Dokyun (DK) Lee - Boston University Questrom

?Dokyun (DK) Lee is a Kelli Questrom Chair Associate Professor of Information Systems and Computing & Data Sciences at Boston University. His research examines the development, deployment, and impact of artificial intelligence in business and society, with particular emphasis on generative AI, large language models, and unstructured data.

His work studies how AI systems affect firm behavior, consumer behavior, market outcomes, and broader societal consequences, including regulation and governance. This includes empirical and causal analysis of AI reliability, human–AI interaction, and the economic implications of algorithmic systems, with attention to the limitations, failure modes, and unintended consequences that arise when AI technologies are deployed in real-world organizational and legal contexts.
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?He is the Principal Investigator of the Business Insights through Text (BIT) Lab (www.dkBITLAB.com) and the lead of the Boston University Digital Business Institute Generative AI Lab, where he conducts interdisciplinary research integrating AI, economics, and information systems.

Area of Expertise and Research?
– Generative AI in Business and Society: Economic and societal impact, organizational use, evaluation, governance, and regulatory implications
– Economics of Unstructured Data: Content extraction, value measurement, monetization, and engineering.
– AI Reliability and Validity: Behavioral consistency, robustness, and limits of AI systems.
– Unintended Consequences of AI: Market impacts, societal and regulatory risk
– Customized and Enterprise Human-AI Systems: Design, assessment, and improvements.
His research is applied across digital consumer management, AI regulation, platform and market design, competition, advertising, human–AI collaboration, innovation, and creativity.

DK has published in leading peer-reviewed journals, including Management Science, Information Systems Research, MIS Quarterly, Proceedings of the National Academy of Sciences (PNAS), Science Advances, Nature Scientific Reports, and Journal of Marketing Research, as well as top artificial intelligence venues such as AAAI, AIES, WWW, and NeuRips Workshop.

DK’s work has received numerous scholarly distinctions, including ISR Best Paper Award (2020), AAAI Award (2021), AMA Don Lehmann Award (2024), Management Science Best Paper Award (2025), 6 finalist distinctions for the Management Science ISR and Marketing Division Best Paper Award, and 13 best-paper awards from prominent conferences (WISE, CIST, ICIS, INFORMS).

DK’s research has been supported by organizations including Adobe, Google, NVIDIA, McKinsey & Company, Bosch Institute, Marketing Science Institute, Net Institute, Prudential Foundation, and MassMutual. His work is frequently consulted in contexts involving AI system impact evaluation, economic impact assessment, regulatory analysis, and disputes concerning the design, deployment, or effects of generative AI technologies, with implications for firms, consumers, and society at large.

DK holds a Bachelor’s degree in Computer Science from Columbia University (Machine Learning Focus), a Master’s degree in Statistics (Master’s Thesis: Johnson-Lindenstrauss Lemma and its Effect on Supervised Learning) from Yale University, and a Ph.D. from the Operation, Information, and Decisions department of the Wharton School (Thesis: Three Essays in Big Data Consumer Analytics in E-Commerce).
Before academia, DK worked at 4 tech start-ups and Blackrock as a quantitative software engineer and at Thomson Reuters as an ML contractor building a natural language processing engine for financial data.

Please contact for Academic-Industry research collaboration, consulting, and speaking inquiries.

Education

Phd, The Wharton School, 2015

MA, Yale University, 2010

BA, Columbia University, 2009

Selected Research Presentations

Lee, D. Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina, Arizona State University, Arizona State University, 2025

Lee, D. Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina, New York University, New York University, 2025

Lee, D. Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina, Conference on Artificial Intelligence, Machine Learning, and Business Analytics, Yale University, 2024

Lee, D. Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina, Harvard Business School, Harvard Business School, 2024

Lee, D. Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina, Wharton 2nd Annual Business & Generative AI Workshop, San Francisco, 2024

Lee, D. Generative AI, Human Creativity, and Art, University of Hyderabad, 2024

Lee, D. Generative AI, Human Creativity, and Art, Tel Aviv Unviversity, Tel Aviv Unviversity, 2024

Lee, D. Generative AI, Human Creativity, and Art, University of Arizona, University of Arizona, 2024

Lee, D. Generative AI, Human Creativity, and Art, University of Florida, University of Florida, 2024

Lee, D. Generative AI, Human Creativity, and Art, CCNY Baruch College, 2024

Lee, D. Generative AI, Human Creativity, and Art, Purdue University, Purdue University, 2024

Lee, D. Generative AI, Human Creativity, and Art, Case Western University, 2024

Srinivasan, S. , Jing, C. , Lee, D. , Fournier, S. Do or Do Not, There Is No Try: Firm Response to Sociopolitical Risk Events, Indian Institute of Management Ahmedabad, 2023

Lee, D. Generative AI, Human Creativity, and Art, MIT, 2023

Jing, C. , Lee, D. , Srinivasan, S. , Fournier, S. Do or Do Not, There Is No Try: Firm Response to Sociopolitical Risk Events, BU IMAP, 2023

Lee, D. Generative AI, Human Creativity, and Art, University of Tennessee, Knoxville, 2023

Lee, D. Generative AI, Human Creativity, and Art, UT Dallas, 2023

Lee, D. Generative AI, Human Creativity, and Art, UW Milwaukee, 2023

Srinivasan, S. , Jing, C. , Lee, D. Firm Sociopolitical Performance Radar, ISMS Marketing Science Conference, Miami, FL, 2023

Srinivasan, S. , Jing, c. , Lee, d. , Fournier, s. Do or Do Not, There Is No Try: Firm Response to Sociopolitical Risk Events, EMAC virtual presentation, Odense Denmark, 2023

Lee, D. Digital Marketing Symposium: Relevance and Privacy- Leveraging Personalized Marketing for Responsible Growth, Questrom School, 2023

Lee, D. DBI + Susilo Institute Symposium: The Emerging Impact of AI on Consumer Engagement, Questrom School, 2023

Lee, D. University Invited Seminar: InnoVAE: Generative AI for Understanding Patents and Innovation, American University, 2023

Cheng, Z. , Jin, G. , Leccese, M. , Lee, D. , Wagman, L. Startup Buyout and Patentable Innovation: New Evidence from PatentsView, AEA, 2023

Publications

Lee, D., Anandasivam, G., Lee, D., Dongwook, S. (In Press). “Nudging Private Ryan: Mobile Micro-giving under Economic Incentives and Audience Effects”, MIS Quarterly

Manzoor, E., Chen, G., Lee, D., Smith, M. (In Press). “Influence via Ethos: On the Persuasive Power of Reputation in Deliberation Online”,

Manzoor, E., Chen, G., Lee, D., Smith, M. (In Press). “Influence via Ethos: On the Persuasive Power of Reputation in Deliberation Online”, Management Science

Zhang, S., Lee, D., Singh, P., Srinivasan, K. (In Press). “How Much Is an Image Worth? Airbnb Property Demand Estimation Leveraging Large Scale Image Analytics”, SSRN Electronic Journal

Hsu, P., Lee, D., Tambe, P., Hsu, D. (In Press). “Deep Learning, Text, and Patent Valuation”, SSRN Electronic Journal

Ahn, D., Lee, D., Hosanagar, K. (In Press). “Interpretable Deep Learning Approach to Churn Management”, SSRN Electronic Journal

Burtch, G., He, Q., Hong, Y., Lee, D. (In Press). “Peer Awards Retain New Users and Encourage Exploitation in Users’ Production of Creative UGC”, SSRN Electronic Journal

Lee, D., Hosanagar, K. (In Press). “How Do Product Attributes Moderate the Impact of Recommender Systems?”, SSRN Electronic Journal

Gao, Y., Lee, D., Burtch, G., Fazelpour, S. (2025). “Take caution in using LLMs as human surrogates.”, Proc Natl Acad Sci U S A, 122 (24), e2501660122-

Gao, Y., Lee, D., Burtch, G., Fazelpour, S. (2025). “Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina”, Proceedings of the National Academy of Sciences of the United States of America

Zhou, E., Lee, D., Gu, B. (2025). “Who expands the human creative frontier with generative AI: Hive minds or masterminds?”, SCIENCE ADVANCES, 11 (36)

Lee, D., Cheng, Z., Mao, C., Manzoor, E. (2024). “Guided Diverse Concept Miner (GDCM): Uncovering Relevant Constructs for Managerial Insights from Text”, Information Systems Research

Burtch, G., Lee, D., Chen, Z. (2024). “The consequences of generative AI for online knowledge communities”, Sci Rep, 14 (1), 10413-10413

Zhou, E., Lee, D. (2024). “Generative artificial intelligence, human creativity, and art.”, PNAS Nexus, 3 (3), pgae052-

Burtch, D., Lee, d., Chen, z. (2024). “Generative AI Degrades Online Communities”, Communications of the ACM, 67 (3), 40-42

Shi, Z., Liu, X., Lee, D., Srinivasan, K. (2023). “How Do Fast-Fashion Copycats Affect the Popularity of Premium Brands? Evidence from Social Media”, Journal of Marketing Research, 60 (6), 1027-1051

Hosanagar, K., Lee, D. (2023). “AI in Personalized Product Recommendations”, Management and Business Review

Lazar, M., Mateja, D., Lifshitz-Assaf, H., Yoo, Y., Ding, M., Nickerson, J., Wolfson, B., Cheng, Z., Lee, D., Tambe, P., Cao, X. (2023). “Innovation in the Digital Age: Expanding the Boundaries of the Creative Process with Generative AI”, Academy of Management Proceedings, 2023 (1)

Zhaoqi, C., Jin, G., Leccese, M., Lee, D., Wagman, L. (2023). “M&A and Innovation: A New Classification of Patents”, American Economic Association. Papers and Proceedings, 113 288-293

Burtch, G., He, Q., Hong, Y., Lee, D. (2022). “How Do Peer Awards Motivate Creative Content? Experimental Evidence from Reddit”, Management Science, 68 (5), 3488-3506

Zhang, S., Lee, D., Singh, P., Srinivasan, K. (2021). “What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features”, Management Science

Zhang, S., Lee, D., Singh, P., Mukhopadhyay, T. (2021). “EXPRESS: Demand Interactions in Sharing Economies: Evidence from a Natural Experiment Involving Airbnb and Uber/Lyft”, Journal of Marketing Research 002224372110621-002224372110621

Wang, W., Zhao, H., Lee, D., Chen, G. (2021). “Machine Learning for Consumers and Markets”, Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining 4165-4166

Lee, D., Hosanagar, K. (2021). “How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?”, Management Science, 67 (1), 524-546

Proserpio, D., Hauser, J., Liu, X., Amano, T., Burnap, A., Guo, T., Lee, D., Lewis, R., Misra, K., Schwarz, E., Timoshenko, A., Xu, L., Yoganarasimhan, H. (2020). “Soul and machine (learning)”, Marketing Letters, 31 (4), 393-404

Liu, X., Lee, D., Srinivasan, K. (2019). “Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning”, Journal of Marketing Research, 56 (6), 918-943

Lee, D., Hosanagar, K. (2019). “How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment”, Information Systems Research, 30 (1), 239-259

Lee, D., Hosanagar, K., Nair, H. (2018). “Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook”, Management Science, 64 (11), 5105-5131

Lee, D., Gopal, A., Lee, D. (2018). “Micro-Giving: On the Use of Mobile Devices and Monetary Subsidies in Charitable Giving”, Academy of Management Proceedings, 2018 (1), 15011-15011

Lee, D., Hosanagar, K. (2016). “When do Recommender Systems Work the Best?”, Proceedings of the 25th International Conference on World Wide Web 85-97

Hosanagar, K., Fleder, D., Lee, D., Buja, A. (2014). “Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation”, Management Science, 60 (4), 805-823