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- January–February 2025
- Article
What People Still Get Wrong About Negotiations: They Assume the Size of the Pie Is Fixed—and So Miss Opportunities to Create Value
By: Max H. Bazerman
Most executives leave value on the negotiating table, for two main reasons: First, many executives mistakenly believe that they’re negotiating over a fixed pie and that gains for one side necessarily mean losses for the other. Second, they focus exclusively on how to... View Details
Bazerman, Max H. "What People Still Get Wrong About Negotiations: They Assume the Size of the Pie Is Fixed—and So Miss Opportunities to Create Value." Harvard Business Review 103, no. 1 (January–February 2025): 71–77.
- December 2024
- Case
WayCool: Addressing Food Loss
By: Rajiv Lal and Kairavi Dey
Headquartered in Chennai, India, WayCool started by focusing on solving the growing challenge of food losses and low farmer incomes. Dasari and Jayaraman, surprised at the amount of fruits and vegetables lost in the agricultural supply chain, set up the company with... View Details
- 2024
- Working Paper
Categorical Processing in a Complex World
By: Marco Sammon, Thomas Graeber and Christopher Roth
In real-world news environments, quantitative information is rarely presented in isolation; it is characterized through qualitative comparisons with various reference levels. Company earnings, for example, are commonly compared to analyst forecasts, previous earnings,... View Details
- 2024
- Article
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
By: Michael Lingzhi Li and Kosuke Imai
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across... View Details
Li, Michael Lingzhi, and Kosuke Imai. "Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules." Journal of Causal Inference 12, no. 1 (2024).
- June 2024
- Case
Growing Foodology into Latin America's Largest Platform for Virtual Restaurants
By: Jorge Tamayo, Rembrand Koning and Jenyfeer Martinez Buitrago
This case delves into the expansion strategy of Foodology, a cloud kitchen startup based in Bogotá that operated across four Latin American countries (Colombia, Brazil, Mexico, and Peru). Co-founders Daniela Izquierdo and Juan Guillermo Azuero (both HBS, 2019) grappled... View Details
Keywords: Entrepreneurship; Food; Digital Platforms; Product Launch; Growth and Development Strategy; Business Strategy; Business Model; Business Startups; Profit; Marketing Strategy; Expansion; Diversification; Food and Beverage Industry; Latin America; South America; Colombia; Brazil; Mexico; Peru
Tamayo, Jorge, Rembrand Koning, and Jenyfeer Martinez Buitrago. "Growing Foodology into Latin America's Largest Platform for Virtual Restaurants." Harvard Business School Case 724-393, June 2024.
- June 2024
- Article
Real Growth in Space Manufacturing Output Substantially Exceeds Growth in the Overall Space Economy
By: Tina Highfill and Matthew Weinzierl
Accurately measuring real economic output in the space economy is made difficult by the rapid increase in capabilities and decrease in prices of launch and satellite technologies achieved over the past two decades. Nominal measures of output in space will tend to... View Details
Highfill, Tina, and Matthew Weinzierl. "Real Growth in Space Manufacturing Output Substantially Exceeds Growth in the Overall Space Economy." Acta Astronautica 219 (June 2024): 236–242.
- April 2024
- Case
Managing AI Risks in Consumer Banking
By: Suraj Srinivasan, Satish Tadikonda, Paul Dongha, Manoj Saxena and Radhika Kak
In early 2024, Ruth Jones, head of digital banking at Signa Bank, a (fictitious) European consumer bank, was thinking about how to best incorporate GenAI capabilities to improve efficiencies and create new ways to improve the customer experience. Where were the biggest... View Details
Keywords: Customer Relationship Management; AI and Machine Learning; Risk Management; Opportunities; Customization and Personalization; Banking Industry; Europe
Srinivasan, Suraj, Satish Tadikonda, Paul Dongha, Manoj Saxena, and Radhika Kak. "Managing AI Risks in Consumer Banking." Harvard Business School Case 124-093, April 2024.
- 2024
- Working Paper
The Cram Method for Efficient Simultaneous Learning and Evaluation
By: Zeyang Jia, Kosuke Imai and Michael Lingzhi Li
We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and tests its empirical... View Details
Keywords: AI and Machine Learning
Jia, Zeyang, Kosuke Imai, and Michael Lingzhi Li. "The Cram Method for Efficient Simultaneous Learning and Evaluation." Working Paper, March 2024.
- 2024
- Working Paper
Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference
By: Michael Lindon, Dae Woong Ham, Martin Tingley and Iavor I. Bojinov
Linear regression adjustment is commonly used to analyze randomized controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to... View Details
Lindon, Michael, Dae Woong Ham, Martin Tingley, and Iavor I. Bojinov. "Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference." Harvard Business School Working Paper, No. 24-060, March 2024.
- January 2024 (Revised February 2024)
- Case
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
The case examines Levi Strauss’ journey in implementing machine learning and AI into its financial forecasting process. The apparel company partnered with the IT company Wipro in 2017 to develop a machine learning algorithm that could help Levi Strauss forecast its... View Details
Keywords: Investor Relations; Forecasting; Machine Learning; Artificial Intelligence; Apparel; Corporate Finance; Forecasting and Prediction; AI and Machine Learning; Digital Transformation; Apparel and Accessories Industry; United States
Egan, Mark. "Data-Driven Denim: Financial Forecasting at Levi Strauss." Harvard Business School Case 224-029, January 2024. (Revised February 2024.)
- December 2023 (Revised January 2025)
- Case
Research In Motion: Launching and Scaling the World's First Smartphone Empire (A)
By: Tatiana Sandino and Samuel Grad
In 2005, Research In Motion’s (RIM) BlackBerry smartphone was a sensation. After its launch in 1999, the groundbreaking BlackBerry had captured the hearts and minds of corporate America through its secure wireless email service. The device was so addictive and... View Details
Keywords: Business Growth and Maturation; Decision Choices and Conditions; Mobile and Wireless Technology; Innovation and Management; Technological Innovation; Business or Company Management; Management Style; Product Development; Managerial Roles; Growth and Development Strategy; Technology Industry; United States; Canada
Sandino, Tatiana, and Samuel Grad. "Research In Motion: Launching and Scaling the World's First Smartphone Empire (A)." Harvard Business School Case 124-023, December 2023. (Revised January 2025.)
- October 2023 (Revised November 2023)
- Case
Recycle & Re-Match: The Future of Soccer Turfs
By: George Serafeim, Lena Duchene and Carlota Moniz
By August 2023, Re-Match, an artificial turf waste-to-value company, had operations in Denmark and the Netherlands and had recycled over 160,000 tons of waste and plastic fiber. With recent capital injection from the VC firm Verdane and a dual revenue business model,... View Details
Keywords: Carbon Emissions; Carbon Abatement; Sustainability; Recycling; Waste Management; Technology; Entrepreneurial Management; Business Growth and Maturation; Business Model; Decisions; Energy Conservation; Investment Return; Profit; Technological Innovation; Patents; Growth and Development Strategy; Market Entry and Exit; Digital Platforms; Wastes and Waste Processing; Business Strategy; Competition; Expansion; Technology Adoption; Sports; Environmental Sustainability; Entrepreneurship; Green Technology Industry; Service Industry; Manufacturing Industry; Rubber Industry; Sports Industry; Denmark; Netherlands; France; United States; Pennsylvania; Europe
Serafeim, George, Lena Duchene, and Carlota Moniz. "Recycle & Re-Match: The Future of Soccer Turfs." Harvard Business School Case 124-032, October 2023. (Revised November 2023.)
- 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
Olagoke, Lukman, Salil Vadhan, and Seth Neel. "Black-box Training Data Identification in GANs via Detector Networks." Working Paper, October 2023.
- 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
Pawelczyk, Martin, Seth Neel, and Himabindu Lakkaraju. "In-Context Unlearning: Language Models as Few Shot Unlearners." Working Paper, October 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
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.
- August 2023
- Supplement
Reimagining Hindustan Unilever (B)
By: Sunil Gupta and Rachna Tahilyani
In April 2023, as the CEO and MD of Hindustan Unilever (HUL), India’s largest fast-moving consumer goods (FMCG) firm, prepared to hand over the firm’s reins to his successor, he proudly reflected on the last decade. His quest to digitally transform HUL into an... View Details
- 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
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
- 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
Menghani, Neil, Edward McFowland III, and Daniel B. Neill. "Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness." Working Paper, June 2023.
- 2023
- Article
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means... View Details
Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. "Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse." Proceedings of the International Conference on Learning Representations (ICLR) (2023).
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details
Chang, Peter W., Leor Fishman, and Seth Neel. "Feature Importance Disparities for Data Bias Investigations." Working Paper, March 2023.