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      • December 2023 (Revised July 2024)
      • Case

      Boortmalt: The Master Maltster

      By: Forest Reinhardt, Jose B Alvarez, Damien McLoughlin, Lena Duchene and Emer Moloney
      By May 2023, Boortmalt was the world’s leading producer of malt, with a production capacity of 3 million tonnes, 15% of global market share, and 27 malting plants across five continents. It had recently acquired a major competitor and had sustained an EBITDA growth of... View Details
      Keywords: Plant-Based Agribusiness; Mergers and Acquisitions; Talent and Talent Management; Customer Focus and Relationships; Values and Beliefs; Financing and Loans; Employee Relationship Management; Collaborative Innovation and Invention; Innovation Leadership; Knowledge Sharing; Leadership Style; Business or Company Management; Growth and Development Strategy; Growth Management; Management Style; Resource Allocation; Corporate Social Responsibility and Impact; Strategic Planning; Environmental Sustainability; Organizational Culture; Agriculture and Agribusiness Industry; Belgium; Europe
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      Reinhardt, Forest, Jose B Alvarez, Damien McLoughlin, Lena Duchene, and Emer Moloney. "Boortmalt: The Master Maltster." Harvard Business School Case 724-021, December 2023. (Revised July 2024.)
      • December 2023 (Revised August 2024)
      • Supplement

      Microsoft Azure and the Cloud Wars (B)

      By: Andy Wu and Matt Higgins
      By 2023, the global market for cloud infrastructure had consolidated into a three-horse race. As of Q4 2022, Amazon, Microsoft, and Google collectively accounted for 66% of the global market. AWS had a market share of 33%, Microsoft Azure had 23%, and Google Cloud had... View Details
      Keywords: Microsoft; Artificial Intelligence; AI; Competition; Information Infrastructure; Market Participation
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      Wu, Andy, and Matt Higgins. "Microsoft Azure and the Cloud Wars (B)." Harvard Business School Supplement 724-434, December 2023. (Revised August 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.)
      • December 2023 (Revised January 2025)
      • Case

      Research In Motion: Launching and Scaling the World's First Smartphone Empire (A)

      By: Tatiana Sandino and Samuel Grad
      In 2005, Research In Motion’s (RIM) BlackBerry smartphone was a sensation. After its launch in 1999, the groundbreaking BlackBerry had captured the hearts and minds of corporate America through its secure wireless email service. The device was so addictive and... View Details
      Keywords: Business Growth and Maturation; Decision Choices and Conditions; Mobile and Wireless Technology; Innovation and Management; Technological Innovation; Business or Company Management; Management Style; Product Development; Managerial Roles; Growth and Development Strategy; Technology Industry; United States; Canada
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      Sandino, Tatiana, and Samuel Grad. "Research In Motion: Launching and Scaling the World's First Smartphone Empire (A)." Harvard Business School Case 124-023, December 2023. (Revised January 2025.)
      • 2023
      • Article

      Balancing Risk and Reward: An Automated Phased Release Strategy

      By: Yufan Li, Jialiang Mao and Iavor Bojinov
      Phased releases are a common strategy in the technology industry for gradually releasing new products or updates through a sequence of A/B tests in which the number of treated units gradually grows until full deployment or deprecation. Performing phased releases in a... View Details
      Keywords: Product Launch; Mathematical Methods; Product Development
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      Li, Yufan, Jialiang Mao, and Iavor Bojinov. "Balancing Risk and Reward: An Automated Phased Release Strategy." Advances in Neural Information Processing Systems (NeurIPS) (2023).
      • 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.
      • 2023
      • Working Paper

      Financial and Informational Integration Through Oracle Networks

      By: Lin William Cong, Eswar Prasad and Daniel Rabetti
      Oracles are software components that enable data exchange between siloed blockchains and external environments, enhancing smart contract capabilities and platform interoperability. We find that oracle integration is positively associated with total value locked and... View Details
      Keywords: Decentralized Finance; Blockchain; Smart Contract; International Economy; Interoperability; Risk Sharing; Applications and Software; Financial Markets; Economics; Risk Management
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      Cong, Lin William, Eswar Prasad, and Daniel Rabetti. "Financial and Informational Integration Through Oracle Networks." Working Paper, December 2023.
      • 2023
      • Article

      M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models

      By: Himabindu Lakkaraju, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai and Haoyi Xiong
      While Explainable Artificial Intelligence (XAI) techniques have been widely studied to explain predictions made by deep neural networks, the way to evaluate the faithfulness of explanation results remains challenging, due to the heterogeneity of explanations for... View Details
      Keywords: AI and Machine Learning
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      Lakkaraju, Himabindu, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, and Haoyi Xiong. "M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
      • 2023
      • Article

      MoPe: Model Perturbation-based Privacy Attacks on Language Models

      By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
      Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training... View Details
      Keywords: Large Language Model; AI and Machine Learning; Cybersecurity
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      Li, Marvin, Jason Wang, Jeffrey Wang, and Seth Neel. "MoPe: Model Perturbation-based Privacy Attacks on Language Models." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2023): 13647–13660.
      • 2023
      • Article

      Post Hoc Explanations of Language Models Can Improve Language Models

      By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
      Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance... View Details
      Keywords: AI and Machine Learning; Performance Effectiveness
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      Krishna, Satyapriya, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, and Himabindu Lakkaraju. "Post Hoc Explanations of Language Models Can Improve Language Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
      • December 2023
      • Article

      Self-Orienting in Human and Machine Learning

      By: Julian De Freitas, Ahmet Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum and T. Ullman
      A current proposal for a computational notion of self is a representation of one’s body in a specific time and place, which includes the recognition of that representation as the agent. This turns self-representation into a process of self-orientation, a challenging... View Details
      Keywords: AI and Machine Learning; Behavior; Learning
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      De Freitas, Julian, Ahmet Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum, and T. Ullman. "Self-Orienting in Human and Machine Learning." Nature Human Behaviour 7, no. 12 (December 2023): 2126–2139.
      • 2023
      • Other Article

      The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications

      By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers and Stuart Shieber
      Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult... View Details
      Keywords: USPTO; Natural Language Processing; Classification; Summarization; Patent Novelty; Patent Trolls; Patent Enforceability; Patents; Innovation and Invention; Intellectual Property; AI and Machine Learning; Analytics and Data Science
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      Suzgun, Mirac, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart Shieber. "The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
      • 2023
      • Chapter

      The Platform Organization

      By: Annabelle Gawer, Michael A. Cusumano and David B. Yoffie
      The industry platform is a distinct organizational form that has grown into importance over the past 20 years. We focus on the digital platform, which, along with its associated ecosystem, is uniquely positioned to create and capture value in the digital economy. View Details
      Keywords: Platform; Organization; Organizations; Internet and the Web; Value Creation; Digital Platforms
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      Gawer, Annabelle, Michael A. Cusumano, and David B. Yoffie. "The Platform Organization." Chap. 5 in Designing Adaptive Organizations, edited by Charles Snow and Øystein D. Fjeldstad, 86–103. Cambridge Companions to Management. Cambridge University Press, 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

      Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability

      By: Usha Bhalla, Suraj Srinivas and Himabindu Lakkaraju
      With the increased deployment of machine learning models in various real-world applications, researchers and practitioners alike have emphasized the need for explanations of model behaviour. To this end, two broad strategies have been outlined in prior literature to... View Details
      Keywords: AI and Machine Learning; Mathematical Methods
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      Bhalla, Usha, Suraj Srinivas, and Himabindu Lakkaraju. "Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability." Advances in Neural Information Processing Systems (NeurIPS) (2023).
      • December 2023
      • Article

      When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments

      By: Christian Kaps, Simone Marinesi and Serguei Netessine
      Globally, 1.5 billion people live off the grid, their only access to electricity often limited to operationally-expensive fossil fuel generators. Solar power has risen as a sustainable and less costly option, but its generation is variable during the day and... View Details
      Keywords: Energy; Renewable Energy
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      Kaps, Christian, Simone Marinesi, and Serguei Netessine. "When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments." Management Science 69, no. 12 (December 2023): 7633–7650.
      • 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 (Revised October 2024)
      • Supplement

      Accounting Outages at Plug Power? (C)

      By: Jonas Heese, Joseph Pacelli and James Barnett
      Set in June 2023, the C case explores Plug Power’s recovery from its financial restatements, how it benefited from government subsidies, and new strategic alliances. View Details
      Keywords: Environmental Accounting; Financial Reporting; Ethics; Finance; Management; Social Enterprise; Green Technology Industry; Green Technology Industry; United States; Europe
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      Heese, Jonas, Joseph Pacelli, and James Barnett. "Accounting Outages at Plug Power? (C)." Harvard Business School Supplement 124-019, November 2023. (Revised October 2024.)
      • 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.
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