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      • Faculty Publications  (126)

      Generative AiRemove Generative Ai →

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      • February 2024 (Revised July 2024)
      • Case

      Taffi: Entrepreneurship in Saudi Arabia

      By: Paul A. Gompers and Fares Khrais
      Taffi was a tech-enabled fashion styling startup founded by Shahad Geoffrey in Saudi Arabia in 2020. Within three years of operating, Geoffrey had pivoted the business multiple times. In 2023, Geoffrey was attempting the business’s most ambitious pivot yet, shifting... View Details
      Keywords: Business Startups; Disruption; Entrepreneurship; Venture Capital; Investment; Growth and Development Strategy; Business Strategy; AI and Machine Learning; Fashion Industry; Technology Industry; Saudi Arabia; Arabian Peninsula
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      Gompers, Paul A., and Fares Khrais. "Taffi: Entrepreneurship in Saudi Arabia." Harvard Business School Case 224-052, February 2024. (Revised July 2024.)
      • February 2024
      • Teaching Note

      TimeCredit

      By: Emanuele Colonnelli, Raymond Kluender and Shai Benjamin Bernstein
      Teaching Note for HBS Case No. 824-139. TimeCredit is an artificial intelligence (AI) startup that is developing large language models (LLMs) to generate accounting memos. The case follows Ndonga Sagnia, a Gambian Harvard Business School MBA student with an accounting... View Details
      Keywords: Accounting; Business Startups; Entrepreneurship; Financing and Loans; AI and Machine Learning; Entrepreneurial Finance; Identity; Partners and Partnerships; Technology Industry
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      Colonnelli, Emanuele, Raymond Kluender, and Shai Benjamin Bernstein. "TimeCredit." Harvard Business School Teaching Note 824-171, February 2024.
      • February 2024 (Revised September 2024)
      • Case

      TimeCredit

      By: Emanuele Colonnelli, Raymond Kluender and Shai Benjamin Bernstein
      TimeCredit is an artificial intelligence (AI) startup that is developing large language models (LLMs) to generate accounting memos. The case follows Ndonga Sagnia, a Gambian Harvard Business School MBA student with an accounting background, as she decides how much... View Details
      Keywords: Accounting; Business Startups; Entrepreneurship; Financing and Loans; AI and Machine Learning; Entrepreneurial Finance; Identity; Technology Industry
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      Colonnelli, Emanuele, Raymond Kluender, and Shai Benjamin Bernstein. "TimeCredit." Harvard Business School Case 824-139, February 2024. (Revised September 2024.)
      • January 2024
      • Background Note

      Note on Generative AI for Business Students

      By: Andrew Rashbass, Ramon Casadesus-Masanell and Jordan Mitchell
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      Rashbass, Andrew, Ramon Casadesus-Masanell, and Jordan Mitchell. "Note on Generative AI for Business Students." Harvard Business School Background Note 724-411, January 2024.
      • January 2024
      • Case

      The Financial Times (FT) and Generative AI

      By: Andrew Rashbass, Ramon Casadesus-Masanell and Jordan Mitchell
      In September 2023, John Ridding, CEO of the Financial Times, was considering the possible impact of Generative AI on the industry and his business. Having navigated successfully the seismic shift from print to digital, and reporting record results, the company... View Details
      Keywords: AI and Machine Learning; Technology Adoption; Change Management; Journalism and News Industry
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      Rashbass, Andrew, Ramon Casadesus-Masanell, and Jordan Mitchell. "The Financial Times (FT) and Generative AI." Harvard Business School Case 724-410, January 2024.
      • 2024
      • Conference Paper

      Quantifying Uncertainty in Natural Language Explanations of Large Language Models

      By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
      Large Language Models (LLMs) are increasingly used as powerful tools for several high-stakes natural language processing (NLP) applications. Recent prompting works claim to elicit intermediate reasoning steps and key tokens that serve as proxy explanations for LLM... View Details
      Keywords: Large Language Model; AI and Machine Learning
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      Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 2024.
      • December 2023 (Revised November 2024)
      • Case

      Generative AI and the Future of Work

      By: Christopher Stanton, Matt Higgins, Shira Aronson and Meg Shriber
      Generative AI seemed poised to reshape the world of work, including the higher-wage, white-collar jobs typically pursued by MBA graduates. Informed by the latest research, this case explores generative AI's potential impacts on work, productivity, value creation, and... View Details
      Keywords: AI; Future Of Work; Labor Market; AI and Machine Learning; Labor; Value Creation; Performance Productivity; Technology Industry; United States
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      Stanton, Christopher, Matt Higgins, Shira Aronson, and Meg Shriber. "Generative AI and the Future of Work." Harvard Business School Case 824-130, December 2023. (Revised November 2024.)
      • December 2023 (Revised August 2024)
      • Case

      Monsters in the Machine? Tackling the Challenge of Responsible AI

      By: Paul M. Healy and Debora L. Spar
      In November of 2022, the small tech company OpenAI released ChatGPT, an artificial intelligence chatbot which quickly captured the public’s imagination—becoming the world’s fastest-growing consumer application within months of its release. Though observers from across... View Details
      Keywords: Technological Innovation; AI and Machine Learning; Ethics; Governing Rules, Regulations, and Reforms; Technology Adoption; Corporate Social Responsibility and Impact; Technology Industry; United States; European Union; China
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      Healy, Paul M., and Debora L. Spar. "Monsters in the Machine? Tackling the Challenge of Responsible AI." Harvard Business School Case 324-062, December 2023. (Revised August 2024.)
      • 2023
      • Article

      Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset

      By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
      Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
      Keywords: Large Language Model; AI and Machine Learning; Analytics and Data Science; Health Industry
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      Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
      • 2023
      • Book

      Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow

      By: Ken Huang, Yang Wang, Feng Zhu, Xi Chen and Chunxiao Xing
      This book explores the transformative potential of ChatGPT, Web3, and their impact on productivity and various industries. It delves into Generative AI (GenAI) and its representative platform ChatGPT, their synergy with Web3, and how they can revolutionize business... View Details
      Keywords: Generative Ai; AI and Machine Learning; Ethics; Technology Adoption
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      Huang, Ken, Yang Wang, Feng Zhu, Xi Chen, and Chunxiao Xing, eds. Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow. Springer, 2023.
      • 2024
      • Working Paper

      The Uneven Impact of Generative AI on Entrepreneurial Performance

      By: Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz and Rembrand Koning
      Scalable and low-cost AI assistance has the potential to improve firm decision-making and economic performance. However, running a business involves a myriad of open-ended problems, making it difficult to know whether recent AI advances can help business owners make... View Details
      Keywords: AI and Machine Learning; Performance Improvement; Small Business; Decision Choices and Conditions; Kenya
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      Otis, Nicholas G., Rowan Clarke, Solène Delecourt, David Holtz, and Rembrand Koning. "The Uneven Impact of Generative AI on Entrepreneurial Performance." Harvard Business School Working Paper, No. 24-042, December 2023.
      • 2023
      • Article

      Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness

      By: Suraj Srinivas, Sebastian Bordt and Himabindu Lakkaraju
      One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite only being trained for classification, PAGs cause... View Details
      Keywords: AI and Machine Learning; Mathematical Methods
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      Srinivas, Suraj, Sebastian Bordt, and Himabindu Lakkaraju. "Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness." Advances in Neural Information Processing Systems (NeurIPS) (2023).
      • November 2023
      • Case

      Open Source Machine Learning at Google

      By: Shane Greenstein, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue and James Barnett
      Set in early 2023, the case exposes students to the challenges of managing open source software at Google. The case focuses on the challenges for Alex Spinelli, Vice President of Product Management for Core Machine Learning. He must set priorities for Google’s efforts... View Details
      Keywords: Decision Choices and Conditions; Technological Innovation; Open Source Distribution; Strategy; AI and Machine Learning; Applications and Software; Technology Industry; United States
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      Greenstein, Shane, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue, and James Barnett. "Open Source Machine Learning at Google." Harvard Business School Case 624-015, November 2023.
      • November 2023
      • Case

      Copilot(s): Generative AI at Microsoft and GitHub

      By: Frank Nagle, Shane Greenstein, Maria P. Roche, Nataliya Langburd Wright and Sarah Mehta
      This case tells the story of Microsoft’s 2018 acquisition of GitHub and the subsequent launch of GitHub Copilot, a tool that uses generative artificial intelligence to suggest snippets of code to software developers in real time. Set in late 2021, when Copilot was... View Details
      Keywords: Business Ventures; Strategy; AI and Machine Learning; Applications and Software; Product Launch; Information Technology Industry; Technology Industry; Web Services Industry; United States; California
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      Nagle, Frank, Shane Greenstein, Maria P. Roche, Nataliya Langburd Wright, and Sarah Mehta. "Copilot(s): Generative AI at Microsoft and GitHub." Harvard Business School Case 624-010, November 2023.
      • November 2023 (Revised April 2024)
      • Case

      Khanmigo: Revolutionizing Learning with GenAI

      By: William A. Sahlman, Allison M. Ciechanover and Emily Grandjean
      Already a leader in the edtech space since its 2008 launch, Khan Academy was now one of the first edtech organizations to embrace generative artificial intelligence ("genAI"). In March 2023, Khan Academy began beta testing Khanmigo, a genAI “guide” and tutor built with... View Details
      Keywords: Technology Adoption; Leading Change; Entrepreneurship; Risk and Uncertainty; Education; AI and Machine Learning; Corporate Social Responsibility and Impact; Education Industry; Technology Industry; United States; San Francisco
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      Sahlman, William A., Allison M. Ciechanover, and Emily Grandjean. "Khanmigo: Revolutionizing Learning with GenAI." Harvard Business School Case 824-059, November 2023. (Revised April 2024.)
      • November–December 2023
      • Article

      Keep Your AI Projects on Track

      By: Iavor Bojinov
      AI—and especially its newest star, generative AI—is today a central theme in corporate boardrooms, leadership discussions, and casual exchanges among employees eager to supercharge their productivity. Sadly, beneath the aspirational headlines and tantalizing potential... View Details
      Keywords: Generative Models; AI and Machine Learning; Success; Failure; Product Development; Technology Adoption
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      Bojinov, Iavor. "Keep Your AI Projects on Track." Harvard Business Review 101, no. 6 (November–December 2023): 53–59.
      • October 2023 (Revised February 2024)
      • Case

      Loris

      By: Shunyuan Zhang, Das Narayandas, Stacy Straaberg and David Lane
      In December 2022, Loris’s executive team considered their go-to-market strategy. Loris was an artificial intelligence (AI) software startup for the customer service industry with two products on the market: 1) Agent Assist which provided customer service agents (CSAs)... View Details
      Keywords: Decisions; Growth and Development Strategy; Product Launch; Product Positioning; Business Strategy; Competitive Strategy; Business Startups; AI and Machine Learning; Applications and Software; Marketing Strategy; Sales; Technology Industry; United States
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      Zhang, Shunyuan, Das Narayandas, Stacy Straaberg, and David Lane. "Loris." Harvard Business School Case 524-010, October 2023. (Revised February 2024.)
      • 2023
      • Working Paper

      Black-box Training Data Identification in GANs via Detector Networks

      By: Lukman Olagoke, Salil Vadhan and Seth Neel
      Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given access to a trained GAN, as well as fresh samples from the underlying distribution, if... View Details
      Keywords: Cybersecurity; Copyright; AI and Machine Learning; Analytics and Data Science
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      Olagoke, Lukman, Salil Vadhan, and Seth Neel. "Black-box Training Data Identification in GANs via Detector Networks." Working Paper, October 2023.
      • 2025
      • Working Paper

      The Impact of Input Inaccuracy on Leveraging AI Tools: Evidence from Algorithmic Labor Scheduling

      By: Caleb Kwon, Antonio Moreno and Ananth Raman
      Problem Definition: Considerable academic and practitioner attention is placed on the value of ex-post interactions (i.e., overrides) in the human-AI interface. In contrast, relatively little attention has been paid to ex-ante human-AI interactions (e.g., the... View Details
      Keywords: AI and Machine Learning; Employees; Performance Effectiveness
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      Kwon, Caleb, Antonio Moreno, and Ananth Raman. "The Impact of Input Inaccuracy on Leveraging AI Tools: Evidence from Algorithmic Labor Scheduling." Working Paper, January 2025.
      • September 2023 (Revised January 2024)
      • Case

      AI21 Labs in 2023: Strategy for Generative AI

      By: David Yoffie, Orna Dan and Elena Corsi
      Israeli generative artificial intelligence company AI21 Labs was founded in 2017 to realize the vision of true machine intelligence. It sought to reinvent writing and reading and in 2020 it launched Wordtune, an app using GenAI software to offer alternate text... View Details
      Keywords: Decision Making; AI and Machine Learning; Innovation Strategy; Growth and Development Strategy; Applications and Software; Competitive Strategy; Technology Industry; Israel
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      Yoffie, David, Orna Dan, and Elena Corsi. "AI21 Labs in 2023: Strategy for Generative AI." Harvard Business School Case 724-383, September 2023. (Revised January 2024.)
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