Browse 6 peer-reviewed papers from University Of Chicago spanning Behavioral Economics, Computation and Language (2020–2025). Research powered by Prolific's high-quality participant data.
This page lists 6 peer-reviewed papers from researchers at University Of Chicago in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: P. Schoenegger, F. Salvi, J. Liu, X. Nan, R. Debnath, B. Fasolo, E. Leivada, G. Recchia, F. Günther, A. Zarifhonarvar, J. Kwon, Z. Ul Islam, M. Dehnert, D. Y. H. Lee, M. G. Reinecke, D. G. Kamper, M. Kobaş, A. Sandford, J. Kgomo, L. Hewitt, S. Kapoor, K. Oktar, E. E. Kucuk, B. Feng, C. R. Jones, I. Gainsburg, S. Olschewski, N. Heinzelmann, F. Cruz, B. M. Tappin, T. Ma, P. S. Park, R. Onyonka, A. Hjorth, P. Slattery, Q. Zeng, L. Finke, I. Grossmann, A. Salatiello, E. Karger
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: London School of Economics and Political Science, University of Cambridge, University College London, Massachusetts Institute of Technology, University of Oxford, Modulo Research, Stanford University, Federal Reserve Bank of Chicago, ETH Zürich, University of Johannesburg
Research Area: Computation and Language
Discipline: Social Science, Artificial Intelligence
This paper compares a frontier LLM (Claude Sonnet 3.5) against incentivized human persuaders in a conversational quiz setting, finding that the AI's persuasion capabilities surpass those of humans with real-money bonuses tied to performance.
Citations: 16
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Authors: AYJ Ha, J Passananti, R Bhaskar, S Shan
Year: 2024
Published in: Proceedings of the ..., 2024 - dl.acm.org
Institution: University of California Santa Barbara, The University of Chicago, Institute of Education, University College London
Research Area: Human-Computer Interaction (HCI), Generative AI, Digital Forensics
Discipline: Human-Computer Interaction (HCI), Generative AI, Digital Forensics
The paper investigates the effectiveness of different approaches, including both human and automated detectors, in distinguishing human art from AI-generated images, finding that a combination of methods offers the best performance despite persistent weaknesses.
Methods: Comparison of human art across 7 styles with AI-generated images from 5 generative models, assessed using 5 automated detectors and 3 human groups (crowdworkers, professional artists, expert artists).
Key Findings: Detection accuracy and robustness of human and automated methods in identifying AI-generated images under benign and adversarial conditions.
DOI: 10.1145/3658644.3670306
Citations: 52
Sample Size: 3993
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Authors: Y Ling, A Kale, A Imas
Year: 2024
Published in: Available at SSRN 5464215, 2025 - papers.ssrn.com
Institution: University of Chicago
Research Area: Behavioral Economics, AI Adoption, AI Disclosure
Discipline: Behavioral Economics
Citations: 7
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Authors: P Schoenegger, P Park, E Karger, P Tetlock
Year: 2024
Published in: ArXiv
Institution: Federal Reserve Bank of Chicago, London School of Economics and Political Science, Massachusetts Institute of Technology, University of Pennsylvania
Research Area: LLM Assistants, Human Forecasting, Predictive Modeling, AI-Augmented Decision Making, LLM
Discipline: Artificial Intelligence, Behavioral Science
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Authors: L Bursztyn, A Imas, R Jiménez-Durán, A Leonard
Year: 2022
Published in: 2025 - nber.org
Institution: University of Chicago, National Bureau of Economic Research, Booth School of Business, Bocconi University, Stanford University
Research Area: Behavioral Economics, Social Dynamics of Technology Adoption
Discipline: Behavioral Economics
Citations: 2
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Authors: J Hanson, M Wei, S Veys, M Kugler
Year: 2020
Published in: Proceedings of the ..., 2020 - dl.acm.org
Institution: University of Chicago, University of Washington
Research Area: Privacy, Crowdwork, Hyper-Personalization in Advertising
Discipline: Human-Computer Interaction (HCI)
DOI: https://doi.org/10.1145/3313831.3376415
Citations: 40