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- All HBS Web
(2,274)
- People (7)
- News (503)
- Research (1,403)
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- Faculty Publications (544)
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- May 2024
- Teaching Note
AI21 Labs in 2023: Strategy for Generative AI
By: David Yoffie
Teaching Note for HBS Case 724-383. The case has 3 important teaching purposes: First, what are the advantages and disadvantages of imitation? (e.g., Should AI21 imitate OpenAI with a chatbot?) Second, what are the advantages and disadvantages of keeping new technology... View Details
- Article
Temporary General Equilibrium in a Sequential Trading Model with Spot and Futures Transactions
By: Jerry R. Green
The existence of an equilibrium is proven for a two-period model in which there are spot transactions and futures transactions in the first period and spot markets in the second period. Prices at that date are viewed with subjective uncertainty by all traders. This... View Details
Green, Jerry R. "Temporary General Equilibrium in a Sequential Trading Model with Spot and Futures Transactions." Econometrica 41, no. 6 (November 1973): 1103–1123.
- 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
Keywords: AI; Artificial Intelligence; Model; Hardware; Data Centers; AI and Machine Learning; Applications and Software; Analytics and Data Science; Value
Wu, Andy, and Matt Higgins. "Generative AI Value Chain." Harvard Business School Background Note 724-355, July 2023. (Revised July 2023.)
- 2024
- Working Paper
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; Crowdsourcing; Generative Ai; Creative Problem-solving; Organizational Search; AI-in-the-loop; Prompt Engineering; AI and Machine Learning; Innovation and Invention
Boussioux, Léonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "The Crowdless Future? Generative AI and Creative Problem Solving." Harvard Business School Working Paper, No. 24-005, July 2023. (Revised July 2024.)
- March 1999 (Revised February 2000)
- Case
Patient Care Delivery Model at the Massachusetts General Hospital, The
By: Amy C. Edmondson, Richard M.J. Bohmer and Emily Heaphy
Examines the implementation of a new patient care delivery model at Massachusetts General Hospital. Uses clinical and financial data to examine different choices for staffing non-physician health care professionals and to understand the challenges of managing change... View Details
Keywords: Change Management; Service Delivery; Health Care and Treatment; Health Industry; Massachusetts
Edmondson, Amy C., Richard M.J. Bohmer, and Emily Heaphy. "Patient Care Delivery Model at the Massachusetts General Hospital, The." Harvard Business School Case 699-154, March 1999. (Revised February 2000.)
- 2020
- Working Paper
A General Theory of Identification
By: Iavor Bojinov and Guillaume Basse
What does it mean to say that a quantity is identifiable from the data? Statisticians seem to agree
on a definition in the context of parametric statistical models — roughly, a parameter θ in a model
P = {Pθ : θ ∈ Θ} is identifiable if the mapping θ 7→ Pθ is injective.... View Details
Bojinov, Iavor, and Guillaume Basse. "A General Theory of Identification." Harvard Business School Working Paper, No. 20-086, February 2020.
- Article
Learning Models for Actionable Recourse
By: Alexis Ross, Himabindu Lakkaraju and Osbert Bastani
As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely... View Details
Ross, Alexis, Himabindu Lakkaraju, and Osbert Bastani. "Learning Models for Actionable Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 1995
- Chapter
Dynamic General Equilibrium Models with Imperfectly Competitive Product Markets
By: Julio J. Rotemberg and Michael Woodford
- August 1976
- Article
A Model of Economic Growth With Altruism Between Generations
By: Elon Kohlberg
Kohlberg, Elon. "A Model of Economic Growth With Altruism Between Generations." Journal of Economic Theory 13, no. 1 (August 1976): 1–13.
- June 2015
- Supplement
Generating Higher Value at IBM (A): EPS Forecasting Model
By: Benjamin C. Esty and Scott Mayfield
This case analyzes IBM's financial performance and its capital allocation decisions over a 10-year period from 2004-2013, during which IBM returned more than $140B to shareholders through a combination of dividends and share repurchases. During this time, CEO Sam... View Details
- January 2014 (Revised December 2014)
- Case
GenapSys: Business Models for the Genome
By: Richard G. Hamermesh, Joseph B. Fuller and Matthew Preble
GenapSys, a California-based startup, was soon to release a new DNA sequencer that the company's founder, Hesaam Esfandyarpour, believed was truly revolutionary. The sequencer would be substantially less expensive—potentially costing just a few thousand dollars—and... View Details
Keywords: DNA Sequencing; Life Sciences; Business Model; Innovation ＆ Entrepreneurship; Health Care and Treatment; Genetics; Business Strategy; Biotechnology Industry; Pharmaceutical Industry; Technology Industry; Health Industry; Medical Devices and Supplies Industry; United States
Hamermesh, Richard G., Joseph B. Fuller, and Matthew Preble. "GenapSys: Business Models for the Genome." Harvard Business School Case 814-050, January 2014. (Revised December 2014.)
- Article
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
By: Dylan Slack, Sophie Hilgard, Sameer Singh and Himabindu Lakkaraju
As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by... View Details
Keywords: Black Box Explanations; Bayesian Modeling; Decision Making; Risk and Uncertainty; Information Technology
Slack, Dylan, Sophie Hilgard, Sameer Singh, and Himabindu Lakkaraju. "Reliable Post hoc Explanations: Modeling Uncertainty in Explainability." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- Article
Faithful and Customizable Explanations of Black Box Models
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec
As predictive models increasingly assist human experts (e.g., doctors) in day-to-day decision making, it is crucial for experts to be able to explore and understand how such models behave in different feature subspaces in order to know if and when to trust them. To... View Details
Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Jure Leskovec. "Faithful and Customizable Explanations of Black Box Models." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2019).
- 2024
- Conference Paper
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
Large Language Models (LLMs) are increasingly used as powerful tools for several
high-stakes natural language processing (NLP) applications. Recent prompting
works claim to elicit intermediate reasoning steps and key tokens that serve as
proxy explanations for LLM... View Details
Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 2024.
- January 2016 (Revised November 2018)
- Case
Match Next: Next Generation Middle School?
By: John J-H Kim and Daniel Goldberg
This case is set in 2015 as a team at Match Education, a high performing charter middle school in Boston, explores new staffing and technology approaches in their quest to obtain what they term "jaw dropping" results. The team hopes to test and model for other schools... View Details
Keywords: General Management; K-12; Charter Schools; Public Schools; Edtech; Education; Information Technology; Management; Public Sector; Entrepreneurship; Education Industry; Boston
Kim, John J-H, and Daniel Goldberg. "Match Next: Next Generation Middle School?" Harvard Business School Case 316-138, January 2016. (Revised November 2018.)
- 2022
- Working Paper
TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet they have become more complex and harder to understand. To address this issue, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability... View Details
Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations." Working Paper, 2022.
- April 2000
- Teaching Note
Patient Care Delivery Model at the Massachusetts General Hospital, The TN
By: Amy C. Edmondson, Richard M.J. Bohmer and Emily Heaphy
Teaching Note for (9-699-154). View Details
- 2023
- Article
Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset
By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
Keywords: Large Language Model; AI and Machine Learning; Analytics and Data Science; Health Industry
Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- November–December 2023
- Article
Keep Your AI Projects on Track
By: Iavor Bojinov
AI—and especially its newest star, generative AI—is today a central theme in corporate boardrooms, leadership discussions, and casual exchanges among employees eager to supercharge their productivity. Sadly, beneath the aspirational headlines and tantalizing potential... View Details
Keywords: Generative Models; AI and Machine Learning; Success; Failure; Product Development; Technology Adoption
Bojinov, Iavor. "Keep Your AI Projects on Track." Harvard Business Review 101, no. 6 (November–December 2023): 53–59.
- 2015
- Chapter
Reliable Sustainability Ratings: The Influence of Business Models on Information Intermediaries
By: Robert G. Eccles, Jock Herron and George Serafeim
A new generation of corporate reporting—integrated reporting—is emerging that will help investors and other key stakeholders such as employees, customers, suppliers, and NGOs develop a deeper and more comprehensive appreciation of corporate performance than what is... View Details
Eccles, Robert G., Jock Herron, and George Serafeim. "Reliable Sustainability Ratings: The Influence of Business Models on Information Intermediaries." Chap. 48 in The Routledge Handbook of Responsible Investment, edited by Tessa Hebb, James Hawley, Andreas Hoepner, Agnes Neher, and David Wood. Routledge, 2015.