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  • All HBS Web  (1,655)
    • News  (549)
    • Research  (600)
    • Events  (29)
    • Multimedia  (82)
  • Faculty Publications  (581)
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  • September 2020
  • Case

True North: Pioneering Analytics, Algorithms and Artificial Intelligence

By: Karim R. Lakhani, Kairavi Dey and Hannah Mayer
True North was a private equity fund that specialized in the growth and buyout of mid-market, India-centric companies. The leadership team initially believed that technology was not core to traditional businesses and steered clear of new age technology-oriented... View Details
Keywords: Artificial Intelligence; Information Technology; Management; Operations; Organizations; Leadership; Innovation and Invention; Business Model; AI and Machine Learning; Computer Industry; Technology Industry
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Lakhani, Karim R., Kairavi Dey, and Hannah Mayer. "True North: Pioneering Analytics, Algorithms and Artificial Intelligence." Harvard Business School Case 621-042, September 2020.
  • March 2022 (Revised January 2025)
  • Technical Note

Prediction & Machine Learning

By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional... View Details
Keywords: Machine Learning; Data Science; Learning; Analytics and Data Science; Performance Evaluation; AI and Machine Learning
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Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised January 2025.)
  • October–December 2022
  • Article

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
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Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
  • 2021
  • Chapter

Towards a Unified Framework for Fair and Stable Graph Representation Learning

By: Chirag Agarwal, Himabindu Lakkaraju and Marinka Zitnik
As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual... View Details
Keywords: Graph Neural Networks; AI and Machine Learning; Prejudice and Bias
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Agarwal, Chirag, Himabindu Lakkaraju, and Marinka Zitnik. "Towards a Unified Framework for Fair and Stable Graph Representation Learning." In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, edited by Cassio de Campos and Marloes H. Maathuis, 2114–2124. AUAI Press, 2021.
  • Teaching Interest

Overview

By: Mitchell B. Weiss
Public entrepreneurship, entrepreneurship, leadership, business and government, cities, artificial intelligence View Details
Keywords: Public Entrepreneurship; Leadership; Business And Government; Artificial Intelligence; Entrepreneurship; Innovation and Invention; Innovation Leadership; Collaborative Innovation and Invention; Public Sector; City; AI and Machine Learning
  • 31 May 2023
  • Research & Ideas

With Predictive Analytics, Companies Can Tap the Ultimate Opportunity: Customers’ Routines

Be Regulated—if at All? Is AI Coming for Your Job? Feedback or ideas to share? Email the Working Knowledge team at hbswk@hbs.edu. Image: iStockphoto/kate_sept2004 View Details
Keywords: by Rachel Layne; Transportation
  • June 2024 (Revised September 2024)
  • Case

Driving Scale with Otto

By: Rebecca Karp, David Allen and Annelena Lobb
This case asks how startup founders make scaling decisions in light of their priorities for their business and for themselves. Otto was a technology company that applied artificial intelligence technology to sales. It deployed natural language processing to find sales... View Details
Keywords: Artificial Intelligence; Natural Language Processing; B2B; B2B Innovation; Scaling; Scaling Tech Ventures; Business Startups; AI and Machine Learning; Finance; Sales; Business Strategy; Growth and Development Strategy; Entrepreneurship; Information Technology Industry; United States; Cambridge; New York (city, NY); Spain
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Karp, Rebecca, David Allen, and Annelena Lobb. "Driving Scale with Otto." Harvard Business School Case 724-407, June 2024. (Revised September 2024.)
  • 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.
  • January–February 2023
  • Article

Forecasting COVID-19 and Analyzing the Effect of Government Interventions

By: Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis and Dimitris Bertsimas
We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more... View Details
Keywords: COVID-19 Pandemic; Epidemics; Analytics and Data Science; Health Pandemics; AI and Machine Learning; Forecasting and Prediction
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Li, Michael Lingzhi, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis, and Dimitris Bertsimas. "Forecasting COVID-19 and Analyzing the Effect of Government Interventions." Operations Research 71, no. 1 (January–February 2023): 184–201.
  • 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.
  • August 2024
  • Background Note

Mitigating Climate Change with Machine Learning

By: Michael W. Toffel, Kelsey Carter, Amy Chambers, Avery Park and Susan Pinckney
This note highlights how machine learning is being used to decarbonize (reduce GHG emissions) several key sectors including electricity, transportation, building, industrial processes, and agriculture -- and how machine learning is being used to accelerate efforts to... View Details
Keywords: Climate; Artificial Intelligence; Adaptation; Climate Change; AI and Machine Learning; Innovation and Invention
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Toffel, Michael W., Kelsey Carter, Amy Chambers, Avery Park, and Susan Pinckney. "Mitigating Climate Change with Machine Learning." Harvard Business School Background Note 625-014, August 2024.
  • March 1, 2022
  • Article

Widespread Use of National Academies Consensus Reports by the American Public

By: Diana Hicks, Matteo Zullo, Ameet Doshi and Omar Isaac Asensio
In seeking to understand how to protect the public information sphere from corruption, researchers understandably focus on dysfunction. However, parts of the public information ecosystem function very well, and understanding this as well will help in protecting and... View Details
Keywords: Reports; Surveys; AI and Machine Learning; Knowledge Dissemination; Knowledge Use and Leverage
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Hicks, Diana, Matteo Zullo, Ameet Doshi, and Omar Isaac Asensio. "Widespread Use of National Academies Consensus Reports by the American Public." e2107760119. Proceedings of the National Academy of Sciences 119, no. 9 (March 1, 2022).
  • 2023
  • Article

Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten

By: Himabindu Lakkaraju, Satyapriya Krishna and Jiaqi Ma
The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an... View Details
Keywords: Analytics and Data Science; AI and Machine Learning; Decision Making; Governing Rules, Regulations, and Reforms
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Lakkaraju, Himabindu, Satyapriya Krishna, and Jiaqi Ma. "Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 17808–17826.
  • 2023
  • Article

Experimental Evaluation of Individualized Treatment Rules

By: Kosuke Imai and Michael Lingzhi Li
The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a... View Details
Keywords: Causal Inference; Heterogeneous Treatment Effects; Precision Medicine; Uplift Modeling; Analytics and Data Science; AI and Machine Learning
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Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association 118, no. 541 (2023): 242–256.
  • December 2020
  • Case

VIA Science (A)

By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman
Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the... View Details
Keywords: Data Analytics; Machine Learning; Artificial Intelligence; Strategy; Business Startups; Markets; AI and Machine Learning; Telecommunications Industry; Utilities Industry; United States; Japan
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Alcácer, Juan, Rembrand Koning, Annelena Lobb, and Kerry Herman. "VIA Science (A)." Harvard Business School Case 721-367, December 2020.
  • July 2025
  • Article

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
To mitigate information asymmetry about borrowers in developing economies, digital lenders use machine-learning algorithms and nontraditional data from borrowers’ mobile devices. Consequently, digital lenders have managed to expand access to credit for millions of... View Details
Keywords: Informal Economy; Digital Banking; Mobile Phones; Developing Countries and Economies; Mobile and Wireless Technology; AI and Machine Learning; Analytics and Data Science; Credit; Borrowing and Debt; Well-being; Banking Industry; Kenya
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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." Accounting Review 100, no. 4 (July 2025): 135–159.
  • December 2018
  • Teaching Note

Autonomous Vehicles: The Rubber Hits the Road…but When?

By: William Kerr and James Palano
The autonomous vehicles have enormous implications for business and society. But, despite the headline-laden attention paid to the technology, there remain more questions than answers. Students will learn about the complex industry and have explicit discussions about... View Details
Keywords: Technology Management; Artificial Intelligence; General Management; Robotics; Technological Innovation; Transportation; Disruption; Information Technology; Management; Decision Making; AI and Machine Learning; Auto Industry; Technology Industry
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Kerr, William, and James Palano. "Autonomous Vehicles: The Rubber Hits the Road…but When?" Harvard Business School Teaching Note 819-040, December 2018.
  • February 2024
  • Case

SundaySky: Changing Customer Experiences through Personalized Video

By: David C. Edelman and James Barnett
In June 2023, SundaySky CEO Jim Dicso considers growth strategies. The software-as-a-service company provided software to create advertising videos, customer service videos, and other videos, like employee training modules, and had begun to pilot a new generative... View Details
Keywords: Advertising; Strategy; Technology Adoption; AI and Machine Learning; Applications and Software; Growth and Development Strategy; Advertising Industry; Technology Industry; United States
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Edelman, David C., and James Barnett. "SundaySky: Changing Customer Experiences through Personalized Video." Harvard Business School Case 524-013, February 2024.
  • 15 Jan 2019
  • First Look

New Research and Ideas, January 15, 2019

that can serve as the basis for a rich classroom discussion. Purchase this case:https://hbsp.harvard.edu/product/519007-PDF-ENG Harvard Business School Case 819-062 Shield AI Shield AI’s quadcopter—with no pilot and no flight plan—could... View Details
Keywords: Dina Gerdeman
  • July 2023
  • Case

DayTwo: Going to Market with Gut Microbiome (Abridged)

By: Ayelet Israeli
DayTwo is a young Israeli startup that applies research on the gut microbiome and machine learning algorithms to deliver personalized nutritional recommendations to its users in order to minimize blood sugar spikes after meals. After a first year of trial rollout in... View Details
Keywords: Business Startups; AI and Machine Learning; Nutrition; Market Entry and Exit; Product Marketing; Distribution Channels
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Israeli, Ayelet. "DayTwo: Going to Market with Gut Microbiome (Abridged)." Harvard Business School Case 524-015, July 2023.
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