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    • All HBS Web  (1,251)
      • Faculty Publications  (438)

      Interpretable Machine LearningRemove Interpretable Machine Learning →

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      • 2024
      • Working Paper

      Old Moats for New Models: Openness, Control, and Competition in Generative AI

      By: Pierre Azoulay, Joshua L. Krieger and Abhishek Nagaraj
      Drawing insights from the field of innovation economics, we discuss the likely competitive environment shaping generative AI advances. Central to our analysis are the concepts of appropriability—whether firms in the industry are able to control the knowledge generated... View Details
      Keywords: Technological Innovation; AI and Machine Learning; Open Source Distribution; Policy
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      Azoulay, Pierre, Joshua L. Krieger, and Abhishek Nagaraj. "Old Moats for New Models: Openness, Control, and Competition in Generative AI." NBER Working Paper Series, No. 7442, May 2024.
      • May–June 2024
      • Article

      Should Your Brand Hire a Virtual Influencer?

      By: Serim Hwang, Shunyuan Zhang, Xiao Liu and Kannan Srinivasan
      Followers respond more favorably to sponsored posts by virtual influencers versus those by humans, costs are lower, and creating an influencer from scratch allows marketers to introduce more diversity. View Details
      Keywords: Social Media; AI and Machine Learning; Brands and Branding; Power and Influence
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      Hwang, Serim, Shunyuan Zhang, Xiao Liu, and Kannan Srinivasan. "Should Your Brand Hire a Virtual Influencer?" Harvard Business Review 102, no. 3 (May–June 2024): 56–60.
      • May 2024
      • Article

      The Health Risks of Generative AI-Based Wellness Apps

      By: Julian De Freitas and G. Cohen
      Artifcial intelligence (AI)-enabled chatbots are increasingly being used to help people manage their mental health. Chatbots for mental health and particularly ‘wellness’ applications currently exist in a regulatory ‘gray area’. Indeed, most generative AI-powered... View Details
      Keywords: AI and Machine Learning; Well-being; Governing Rules, Regulations, and Reforms; Applications and Software
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      De Freitas, Julian, and G. Cohen. "The Health Risks of Generative AI-Based Wellness Apps." Nature Medicine 30, no. 5 (May 2024): 1269–1275.
      • April 2024 (Revised December 2024)
      • Case

      Anthropic: Building Safe AI

      By: Shikhar Ghosh and Shweta Bagai
      In late 2024, Anthropic, a leading AI safety and research company, achieved a significant breakthrough with computer use capabilities that allowed AI to interact with computers like humans. Co-founded by former OpenAI employees and known for its generative AI... View Details
      Keywords: AI and Machine Learning; Corporate Accountability; Corporate Social Responsibility and Impact; Business Growth and Maturation; Corporate Strategy; Technology Industry; United States
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      Ghosh, Shikhar, and Shweta Bagai. "Anthropic: Building Safe AI." Harvard Business School Case 824-129, April 2024. (Revised December 2024.)
      • April 2024
      • Case

      Managing AI Risks in Consumer Banking

      By: Suraj Srinivasan, Satish Tadikonda, Paul Dongha, Manoj Saxena and Radhika Kak
      In early 2024, Ruth Jones, head of digital banking at Signa Bank, a (fictitious) European consumer bank, was thinking about how to best incorporate GenAI capabilities to improve efficiencies and create new ways to improve the customer experience. Where were the biggest... View Details
      Keywords: Customer Relationship Management; AI and Machine Learning; Risk Management; Opportunities; Customization and Personalization; Banking Industry; Europe
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      Srinivasan, Suraj, Satish Tadikonda, Paul Dongha, Manoj Saxena, and Radhika Kak. "Managing AI Risks in Consumer Banking." Harvard Business School Case 124-093, April 2024.
      • 2024
      • Working Paper

      Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions

      By: Caleb Kwon, Ananth Raman and Jorge Tamayo
      We investigate whether corporate officers should grant managers discretion to override AI-driven demand forecasts and labor scheduling tools. Analyzing five years of administrative data from a large grocery retailer using such an AI tool, encompassing over 500 stores,... View Details
      Keywords: AI and Machine Learning; Forecasting and Prediction; Working Conditions; Performance Productivity
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      Kwon, Caleb, Ananth Raman, and Jorge Tamayo. "Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions." Working Paper, April 2024.
      • April 2024
      • Article

      A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification

      By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
      Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),... View Details
      Keywords: Health Disorders; Health Testing and Trials; AI and Machine Learning; Health Industry
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      Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278.
      • April 2024
      • Article

      Detecting Routines: Applications to Ridesharing CRM

      By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman
      Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal... View Details
      Keywords: Ride-sharing; Routine; Machine Learning; Customer Relationship Management; Consumer Behavior; Segmentation
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      Dew, Ryan, Eva Ascarza, Oded Netzer, and Nachum Sicherman. "Detecting Routines: Applications to Ridesharing CRM." Journal of Marketing Research (JMR) 61, no. 2 (April 2024): 368–392.
      • April 2024 (Revised November 2024)
      • Case

      Moderna: Pioneering a People Platform to Accelerate Science Innovation

      By: Tatiana Sandino, Emil Dy and Samuel Grad
      Moderna was founded in 2010 to explore how messenger ribonucleic acid (mRNA) could be used to create breakthrough medicines by encoding instructions for the body to create antibodies. When Stéphane Bancel (HBS 2000) took over in 2011, he bet on the potential of this... View Details
      Keywords: Disruptive Innovation; Talent and Talent Management; Selection and Staffing; AI and Machine Learning; Digital Strategy; Innovation and Management; Leadership Development; Management Practices and Processes; Management Systems; Organizational Culture; Performance Evaluation; Alignment; Employee Relationship Management; Science-Based Business; Expansion; Pharmaceutical Industry; Biotechnology Industry; United States
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      Sandino, Tatiana, Emil Dy, and Samuel Grad. "Moderna: Pioneering a People Platform to Accelerate Science Innovation." Harvard Business School Case 124-091, April 2024. (Revised November 2024.)
      • March 2024 (Revised April 2024)
      • Case

      Coursera's Foray into GenAI

      By: Suraj Srinivasan, Michael Parzen and Radhika Kak
      In early 2023, Maggioncalda, CEO of US EdTech firm Coursera, launched Project Genesis to develop a strategy for incorporating GenAI capabilities into the firm's offerings, asking his teams to focus on value to the firm and cost of implementation. The team identified... View Details
      Keywords: Business Model; AI and Machine Learning; Brands and Branding; Business Strategy; Competitive Advantage; Technological Innovation; Education Industry; Technology Industry; United States
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      Srinivasan, Suraj, Michael Parzen, and Radhika Kak. "Coursera's Foray into GenAI." Harvard Business School Case 124-089, March 2024. (Revised April 2024.)
      • March 2024 (Revised May 2024)
      • Case

      Amperity: First-Party Data at a Crossroads

      By: Elie Ofek, Hema Yoganarasimhan and Alexis Lefort
      In the summer of 2023, Amperity management was facing a critical decision on its future direction. Given the dramatic changes occurring within the digital advertising ecosystem, as concerns over consumer privacy placed limits on the ability to engage in third-party... View Details
      Keywords: AI and Machine Learning; Technology Adoption; Business Strategy; Digital Marketing; Price; Product; Business or Company Management; Advertising Industry
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      Ofek, Elie, Hema Yoganarasimhan, and Alexis Lefort. "Amperity: First-Party Data at a Crossroads." Harvard Business School Case 524-017, March 2024. (Revised May 2024.)
      • March 2024
      • Simulation

      'Storrowed'

      By: Mitchell Weiss
      The game was built to accompany "Storrowed": A Generative AI Exercise, available through Harvard Business Publishing. The game adds a timing element to "Storrowed" and enables the teacher to reward teams for strong prompts or penalize teams for believing AI... View Details
      Keywords: AI and Machine Learning; Decision Choices and Conditions; Risk and Uncertainty
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      Weiss, Mitchell. "'Storrowed'." Harvard Business School Simulation 824-714, March 2024.
      • March 2024 (Revised April 2025)
      • Case

      TELEXISTENCE Inc.

      By: Paul A. Gompers and Akiko Saito
      A case about a Japanese robotics startup aiming to enter the U.S. market with its robots that combine AI and human intervention to complete restocking tasks in retail stores. View Details
      Keywords: Business Startups; Entrepreneurship; Market Entry and Exit; Technology Adoption; Decisions; AI and Machine Learning; Retail Industry; Technology Industry; Japan; United States
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      Gompers, Paul A., and Akiko Saito. "TELEXISTENCE Inc." Harvard Business School Case 224-031, March 2024. (Revised April 2025.)
      • 2024
      • Working Paper

      The Cram Method for Efficient Simultaneous Learning and Evaluation

      By: Zeyang Jia, Kosuke Imai and Michael Lingzhi Li
      We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and tests its empirical... View Details
      Keywords: AI and Machine Learning
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      Jia, Zeyang, Kosuke Imai, and Michael Lingzhi Li. "The Cram Method for Efficient Simultaneous Learning and Evaluation." Working Paper, March 2024.
      • March 2024
      • Teaching Note

      'Storrowed': A Generative AI Exercise

      By: Mitchell Weiss
      Teaching Note for HBS Exercise No. 824-188. “Storrowed” is an exercise to help participants raise their proficiency with generative AI. It begins by highlighting a problem: trucks getting wedged underneath bridges in Boston, Massachusetts on the city’s Storrow Drive.... View Details
      Keywords: AI and Machine Learning; Entrepreneurship; Innovation and Invention; Government Administration; Transportation Industry; Public Administration Industry
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      Weiss, Mitchell. "'Storrowed': A Generative AI Exercise." Harvard Business School Teaching Note 824-189, March 2024.
      • March 2024
      • Exercise

      'Storrowed': A Generative AI Exercise

      By: Mitchell Weiss
      "Storrowed" is an exercise to help participants raise their capacity and curiosity for generative AI. It focuses on generative AI for problem understanding and ideation, but can be adapted for use more broadly. Participants use generative AI tools to understand a... View Details
      Keywords: AI and Machine Learning; Problems and Challenges
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      Weiss, Mitchell. "'Storrowed': A Generative AI Exercise." Harvard Business School Exercise 824-188, March 2024.
      • March 2024
      • Teaching Note

      CoPilot(s): Generative AI at Microsoft and GitHub

      By: Frank Nagle and Maria P. Roche
      This teaching note is the companion to case N9-624-010 CoPilot(s): Generative AI at Microsoft and GitHub, which takes place in late 2021. The case briefly describes the history of both GitHub and Microsoft with a particular focus on open source software (OSS)—software... View Details
      Keywords: Mergers and Acquisitions; AI and Machine Learning; Applications and Software; Technological Innovation; Product Launch; Open Source Distribution; Product Development; Commercialization; Competition; Resource Allocation; Technology Industry
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      Nagle, Frank, and Maria P. Roche. "CoPilot(s): Generative AI at Microsoft and GitHub." Harvard Business School Teaching Note 724-452, March 2024.
      • 2023
      • Working Paper

      An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits

      By: Biyonka Liang and Iavor I. Bojinov
      Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the... View Details
      Keywords: Analytics and Data Science; AI and Machine Learning; Mathematical Methods
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      Liang, Biyonka, and Iavor I. Bojinov. "An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits." Harvard Business School Working Paper, No. 24-057, March 2024.
      • March 2024 (Revised June 2024)
      • Case

      Governing OpenAI (A)

      By: Lynn S. Paine, Suraj Srinivasan and Will Hurwitz
      In late November 2023, OpenAI’s new board of directors took stock of the situation. The company, which sought to develop artificial general intelligence (AGI)—computer systems with capabilities exceeding human abilities—was looking to regain its footing after a chaotic... View Details
      Keywords: Artificial Intelligence; Board Of Directors; Board Decisions; Board Dynamics; Corporate Boards; Governance Changes; Governance Structure; Leadership Change; Legal Aspects Of Business; Nonprofit Governance; Strategy And Execution; Technological Change; AI and Machine Learning; Corporate Governance; Leadership; Management; Mission and Purpose; Technological Innovation; Governing Rules, Regulations, and Reforms; Governing and Advisory Boards; Resignation and Termination; Ethics; Nonprofit Organizations; Open Source Distribution; Partners and Partnerships; Technology Industry; San Francisco; United States
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      Paine, Lynn S., Suraj Srinivasan, and Will Hurwitz. "Governing OpenAI (A)." Harvard Business School Case 324-103, March 2024. (Revised June 2024.)
      • March 2024 (Revised August 2024)
      • Case

      Darktrace: Scaling Cybersecurity and AI (A)

      By: Jeffrey F. Rayport and Alexis Lefort
      In 2023, Darktrace CEO Poppy Gustafsson was contemplating her growth strategy at a leading U.K.-based cybersecurity venture, launched in 2013 by a group of anti-terror cyber specialists, University of Cambridge mathematicians, and artificial intelligence (AI) experts.... View Details
      Keywords: Technology; Talent; Scaling; Entrepreneurship; Cybersecurity; Leadership; Business Growth and Maturation; Recruitment; Resignation and Termination; AI and Machine Learning; Growth and Development Strategy; Organizational Culture; Going Public; Technology Industry; United Kingdom; Europe; United States
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      Rayport, Jeffrey F., and Alexis Lefort. "Darktrace: Scaling Cybersecurity and AI (A)." Harvard Business School Case 824-092, March 2024. (Revised August 2024.)
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