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      • 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.
      • October 2023
      • Article

      Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

      By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
      We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years.... View Details
      Keywords: Safety Regulations; Regulations; Regulatory Enforcement; Machine Learning Models; Safety; Operations; Service Operations; Production; Forecasting and Prediction; Decisions; United States
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      Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." American Economic Journal: Applied Economics 15, no. 4 (October 2023): 30–67. (Profiled in the Regulatory Review.)
      • 2023
      • Working Paper

      In-Context Unlearning: Language Models as Few Shot Unlearners

      By: Martin Pawelczyk, Seth Neel and Himabindu Lakkaraju
      Machine unlearning, the study of efficiently removing the impact of specific training points on the trained model, has garnered increased attention of late, driven by the need to comply with privacy regulations like the Right to be Forgotten. Although unlearning is... View Details
      Keywords: AI and Machine Learning; Copyright; Information
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      Pawelczyk, Martin, Seth Neel, and Himabindu Lakkaraju. "In-Context Unlearning: Language Models as Few Shot Unlearners." Working Paper, October 2023.
      • 2023
      • Working Paper

      The Customer Journey as a Source of Information

      By: Nicolas Padilla, Eva Ascarza and Oded Netzer
      In the face of heightened data privacy concerns and diminishing third-party data access, firms are placing increased emphasis on first-party data (1PD) for marketing decisions. However, in environments with infrequent purchases, reliance on past purchases 1PD... View Details
      Keywords: Customer Journey; Privacy; Consumer Behavior; Analytics and Data Science; AI and Machine Learning; Customer Focus and Relationships
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      Padilla, Nicolas, Eva Ascarza, and Oded Netzer. "The Customer Journey as a Source of Information." Harvard Business School Working Paper, No. 24-035, October 2023. (Revised 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 29, 2023
      • Article

      Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI

      By: Simon Friis and James Riley
      When it comes to artificial intelligence and inequality, algorithmic bias rightly receives a lot of attention. But it’s just one way that AI can lead to inequitable outcomes. To truly create equitable AI, we need to consider three forces through which it might make... View Details
      Keywords: AI and Machine Learning; Prejudice and Bias; Equality and Inequality
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      Friis, Simon, and James Riley. "Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI." Harvard Business Review (website) (September 29, 2023).
      • September 2023
      • Case

      Ada: Cultivating Investors

      By: Reza Satchu and Patrick Sanguineti
      Mike Murchison, co-founder and CEO of Ada, has an enviable dilemma. Launched in 2016 by Murchison and his co-founder David Hariri, Ada is an AI-native company that aims to revolutionize how businesses approach customer service. The company has already attracted a buzz,... View Details
      Keywords: Founder; Fundraising; Business Startups; Decisions; Entrepreneurship; Venture Capital; AI and Machine Learning; Technology Industry
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      Satchu, Reza, and Patrick Sanguineti. "Ada: Cultivating Investors." Harvard Business School Case 824-090, September 2023.
      • 2023
      • Working Paper

      Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality

      By: Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani
      The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine... View Details
      Keywords: Large Language Model; AI and Machine Learning; Performance Efficiency; Performance Improvement
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      Dell'Acqua, Fabrizio, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim R. Lakhani. "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality." Harvard Business School Working Paper, No. 24-013, September 2023.
      • 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.)
      • September 2023 (Revised April 2024)
      • Case

      Atomwise: Strategic Opportunities in AI for Pharma

      By: Satish Tadikonda
      Abraham Heifets and his co-founder, Izhar Wallach, had founded Atomwise to develop i) an AI engine to transform drug discovery by creating better medicines faster, and ii) a machine learning-based discovery engine that combined the power of convolutional neural... View Details
      Keywords: Business Model; Business Startups; AI and Machine Learning; Science-Based Business; Technological Innovation; Biotechnology Industry; Pharmaceutical Industry
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      Tadikonda, Satish. "Atomwise: Strategic Opportunities in AI for Pharma." Harvard Business School Case 824-043, September 2023. (Revised April 2024.)
      • September 2023
      • Case

      Super Quantum: Using Artificial Intelligence to Transform Asset Management (A)

      By: Feng Zhu and Kerry Herman
      Dr. Zhang, CEO of Super Quantum, an AI-driven hedge fund, is considering an investor’s request to withdraw their funds as the markets experience volatility. Should he pull the investor’s funds? View Details
      Keywords: AI and Machine Learning; Volatility; Financial Markets; Investment Funds; Decision Choices and Conditions; Financial Services Industry
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      Zhu, Feng, and Kerry Herman. "Super Quantum: Using Artificial Intelligence to Transform Asset Management (A)." Harvard Business School Case 624-027, September 2023.
      • September 2023
      • Article

      A Pull versus Push Framework for Reputation

      By: Jillian J. Jordan
      Reputation is a powerful driver of human behavior. Reputation systems incentivize 'actors' to take reputation-enhancing actions, and 'evaluators' to reward actors with positive reputations by preferentially cooperating with them. This article proposes a reputation... View Details
      Keywords: Reputation; Behavior; Game Theory
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      Jordan, Jillian J. "A Pull versus Push Framework for Reputation." Trends in Cognitive Sciences 27, no. 9 (September 2023): 852–866.
      • 2023
      • Article

      On the Impact of Actionable Explanations on Social Segregation

      By: Ruijiang Gao and Himabindu Lakkaraju
      As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse. To this end, there has been a growing body of research... View Details
      Keywords: Forecasting and Prediction; AI and Machine Learning; Outcome or Result
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      Gao, Ruijiang, and Himabindu Lakkaraju. "On the Impact of Actionable Explanations on Social Segregation." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 10727–10743.
      • September–October 2023
      • Article

      Reskilling in the Age of AI

      By: Jorge Tamayo, Leila Doumi, Sagar Goel, Orsolya Kovács-Ondrejkovic and Raffaella Sadun
      In the coming decades, as the pace of technological change continues to increase, millions of workers may need to be not just upskilled but reskilled—a profoundly complex societal challenge that will sometimes require workers to both acquire new skills and... View Details
      Keywords: Competency and Skills; AI and Machine Learning; Training; Adaptation; Employees; Digital Transformation
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      Tamayo, Jorge, Leila Doumi, Sagar Goel, Orsolya Kovács-Ondrejkovic, and Raffaella Sadun. "Reskilling in the Age of AI." Harvard Business Review 101, no. 5 (September–October 2023): 56–65.
      • September 2023
      • Article

      The Dynamics of Team Learning: Harmony and Rhythm in Teamwork Arrangements for Innovation

      By: Jean-François Harvey, Johnathan R. Cromwell, Kevin J. Johnson and Amy C. Edmondson
      Innovation teams must navigate inherent tensions between different learning activities to produce high levels of performance. Yet, we know little about how teams combine these activities—notably reflexive, experimental, vicarious, and contextual learning—most... View Details
      Keywords: Groups and Teams; Learning; Performance Effectiveness; Collaborative Innovation and Invention
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      Harvey, Jean-François, Johnathan R. Cromwell, Kevin J. Johnson, and Amy C. Edmondson. "The Dynamics of Team Learning: Harmony and Rhythm in Teamwork Arrangements for Innovation." Administrative Science Quarterly 68, no. 3 (September 2023): 601–647.
      • August 2023 (Revised December 2023)
      • Case

      Automating Morality: Ethics for Intelligent Machines

      By: Joseph L. Badaracco Jr. and Tom Quinn
      As autonomy became a more significant part of modern life – most notably in autonomous vehicles (AVs), such as Teslas – ethical debates about whether and how to impart ethics to machines heated up. Utilitarians pointed out that autonomous vehicles crashed much less... View Details
      Keywords: Cost vs Benefits; Judgments; Fairness; Moral Sensibility; Values and Beliefs; Cross-Cultural and Cross-Border Issues; Disruptive Innovation; Technology Adoption; Risk and Uncertainty; Cognition and Thinking; Technological Innovation; Auto Industry; Technology Industry; Africa; Asia; Europe; North and Central America; Oceania; South America
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      Badaracco, Joseph L., Jr., and Tom Quinn. "Automating Morality: Ethics for Intelligent Machines." Harvard Business School Case 324-007, August 2023. (Revised December 2023.)
      • 2024
      • Working Paper

      The Crowdless Future? Generative AI and Creative Problem Solving

      By: Léonard Boussioux, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic and Karim R. Lakhani
      The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy... View Details
      Keywords: Large Language Models; Crowdsourcing; Generative Ai; Creative Problem-solving; Organizational Search; AI-in-the-loop; Prompt Engineering; AI and Machine Learning; Innovation and Invention
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      Boussioux, Léonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "The Crowdless Future? Generative AI and Creative Problem Solving." Harvard Business School Working Paper, No. 24-005, July 2023. (Revised July 2024.)
      • 2023
      • Working Paper

      Channeled Attention and Stable Errors

      By: Tristan Gagnon-Bartsch, Matthew Rabin and Joshua Schwartzstein
      We develop a framework for assessing when somebody will eventually notice that she has a misspecified model of the world, premised on the idea that she neglects information that she deems—through the lens of her misconceptions—to be irrelevant. In doing so, we... View Details
      Keywords: Attentional Stability; Cognition and Thinking; Attitudes; Information; Theory
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      Gagnon-Bartsch, Tristan, Matthew Rabin, and Joshua Schwartzstein. "Channeled Attention and Stable Errors." Working Paper, August 2023. (Revise and Resubmit, Quarterly Journal of Economics.)
      • August 2023
      • Article

      Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel

      By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
      Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use... View Details
      Keywords: AI and Machine Learning; Technological Innovation; Technology Adoption
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      Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel." Nature Machine Intelligence 5, no. 8 (August 2023): 873–883.
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