Publications
Publications
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- All HBS Web
(3,734)
- Faculty Publications (399)
- October 2023 (Revised January 2025)
- Case
Ӧzyeğin Social Investments: A Legacy of Giving
By: Christina R. Wing, Zeshan Gondal and Brittany L. Logan
This case explores the work of Özyeğin Social Investments, founded by Hüsnü Özyeğin, one of Turkey's most successful entrepreneurs. With a focus on education, health, gender equality, rural development, and disaster relief in Turkey, Özyeğin Social Investments and the... View Details
- October 2023
- Teaching Note
Timnit Gebru: 'SILENCED No More' on AI Bias and The Harms of Large Language Models
By: Tsedal Neeley and Tim Englehart
Teaching Note for HBS Case No. 422-085. Dr. Timnit Gebru—a leading artificial intelligence (AI) computer scientist and co-lead of Google’s Ethical AI team—was messaging with one of her colleagues when she saw the words: “Did you resign?? Megan sent an email saying that... View Details
- October 2023
- Case
Driving Sustainability at AB InBev
By: Ethan Rouen and Antonio Manuel Oftelie
It was the height of the summer in 2022, and Michel Doukeris, the CEO of Anheuser-Busch InBev (AB InBev), and Peter Kraemer, the company’s Chief Supply Officer, gazed across the vast desert surrounding Zacatecas, Mexico. They were visiting their Grupo Modelo Brewery,... View Details
- 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
- September 2023
- Technical Note
Work Arrangements in the Post-Pandemic World
By: Leslie A. Perlow and Salvatore J. Affinito
Whether you are choosing your job based on geographic location, work arrangements (i.e., fully in-person, hybrid, fully virtual), or other unrelated factors (e.g., career, family, partner, hobbies), in the post-pandemic world you will likely be surrounded by people... View Details
- 2023
- Working Paper
Spatial Mobility, Economic Opportunity, and Crime
By: Gaurav Khanna, Carlos Medina, Anant Nyshadham, Daniel Ramos-Menchelli, Jorge Tamayo and Audrey Tiew
Neighborhoods are strong determinants of both economic opportunity and criminal activity. Does improving connectedness between segregated and unequal parts of a city predominantly import opportunity or export crime? We use a spatial general equilibrium framework to... View Details
- August 2023
- Case
Stay or Go? Sarah Reynolds at Kensington Partners
By: David G. Fubini, Amr Seifeldin and Patrick Sanguineti
Sarah Reynolds, a Partner at the global Kensington Partners strategy consulting firm, has headed the firm's Telecommunications Group for a few years. Thanks to her stellar track record with clients, she has brought the group, and herself, a range of accolades and... View Details
- 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
- May 2023 (Revised May 2023)
- Case
Stay or Go? Sarah Reynolds Kensington Partners
By: David G. Fubini, Amr Seifeldin and Patrick Sanguineti
Sarah Reynolds, a relatively new Partner at the global Kensington Partners strategy consulting firm, has headed the firm's Telecommunications Group for a few years. Thanks to her stellar track record with clients, she has brought the group a range of accolades and... View Details
- 2023
- Working Paper
The Politics of Philanthropy in China
By: Geoffrey Jones and Yuhai Wu
This working paper looks historically at business philanthropy in China. In the West, the literature has distinguished between entrepreneurial and customary philanthropy, while the phenomenon of spiritual philanthropy has been identified in many emerging markets. This... View Details
- April 2023 (Revised September 2023)
- Case
Levels: The Remote, Asynchronous, Deep Work Management System
By: Joseph B. Fuller and George Gonzalez
Levels is a highly innovative startup in the health care space. They intend to revolutionize health by linking behavior—eating, exercise, sleeping, etc.—to changes in metabolism. They believe metabolic health can be managed through careful monitoring of changes in... View Details
- April 2023
- Article
On the Privacy Risks of Algorithmic Recourse
By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details
- March 2023 (Revised March 2025)
- Case
Close Concerns: Diabetes Research and Advocacy
By: Regina Herzlinger and Brian L. Walker
Diagnosed with diabetes at the age of 18, Kelly Close understood the importance of balancing consistency and iteration. This principle had also informed her professional work, which started with a rapid promotion from financial analyst at Goldman Sachs to an analyst... View Details
- 2023
- Chapter
Marketing Through the Machine’s Eyes: Image Analytics and Interpretability
By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan
he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the... View Details
- 2023
- Working Paper
Remote Work across Jobs, Companies, and Space
By: Stephen Hansen, Peter John Lambert, Nick Bloom, Steven J. Davis, Raffaella Sadun and Bledi Taska
The pandemic catalyzed an enduring shift to remote work. To measure and characterize
this shift, we examine more than 250 million job vacancy postings across five
English-speaking countries. Our measurements rely on a state-of-the-art language-processing
framework... View Details
- January–February 2023
- Article
Data-Driven COVID-19 Vaccine Development for Janssen
By: Dimitris Bertsimas, Michael Lingzhi Li, Xinggang Liu, Jennings Xu and Najat Khan
The COVID-19 pandemic has spurred extensive vaccine research worldwide. One crucial part of vaccine development is the phase III clinical trial that assesses the vaccine for safety and efficacy in the prevention of COVID-19. In this work, we enumerate the first... View Details
- January–February 2023
- Article
The Overlooked Key to a Successful Scale-Up
By: Jeffrey F. Rayport, Davide Sola and Martin Kupp
Many start-ups experience enormous popularity and runaway growth, but only a few go on to become stable giants. What separates them from the pack? They all go through a developmental stage called extrapolation, say three business school professors.
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- 2022
- Article
OpenXAI: Towards a Transparent Evaluation of Model Explanations
By: Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik and Himabindu Lakkaraju
While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introduce OpenXAI, a comprehensive and extensible opensource framework for evaluating and... View Details
- 2022
- Article
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations
By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
A critical problem in the field of post hoc explainability is the lack of a common foundational goal among methods. For example, some methods are motivated by function approximation, some by game theoretic notions, and some by obtaining clean visualizations. This... View Details