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Publications

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    • All HBS Web  (2,806)
      • Faculty Publications  (426)

      Machine Learning Models Remove Machine Learning Models →

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      • 2023
      • Working Paper

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

      By: Frabrizio 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...  View Details
      Keywords: Large Language Model; AI and Machine Learning; Performance Efficiency; Performance Improvement
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      Dell'Acqua, Frabrizio, 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–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.
      • 2023
      • Working Paper

      The Crowdless Future? How Generative AI Is Shaping the Future of Human Crowdsourcing

      By: Léonard Boussioux, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic and Karim R. Lakhani
      This study investigates the capability of generative artificial intelligence (AI) in creating innovative business solutions compared to human crowdsourcing methods. We initiated a crowdsourcing challenge focused on sustainable, circular economy business opportunities....  View Details
      Keywords: Large Language Model; Crowdsourcing; 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? How Generative AI Is Shaping the Future of Human Crowdsourcing." Harvard Business School Working Paper, No. 24-005, July 2023.
      • 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.
      • July–August 2023
      • Article

      What Smart Companies Know About Integrating AI

      By: Silvio Palumbo and David Edelman
      AI has the power to gather, analyze, and utilize enormous volumes of individual customer data to achieve precision and scale in personalization. The experiences of Mercury Financial, CVS Health, and Starbucks debunk the prevailing notion that extracting value from AI...  View Details
      Keywords: AI and Machine Learning; Customization and Personalization; Integration; Technology Adoption
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      Palumbo, Silvio, and David Edelman. "What Smart Companies Know About Integrating AI." Harvard Business Review 101, no. 4 (July–August 2023): 116–125.
      • 2023
      • Working Paper

      Beyond the Hype: Unveiling the Marginal Benefits of 3D Virtual Tours in Real Estate

      By: Mengxia Zhang and Isamar Troncoso
      3D virtual tours (VTs) have become a popular digital tool in real estate platforms, enabling potential buyers to virtually walk through the houses they search for online. In this paper, we study home sellers’ adoption of VTs and the VTs’ relative benefits compared to...  View Details
      Keywords: Marketing; AI and Machine Learning; Technology Adoption; Real Estate Industry
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      Zhang, Mengxia, and Isamar Troncoso. "Beyond the Hype: Unveiling the Marginal Benefits of 3D Virtual Tours in Real Estate." Harvard Business School Working Paper, No. 24-003, July 2023.
      • July 2023 (Revised August 2023)
      • Case

      Revenue Recognition at Stride Funding: Making Sense of Revenues for a Fintech Startup

      By: Paul M. Healy and Jung Koo Kang
      The case explores the challenges of revenue recognition and financial reporting for Stride Funding (Stride), a fintech startup that has disrupted the student loan market. Stride leveraged proprietary machine learning and financial models to underwrite alternative...  View Details
      Keywords: Financial Services Industry; United States
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      Healy, Paul M., and Jung Koo Kang. "Revenue Recognition at Stride Funding: Making Sense of Revenues for a Fintech Startup." Harvard Business School Case 124-015, July 2023. (Revised August 2023.)
      • July 2023 (Revised July 2023)
      • Background Note

      Generative AI Value Chain

      By: Andy Wu and Matt Higgins
      Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are...  View Details
      Keywords: AI; Artificial Intelligence; Model; Hardware; Data Centers
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      Wu, Andy, and Matt Higgins. "Generative AI Value Chain." Harvard Business School Background Note 724-355, July 2023. (Revised July 2023.)
      • June 20, 2023
      • Article

      Cautious Adoption of AI Can Create Positive Company Culture

      By: Joseph Pacelli and Jonas Heese
      Keywords: AI and Machine Learning; Organizational Culture; Employees
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      Pacelli, Joseph, and Jonas Heese. "Cautious Adoption of AI Can Create Positive Company Culture." CMR Insights (June 20, 2023).
      • 2023
      • Working Paper

      Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness

      By: Neil Menghani, Edward McFowland III and Daniel B. Neill
      In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false...  View Details
      Keywords: AI and Machine Learning; Forecasting and Prediction; Prejudice and Bias
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      Menghani, Neil, Edward McFowland III, and Daniel B. Neill. "Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness." Working Paper, June 2023.
      • June 19, 2023
      • Article

      Should You Start a Generative AI Company?

      By: Julian De Freitas
      Many entrepreneurs are considering starting companies that leverage the latest generative AI technology, but they must ask themselves whether they have what it takes to compete on increasingly commoditized foundational models, or whether they should instead...  View Details
      Keywords: Business Startups; Entrepreneurship; AI and Machine Learning; Applications and Software
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      De Freitas, Julian. "Should You Start a Generative AI Company?" Harvard Business Review (website) (June 19, 2023).
      • 2023
      • Working Paper

      Evaluation and Learning in R&D Investment

      By: Alexander P. Frankel, Joshua L. Krieger, Danielle Li and Dimitris Papanikolaou
      We examine the role of spillover learning in shaping the value of exploratory versus incremental R&D. Using data from drug development, we show that novel drug candidates generate more knowledge spillovers than incremental ones. Despite being less likely to reach...  View Details
      Keywords: Research and Development; Forecasting and Prediction; Valuation; Pharmaceutical Industry
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      Frankel, Alexander P., Joshua L. Krieger, Danielle Li, and Dimitris Papanikolaou. "Evaluation and Learning in R&D Investment." Harvard Business School Working Paper, No. 23-074, May 2023. (NBER Working Paper Series, No. 31290, May 2023.)
      • 2023
      • Working Paper

      Auditing Predictive Models for Intersectional Biases

      By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill
      Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we...  View Details
      Keywords: Predictive Models; Bias; AI and Machine Learning
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      Boxer, Kate S., Edward McFowland III, and Daniel B. Neill. "Auditing Predictive Models for Intersectional Biases." Working Paper, June 2023.
      • 2023
      • Working Paper

      Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data

      By: AJ Chen, Omri Even-Tov, Jung Koo Kang and Regina Wittenberg-Moerman
      By leveraging machine-learning algorithms and using nontraditional digital data derived primarily from borrowers’ mobile devices, digital lenders have vastly expanded access to credit in developing economies for millions of individuals without a prior credit history....  View Details
      Keywords: Borrowing and Debt; Credit; AI and Machine Learning; Welfare; Well-being; Developing Countries and Economies
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      Chen, AJ, Omri Even-Tov, Jung Koo Kang, and Regina Wittenberg-Moerman. "Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data." Harvard Business School Working Paper, No. 23-076, April 2023. (Revised June 2023. SSRN Working Paper Series, April 2023)
      • 2023
      • Article

      Provable Detection of Propagating Sampling Bias in Prediction Models

      By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
      With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider...  View Details
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      Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
      • 2023
      • Working Paper

      Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models

      By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
      Algorithmic assignment of refugees and asylum seekers to locations within host countries has gained attention in recent years, with implementations in the U.S. and Switzerland. These approaches use data on past arrivals to generate machine learning models that can...  View Details
      Keywords: AI and Machine Learning; Refugees; Geographic Location; Netherlands
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      Bansak, Kirk, Elisabeth Paulson, and Dominik Rothenhäusler. "Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models." Working Paper, June 2023.
      • June 2020
      • Article

      Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure

      By: Omar Isaac Asensio, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer and Sooji Ha
      By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to...  View Details
      Keywords: Environmental Sustainability; Transportation; Infrastructure; Behavior; AI and Machine Learning; Demand and Consumers
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      Asensio, Omar Isaac, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer, and Sooji Ha. "Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure." Nature Sustainability 3, no. 6 (June 2020): 463–471.
      • June 2023
      • Article

      When Does Uncertainty Matter? Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making

      By: Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage and Himabindu Lakkaraju
      As machine learning (ML) models are increasingly being employed to assist human decision makers, it becomes critical to provide these decision makers with relevant inputs which can help them decide if and how to incorporate model predictions into their decision...  View Details
      Keywords: AI and Machine Learning; Decision Making
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      McGrath, Sean, Parth Mehta, Alexandra Zytek, Isaac Lage, and Himabindu Lakkaraju. "When Does Uncertainty Matter? Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making." Transactions on Machine Learning Research (TMLR) (June 2023).
      • 2023
      • Article

      Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators

      By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
      Regression discontinuity (RD) designs are widely used to estimate causal effects in the absence of a randomized experiment. However, standard approaches to RD analysis face two significant limitations. First, they require a priori knowledge of discontinuities in...  View Details
      Keywords: Regression Discontinuity Design; Analytics and Data Science; AI and Machine Learning
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      Jakubowski, Benjamin, Siram Somanchi, Edward McFowland III, and Daniel B. Neill. "Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators." Journal of Machine Learning Research 24, no. 133 (2023): 1–57.
      • May 2023
      • Article

      Decarbonizing Health Care: Engaging Leaders in Change

      By: Vivian S. Lee, Kathy Gerwig, Emily Hough, Kedar Mate, Robert Biggio and Robert S. Kaplan
      Health care leaders are often surprised to learn that their operations contribute significantly to a warming climate. In addition to their roles as responders to and victims of extreme weather events, health care organizations have an obligation to reduce...  View Details
      Keywords: Health Care; Decarbonization; Carbon Emissions; Net-zero Emissions; Climate Change; Health Care and Treatment; Health Industry
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      Lee, Vivian S., Kathy Gerwig, Emily Hough, Kedar Mate, Robert Biggio, and Robert S. Kaplan. "Decarbonizing Health Care: Engaging Leaders in Change." NEJM Catalyst Innovations in Care Delivery 4, no. 5 (May 2023).
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