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- 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
- 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
- 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
- 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
- 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
- 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
- December 2023 (Revised December 2023)
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 2022
- Article
Efficiently Training Low-Curvature Neural Networks
By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
Standard deep neural networks often have excess non-linearity, making them susceptible to issues such as low adversarial robustness and gradient instability. Common methods to address these downstream issues, such as adversarial training, are expensive and often... View Details
Keywords: AI and Machine Learning
- November–December 2022
- Article
Can AI Really Help You Sell?: It Can, Depending on When and How You Implement It
By: Jim Dickie, Boris Groysberg, Benson P. Shapiro and Barry Trailer
Many salespeople today are struggling; only 57% of them make their annual quotas, surveys show. One problem is that buying processes have evolved faster than selling processes, and buyers today can access a wide range of online resources that let them evaluate products... View Details
- September 2022
- Technical Note
Addressing Social Determinants of Health in the American Landscape
By: Susanna Gallani and Jacob Riegler
Social determinants of health (SDOH) have gained significant attention in recent years. A growing body of research shows that a person’s health is influenced by a large number of non-genetic factors, most of which operate outside the realm of health care and are... View Details
- 2022
- Article
Towards Robust Off-Policy Evaluation via Human Inputs
By: Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez and Himabindu Lakkaraju
Off-policy Evaluation (OPE) methods are crucial tools for evaluating policies in high-stakes domains such as healthcare, where direct deployment is often infeasible, unethical, or expensive. When deployment environments are expected to undergo changes (that is, dataset... View Details