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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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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