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Show Results For
- All HBS Web
(1,002)
- People (1)
- News (156)
- Research (635)
- Events (13)
- Multimedia (3)
- Faculty Publications (544)
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- 2025
- Article
Humor as a Window into Generative AI Bias
By: Roger Samure, Julian De Freitas and Stefano Puntoni
A preregistered audit of 600 images by generative AI across 150 different prompts explores the link between humor and discrimination in consumer-facing AI solutions. When ChatGPT updates images to make them “funnier”, the prevalence of stereotyped groups changes. While... View Details
Samure, Roger, Julian De Freitas, and Stefano Puntoni. "Humor as a Window into Generative AI Bias." Art. 1326. Scientific Reports 15 (2025).
- 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
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.
- June 20, 2023
- Article
Cautious Adoption of AI Can Create Positive Company Culture
By: Joseph Pacelli and Jonas Heese
Pacelli, Joseph, and Jonas Heese. "Cautious Adoption of AI Can Create Positive Company Culture." CMR Insights (June 20, 2023).
- February 2024
- Technical Note
AI Product Development Lifecycle
By: Michael Parzen, Jessie Li and Marily Nika
In this article, we will discuss the concept of AI Products, how they are changing our daily lives, how the field of AI & Product Management is evolving, and the AI Product Development Lifecycle. View Details
Keywords: Artificial Intelligence; Product Management; Product Life Cycle; Technology; AI and Machine Learning; Product Development
Parzen, Michael, Jessie Li, and Marily Nika. "AI Product Development Lifecycle." Harvard Business School Technical Note 624-070, February 2024.
- 2025
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers
By: Matthew DosSantos DiSorbo, Kris Ferreira, Maya Balakrishnan and Jordan Tong
Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). How can humans and algorithms work together to make... View Details
DosSantos DiSorbo, Matthew, Kris Ferreira, Maya Balakrishnan, and Jordan Tong. "Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers." Working Paper, May 2025.
- 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
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 2022
- Supplement
Borusan CAT: Monetizing Prediction in the Age of AI (B)
By: Navid Mojir and Gamze Yucaoglu
Borusan Cat is an international distributor of Caterpillar heavy machines. In 2021, it had been three years since Ozgur Gunaydin (CEO) and Esra Durgun (Director of Strategy, Digitization, and Innovation) started working on Muneccim, the company’s predictive AI tool.... View Details
Keywords: AI and Machine Learning; Commercialization; Technology Adoption; Industrial Products Industry; Turkey; Middle East
Mojir, Navid, and Gamze Yucaoglu. "Borusan CAT: Monetizing Prediction in the Age of AI (B)." Harvard Business School Supplement 522-045, May 2022.
- October 14, 2023
- Article
Will Consumers Buy Selfish Self-Driving Cars?
De Freitas, Julian. "Will Consumers Buy Selfish Self-Driving Cars?" Wall Street Journal (October 14, 2023), C5.
- August 13, 2024
- Editorial
Can AI Save Physicians from Burnout?
By: Susanna Gallani, Lidia Moura and Katie Sonnefeldt
Gallani, Susanna, Lidia Moura, and Katie Sonnefeldt. "Can AI Save Physicians from Burnout?" Harvard Business School Working Knowledge (August 13, 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
- July 2024
- Case
The Voice Wars Continues 2024: Hey Google vs. Alexa vs. Siri vs. ChatGPT
By: David B. Yoffie and Sarah von Bargen
In 2024, the Voice War was transforming from a relatively simple index-based technology system, which relied on a list of commands and answers, to a generative AI system, which offered the promise to enable free flowing conversations between people and machines. The... View Details
Yoffie, David B., and Sarah von Bargen. "The Voice Wars Continues 2024: Hey Google vs. Alexa vs. Siri vs. ChatGPT." Harvard Business School Case 725-352, July 2024.
- January 2024
- Case
The Financial Times (FT) and Generative AI
By: Andrew Rashbass, Ramon Casadesus-Masanell and Jordan Mitchell
In September 2023, John Ridding, CEO of the Financial Times, was considering the possible impact of Generative AI on the industry and his business. Having navigated successfully the seismic shift from print to digital, and reporting record results, the company... View Details
Keywords: AI and Machine Learning; Technology Adoption; Change Management; Journalism and News Industry
Rashbass, Andrew, Ramon Casadesus-Masanell, and Jordan Mitchell. "The Financial Times (FT) and Generative AI." Harvard Business School Case 724-410, January 2024.
- January–February 2025
- Article
Why People Resist Embracing AI
The success of AI depends not only on its capabilities, which are becoming more advanced each day, but on people’s willingness to harness them. Unfortunately, many people view AI negatively, fearing it will cause job losses, increase the likelihood that their personal... View Details
De Freitas, Julian. "Why People Resist Embracing AI." Harvard Business Review 103, no. 1 (January–February 2025): 52–56.
- 11 Oct 2024
- Research & Ideas
How AI Could Ease the Refugee Crisis and Bring New Talent to Businesses
says. “What we’re asking is, can we build algorithms that will help find better matches that will allow people to integrate more easily?” The paper presents data from Switzerland and the United States that showed promise in using machine... View Details
- April 2021
- Case
Distinct Software
By: Das Narayandas, Arijit Sengupta and Jonathan Wray
Distinct Software (disguised name), a global enterprise software company, is at an important point in its growth trajectory where the luster of its mantra of “grow and win at any cost” has dimmed with increasing competition and margin pressures. To help navigate its... View Details
Keywords: Artificial Intelligence; Marketing; Sales; Performance Productivity; Technological Innovation; AI and Machine Learning
Narayandas, Das, Arijit Sengupta, and Jonathan Wray. "Distinct Software." Harvard Business School Case 521-101, April 2021.
- March 2019
- Teaching Note
Numenta: Inventing and (or) Commercializing AI
By: David B. Yoffie
This teaching notes accompanies the Numenta case, HBS No. 716-469. The focus is how to scale a new artificial intelligence technology, how to build a platform and overcome chicken-or-the-egg problems, and how to utilize open source software and licensing. View Details
- September–October 2024
- Article
The Crowdless Future? Generative AI and Creative Problem-Solving
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; Generative Ai; Crowdsourcing; AI and Machine Learning; Creativity; Technological Innovation
Boussioux, Léonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "The Crowdless Future? Generative AI and Creative Problem-Solving." Organization Science 35, no. 5 (September–October 2024): 1589–1607.
- August 2024 (Revised March 2025)
- Case
DBS' AI Journey
By: Feng Zhu, Harold Zhu and Adina Wong
Headquartered in Singapore, DBS Bank, one of Asia's leading financial services groups, embarked on a multi-year digital transformation under CEO Piyush Gupta in 2014. It was then that DBS also began experimenting with AI to drive value for the business and customers.... View Details
Keywords: Corporate Governance; AI and Machine Learning; Digital Transformation; Risk Management; Value Creation; Banking Industry; Financial Services Industry; Asia; Singapore
Zhu, Feng, Harold Zhu, and Adina Wong. "DBS' AI Journey." Harvard Business School Case 625-053, August 2024. (Revised March 2025.)
- 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
Kwon, Caleb, Ananth Raman, and Jorge Tamayo. "Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions." Working Paper, April 2024.
- 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
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.