic, political, and geopolitical data (Londono, Ma, & Wilson, 2025). But we know little about how people learn when uncertainty is two-fold, stemming from both environmental and social uncertainty. We call two-fold uncertainty, high uncertainty. While learning theories primarily study one type of learning in isolation, we measured two potential channels of learning simultaneously. We tracked perceptions and behavior at the height of uncertainty during the COVID-19 pandemic, post-lockdown and pre-vaccine. We measured whether perceptions and observations are associated with behaviors under high uncertainty, which we call minimal experiential learning and minimal signal learning. Importantly, both were associated with a gradual increase in precarious activity that put people at risk of negative consequences. We call this risk creep and suspect that it is a common by-product of learning under high uncertainty.
Hybrid Intelligence: A Paradigm for More Responsible Practice
Stanford's Center for Advanced Study in the Behavioral Sciences White Paper
Research Awards
Algorithm Appreciation
- Ranked #1 in 2021 as "Most Cited Organizational Behavior and Human Decision Processes Articles Since 2018"
- Top 10% of authors on SSRN (by new downloads) 2017-Present
Is Overconfidence a Motivated Bias?
- Early Career Award as judged by the Journal of Experimental Psychology's editors, from five sections (2019)
Teaching Awards
Top 50 Undergrad B-School Profs: Poets & Quants (2021)
Georgetown Career Champion: student nominated (2022)
The Cost of Generative AI Collaboration Strategies to the Psychological Experience of Work
Generative AI (GenAI) is transforming the way people work by increasing productivity and changing workflows. Yet, little is known about the optimal way to use it in order to balance both productivity and the psychological experience of work (i.e., control, responsibility, and creativity). We examined different strategies, which we call GenAI collaboration strategies, and their associated productivity benefits and psychological costs. In two pre-registered experiments (N = 1,299), we examined two common uses of GenAI, using it to draft versus edit. First, using GenAI, relative to not using it, improved productivity but degraded control, responsibility, and creativity. Second, we identified the optimal GenAI collaboration strategy: using GenAI to edit. Productivity was similar between collaboration strategies, but using GenAI to draft further hurt people's psychological experience of work. Yet, the majority of people chose the suboptimal strategy, including a sample of teachers creating lesson plans for a class. GenAI is a powerful tool to increase productivity, but it is not without costs. Thus, it is not enough for organizations to provide employees with GenAI; how people use GenAI has implications for their psychology.
A Simple Explanation Reconciles “Algorithm Aversion” and “Algorithm Appreciation”: Hypotheticals vs. Judgments
(Top 10 SSRN Download List in 3 Topic Categories within 1 month)
We propose a simple explanation to reconcile research documenting algorithm aversion with research documenting algorithm appreciation: elicitation methods. We compare self-reports and judgments. When making actual judgments, people consistently utilize algorithmic advice more than human advice. In contrast, hypotheticals produce unstable preferences; people sometimes report indifference and sometimes report preferring human judgment. Moreover, people fail to correctly anticipate behavior, utilizing algorithmic advice more than they anticipate. A slight change in the framing of a hypothetical task additionally moderates algorithm aversion. Stated preferences about algorithms are less stable than actual judgments, suggesting that algorithm aversion may be less stable than previous research leads us to believe.
Risk Creep: Learning Under High Uncertainty is Associated with a Creeping Tolerance for Risk
(Top 10 SSRN Download List in 5 Topic Categories within 2 months)
According to The Federal Reserve, uncertainty has reached its “highest levels in decades” based on economic, political, and geopolitical data (Londono, Ma, & Wilson, 2025). But we know little about how people learn when uncertainty is two-fold, stemming from both environmental and social uncertainty. We call two-fold uncertainty, high uncertainty. While learning theories primarily study one type of learning in isolation, we measured two potential channels of learning simultaneously. We tracked perceptions and behavior at the height of uncertainty during the COVID-19 pandemic, post-lockdown and pre-vaccine. We measured whether perceptions and observations are associated with behaviors under high uncertainty, which we call minimal experiential learning and minimal signal learning. Importantly, both were associated with a gradual increase in precarious activity that put people at risk of negative consequences. We call this risk creep and suspect that it is a common by-product of learning under high uncertainty.
Hybrid Intelligence: A Paradigm for More Responsible Practice
Stanford's Center for Advanced Study in the Behavioral Sciences White Paper
Research Awards
Algorithm Appreciation
- Ranked #1 in 2021 as "Most Cited Organizational Behavior and Human Decision Processes Articles Since 2018"
- Top 10% of authors on SSRN (by new downloads) 2017-Present
Is Overconfidence a Motivated Bias?
- Early Career Award as judged by the Journal of Experimental Psychology's editors, from five sections (2019)
Teaching Awards
Top 50 Undergrad B-School Profs: Poets & Quants (2021)
Georgetown Career Champion: student nominated (2022)
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Aug. 8, 2019
Algorithms as Magnifying Glasses: Using Algorithms to Understand the Biases in Your Organization Harvard Business Review |
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Oct. 26, 2018
Stop Naming Your Algorithms: Do People Trust Algorithms More Than Companies Realize? Harvard Business Review |
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May 10, 2019
Harnessing the Wisdom of Crowds: How Asking Multiple People for Advice Can Backfire Harvard Business Review |
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Nov. 17, 2020
A Social Perspective of a Cognitive Bias: Overconfidence is Contagious Harvard Business Review |
Past Speaking Engagements
Click links for Talks, Papers, and Programs
Talk: Developing a "Theory of Machine"
Naval Applications of Machine Learning
NIWC Pacific Workshop
March 2024
Talk: A Simple Explanation Reconciles
“Algorithm Aversion” and “Algorithm Appreciation”:
Hypotheticals vs. Real Judgments
JDM Conference
San Francisco, CA
Nov 2023
Talk: Anticipated Preferences vs. Utilization of Algorithmic Advice
ACR Conference
Denver, CO
Oct 2022
Talk: Anticipated Preferences vs. Utilization of Algorithmic Advice
AOM Conference
Understanding the Future of Work
with Algorithms, AI, & Automation
Aug 6, 2022
Panel Webcast: Building Social Science into the Foundation of AI Practice
Invited Panelist
Produced by Stanford University's Center for Advanced Study in the Behavioral Sciences
June 14, 2022
Summary
Video
Podcast
Talk: Pre-Registration: Weighing Costs and Benefits for Researchers
Invited Speaker
HEC Montreal Research Day on Replication and Open Science
April 5, 2022
Talk: Developing a "Theory of Machine"
Harvard University
Lerner Lab
April 8, 2022
Talk: Algorithmic Hiring
APA Conference
Advancing Human-AI Communication and Interaction Symposium
Invited Speaker
May 2022
Invited Panelist
Workshop on AI For Behavior Change
Talk: Developing a "Theory of Machine"
Invited Talk
Max Planck Institute
May 4, 2021
Talk: www.youtube.com/watch?v=xyDR9kQ5YDk
Talk: Algorithmic Hiring
Psychology of Technology
Algorithms and Decision-Making Session
Oct. 26, 2020
Talk: https://www.psychoftech.org/rising-stars-data-blitz
Talk: Algorithmic Hiring
Academy of Management
Aug. 10, 2020
Session: Algorithmic Decision Making
Talk: Using Algorithms to Detect Bias
Invited Talk
DC United (MLS Soccer)
July 21, 2020
Talk: Using Algorithms to Detect Bias
Invited Talk
Naval Applications of Machine Learning
NIWC Pacific Workshop
Feb. 24, 2020
Invited Interview (Information Collection Stage)
U.S. Senate
Oct. 18, 2019
Consumer Online Privacy Rights Act (COPRA) Bill Introduced 11.26.19
Overview of Bill
Talk: Algorithm Appreciation
Invited Talk, Postponed
Harvard University
Science Based Business Initiative
Co-Sponsored by Economics of Science & Engineering and
Technology & Operations Management
Click links for Talks, Papers, and Programs
Talk: Developing a "Theory of Machine"
Naval Applications of Machine Learning
NIWC Pacific Workshop
March 2024
Talk: A Simple Explanation Reconciles
“Algorithm Aversion” and “Algorithm Appreciation”:
Hypotheticals vs. Real Judgments
JDM Conference
San Francisco, CA
Nov 2023
Talk: Anticipated Preferences vs. Utilization of Algorithmic Advice
ACR Conference
Denver, CO
Oct 2022
Talk: Anticipated Preferences vs. Utilization of Algorithmic Advice
AOM Conference
Understanding the Future of Work
with Algorithms, AI, & Automation
Aug 6, 2022
Panel Webcast: Building Social Science into the Foundation of AI Practice
Invited Panelist
Produced by Stanford University's Center for Advanced Study in the Behavioral Sciences
June 14, 2022
Summary
Video
Podcast
Talk: Pre-Registration: Weighing Costs and Benefits for Researchers
Invited Speaker
HEC Montreal Research Day on Replication and Open Science
April 5, 2022
Talk: Developing a "Theory of Machine"
Harvard University
Lerner Lab
April 8, 2022
Talk: Algorithmic Hiring
APA Conference
Advancing Human-AI Communication and Interaction Symposium
Invited Speaker
May 2022
Invited Panelist
Workshop on AI For Behavior Change
Talk: Developing a "Theory of Machine"
Invited Talk
Max Planck Institute
May 4, 2021
Talk: www.youtube.com/watch?v=xyDR9kQ5YDk
Talk: Algorithmic Hiring
Psychology of Technology
Algorithms and Decision-Making Session
Oct. 26, 2020
Talk: https://www.psychoftech.org/rising-stars-data-blitz
Talk: Algorithmic Hiring
Academy of Management
Aug. 10, 2020
Session: Algorithmic Decision Making
Talk: Using Algorithms to Detect Bias
Invited Talk
DC United (MLS Soccer)
July 21, 2020
Talk: Using Algorithms to Detect Bias
Invited Talk
Naval Applications of Machine Learning
NIWC Pacific Workshop
Feb. 24, 2020
Invited Interview (Information Collection Stage)
U.S. Senate
Oct. 18, 2019
Consumer Online Privacy Rights Act (COPRA) Bill Introduced 11.26.19
Overview of Bill
Talk: Algorithm Appreciation
Invited Talk, Postponed
Harvard University
Science Based Business Initiative
Co-Sponsored by Economics of Science & Engineering and
Technology & Operations Management