GenAI

The aim of the GenAI Initiative is to promote research and an open flow of ideas on how to safely and responsibly shape our future. Our focus will be on investigating the impact and responsible use of Generative AI in firms, their operations, industries, markets, and societies. We firmly believe that business leaders are responsible for transforming scientific discoveries and engineering innovations into practical products and services that improve the quality of human life and well-being.

GenAI Faculty Fellows

Research Updates

Gordon Burtch:

The impact of abortion restrictions on American mental health, Michael R. Anderson, Gordon Burtch, Brad N. Greenwood, Science Advances, 3, Jul 2024, Vol 10, Issue 27

An Empirical Examination of the Antecedents and Consequences of Contribution Patterns in Crowd-Funded Markets, Bapna, Sofia, Burtch Gordon, INFORMS, Vol. 24, No. 3, September 2013, pp. 499–519

Do digital technologies reduce racially biased reporting? Evidence from NYPD administrative data, Jeremy Watson, Gordon Burtch, Brad N. Greenwood, Proceedings of the National Academy of Sciences, June 3, 2024

Andrei Hagiu:
Will That Marketplace Succeed? (with Julian Wright) Harvard Business Review 102(4), 94-103, July-August 2024

Turn Generative AI from an Existential Threat into a Competitive Advantage
(with Scott Cook and Julian Wright) Harvard Business Review 102(1), 118-125, January-February 2024

Marketplace leakage
(with Julian Wright) Management Science 70(3), 1529-1553, March 2024

When Data Creates a Competitive Advantage (and When it Doesn’t)
Andrei Hagiu Harvard Business Review, August 2021

Don’t let Platforms Commoditize Your Business Harvard Business Review, June 2021

Dokyun Lee:

Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina, Yuan Gao, Gordon Burtch, Dokyun Lee, Sina Fazelpour, ARZIV, October 2024

Guided diverse concept miner (GDCM): Uncovering relevant constructs for managerial insights from text, Dokyun Lee, Zhaoqi Cheng, Chengfeng Mao, Emaad Manzoor, INFORMS, May 2024

The consequences of generative AI for online knowledge communities, Gordon Burtch, Dokyun Lee, Zhichen Chen, Scientific Reports, May 2024

Ben Lubin:
Designing core-selecting payment rules: A computational search approach, Benedikt Bünz, Benjamin Lubin, Sven Seuken, December 2022

iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding, Manuel Beyeler, Gianluca Brero, Benjamin Lubin, Sven Seuken, 22nd ACM Conference on Economics and Computation, July 2021

InnoVAE: Generative AI for Understanding Patents and Innovation

Zhaoqi “ZQ” Cheng, Boston University – Questrom School of Business
Dokyun “DK” Lee, Boston University – Questrom School of Business
Sonny Tambe, Wharton School, U. Pennsylvania

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3868599

A lack of interpretability limits the use of common unsupervised learning techniques (e.g., PCA, t-SNE) in contexts where they are meant to augment managerial decision-making. We develop a generative deep learning model based on a Variational AutoEncoding Large Language Model (“InnoVAE”) that converts unstructured patent text into an interpretable, spatial representation of innovation (“Innovation Space”). After validating the internal consistency of the model, we apply it to approximately 0.5 million AI patents to show that our approach can be used to construct economically interpretable measures—at scale—that characterize a firm’s IP portfolio from the text of its patents, such as whether a patent is a breakthrough innovation, the volume of intellectual property enclosed by a portfolio of patents, or the density of patents at a point in Innovation Space. We show that for explaining innovation outcomes, these interpretable, engineered features have explanatory power that augments and often surpasses the structured patent variables that have informed the very large and influential literature on patents and innovation.

Our findings illustrate the potential of using generative methods on unstructured data to guide managerial decision-making. The same methodology can be applied to a wide variety of different business objects such as embedding businesses into business strategy space, jobs into skills space, assets into risk space, products into attribute space, and more. This embedding space enables further combinational synthesis as well as a high-resolution exploration into the multi-modal business entity.

Generative AI, Human Creativity, and Art

Eric Zhou
Dokyun Lee

Artificial intelligence (AI) has demonstrated its ability to produce outputs that society traditionally considers “creative”. One such system is text-to-image generative AI (e.g., MidJourney, Stable Diffusion), which automates humans’ execution to generate high-quality digital artworks. To assess its impact, we collected a dataset of over 4 million artworks from more than 50,000 unique users on a prominent art-sharing platform, including over 5,800 AI adopters. Our research shows that text-to-image AI substantially enhances human creative productivity by 25% and doubles the perceived worth of the artifacts, gauged by favorites per view. Interestingly, although the peak novelty of creations rises over time, the average novelty diminishes, implying an expanding realm of creative possibilities but with some inefficiencies. Furthermore, we provide additional insights into the specific circumstances and artist-level differences for which generative AI enhances creativity and productivity. The results identify clear winners and losers in human-AI collaborative creation and offer guidance on how best to leverage this technology to enhance human creativity and productivity.

Figure 1 Innovation Space

Figure 2 Art Sharing Platform

Figure 3 Human-AI Generative Synesthesia

Figure 4 Human-AI Collaborative Art

The Consequences of Generative AI for UGC and Online Community Engagement

Gordon Burtch, Boston University – Questrom School of Business
Dokyun Lee, Boston University – Questrom School of Business
Zhichen Chen, Boston University – Questrom School of Business

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4521754

Generative AI technologies like ChatGPT are transforming how people create, share, and consume content in various domains. Though these technologies have the potential to democratize information access and streamline content production, they may also have unintended consequences for the health and sustainability of online knowledge communities, particularly those that focus on information exchange and lack robust mechanisms of social attachment. We provide initial evidence of this, examining the impact that ChatGPT has had on user activity at StackOverflow, contrasting with user activity at Reddit. We analyze a large sample of data for a period spanning October 2021 and March 2023, capturing content posted to Stack Overflow and Reddit related to the most popular StackOverflow topics. We find that ChatGPT has led to large, significant declines in StackOverflow questions related to these topics, with larger effects manifesting for topics where ChatGPT is more likely to excel, based on the volume of public, online data that would have been available for training. By contrast, considering Reddit sub-communities focused on the same topics, we find no evidence that ChatGPT has had any effect, suggesting the importance of social attachment in online knowledge communities for community survival. Finally, we consider potential shifts in the quality of answers provided at StackOverflow, considering users’ potential reliance on ChatGPT when providing answers, and potential declines in collective expertise as users depart the community. We find that average answer quality has declined significantly, primarily due to user exit. We discuss implications for the management of online knowledge communities.

Figure 5 AnswerBot