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Show Results For
- All HBS Web
(1,018)
- People (1)
- News (186)
- Research (659)
- Events (13)
- Multimedia (3)
- Faculty Publications (540)
- 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
Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 206 (April 2023).
- October 2024
- Technical Note
Prompt Engineering
By: Michael Parzen and Jo Ellery
This note covers the basics of prompt engineering, a key tool for making use of modern generative AI. We discuss the principles of prompt engineering and illustrate these principles with techniques for asking questions. We further list the types of prompts that can be... View Details
Parzen, Michael, and Jo Ellery. "Prompt Engineering." Harvard Business School Technical Note 625-056, October 2024.
- September 23, 2024
- Article
AI Wants to Make You Less Lonely. Does It Work?
De Freitas, Julian. "AI Wants to Make You Less Lonely. Does It Work?" Wall Street Journal (September 23, 2024), R.11.
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first... View Details
Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li. "Distributionally Robust Causal Inference with Observational Data." Working Paper, February 2023.
- December 18, 2024
- Article
Is AI the Right Tool to Solve That Problem?
By: Paolo Cervini, Chiara Farronato, Pushmeet Kohli and Marshall W Van Alstyne
While AI has the potential to solve major problems, organizations embarking on such journeys of often encounter obstacles. They include a dearth of high-quality data; too many possible solutions; the lack of a clear, measurable objective; and difficulty in identifying... View Details
Cervini, Paolo, Chiara Farronato, Pushmeet Kohli, and Marshall W Van Alstyne. "Is AI the Right Tool to Solve That Problem?" Harvard Business Review (website) (December 18, 2024).
- March 2024
- Exercise
'Storrowed': A Generative AI Exercise
By: Mitchell Weiss
"Storrowed" is an exercise to help participants raise their capacity and curiosity for generative AI. It focuses on generative AI for problem understanding and ideation, but can be adapted for use more broadly. Participants use generative AI tools to understand a... View Details
Weiss, Mitchell. "'Storrowed': A Generative AI Exercise." Harvard Business School Exercise 824-188, March 2024.
- Winter 2021
- Editorial
Introduction
This issue of Negotiation Journal is dedicated to the theme of artificial intelligence, technology, and negotiation. It arose from a Program on Negotiation (PON) working conference on that important topic held virtually on May 17–18. The conference was not the... View Details
Wheeler, Michael A. "Introduction." Special Issue on Artificial Intelligence, Technology, and Negotiation. Negotiation Journal 37, no. 1 (Winter 2021): 5–12.
- 2024
- Working Paper
Displacement or Complementarity? The Labor Market Impact of Generative AI
By: Wilbur Xinyuan Chen, Suraj Srinivasan and Saleh Zakerinia
Generative AI is poised to reshape the labor market, affecting cognitive and white-collar occupations in ways distinct from past technological revolutions. This study examines whether generative AI displaces workers or augments their jobs by analyzing labor demand and... View Details
Keywords: Generative Ai; Labor Market; Automation And Augmentation; Labor; AI and Machine Learning; Competency and Skills
Chen, Wilbur Xinyuan, Suraj Srinivasan, and Saleh Zakerinia. "Displacement or Complementarity? The Labor Market Impact of Generative AI." Harvard Business School Working Paper, No. 25-039, December 2024.
- 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.
- March 2025
- Case
Mobvoi’s Path Through Market Challenges and Business Reinvention
By: Paul A. Gompers and Shu Lin
Founded in 2012, Mobvoi evolved through multiple transformations—from AI-driven voice technology to smart wearables and later AI-generated content. Backed by major investors, the company navigated shifts in strategy while facing two failed IPO attempts. As market... View Details
- July–August 2021
- Article
Why You Aren't Getting More from Your Marketing AI
By: Eva Ascarza, Michael Ross and Bruce G.S. Hardie
Fewer than 40% of companies that invest in AI see gains from it, usually because of one or more of these errors: (1) They don’t ask the right question, and end up directing AI to solve the wrong problem. (2) They don’t recognize the differences between the value of... View Details
Keywords: Artificial Intelligence; Marketing; Decision Making; Communication; Framework; AI and Machine Learning
Ascarza, Eva, Michael Ross, and Bruce G.S. Hardie. "Why You Aren't Getting More from Your Marketing AI." Harvard Business Review 99, no. 4 (July–August 2021): 48–54.
- January 2025
- Technical Note
AI vs Human: Analyzing Acceptable Error Rates Using the Confusion Matrix
By: Tsedal Neeley and Tim Englehart
This technical note introduces the confusion matrix as a foundational tool in artificial intelligence (AI) and large language models (LLMs) for assessing the performance of classification models, focusing on their reliability for decision-making. A confusion matrix... View Details
Keywords: Reliability; Confusion Matrix; AI and Machine Learning; Decision Making; Measurement and Metrics; Performance
Neeley, Tsedal, and Tim Englehart. "AI vs Human: Analyzing Acceptable Error Rates Using the Confusion Matrix." Harvard Business School Technical Note 425-049, January 2025.
- September 2024
- Exercise
Finding Your 'Jagged Frontier': A Generative AI Exercise
By: Mitchell Weiss
In 2023 a set of scholars set out to study the effect of artificial intelligence (AI) on the quality and productivity of knowledge workers—in this specific instance, management consultants. They wanted to know across a range of tasks in a workflow, which, if any, would... View Details
Keywords: AI and Machine Learning; Performance Productivity; Performance Evaluation; Consulting Industry
Weiss, Mitchell. "Finding Your 'Jagged Frontier': A Generative AI Exercise." Harvard Business School Exercise 825-070, September 2024.
- November 2, 2021
- Article
The Cultural Benefits of Artificial Intelligence in the Enterprise
By: Sam Ransbotham, François Candelon, David Kiron, Burt LaFountain and Shervin Khodabandeh
The 2021 MIT SMR-BCG report identifies a wide range of AI-related cultural benefits at both the team and organizational levels. Whether it’s reconsidering business assumptions or empowering teams, managing the dynamics across culture, AI use, and organizational... View Details
Ransbotham, Sam, François Candelon, David Kiron, Burt LaFountain, and Shervin Khodabandeh. "The Cultural Benefits of Artificial Intelligence in the Enterprise." MIT Sloan Management Review, Big Ideas Artificial Intelligence and Business Strategy Initiative (website) (November 2, 2021). (Findings from the 2021 Artificial Intelligence and Business Strategy Global Executive Study and Research Project.)
- 20 Oct 2022 - 22 Oct 2022
- Talk
Stigma Against AI Companion Applications
By: Julian De Freitas, A. Ragnhildstveit and A.K. Uğuralp
- 2023
- Article
MoPe: Model Perturbation-based Privacy Attacks on Language Models
By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training... View Details
Li, Marvin, Jason Wang, Jeffrey Wang, and Seth Neel. "MoPe: Model Perturbation-based Privacy Attacks on Language Models." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2023): 13647–13660.
- 2023
- Article
Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
By: Suraj Srinivas, Sebastian Bordt and Himabindu Lakkaraju
One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite only being trained for classification, PAGs cause... View Details
Srinivas, Suraj, Sebastian Bordt, and Himabindu Lakkaraju. "Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 8 Sep 2023
- Conference Presentation
Chatbots and Mental Health: Insights into the Safety of Generative AI
By: Julian De Freitas, K. Uguralp, Z. Uguralp and Stefano Puntoni
De Freitas, Julian, K. Uguralp, Z. Uguralp, and Stefano Puntoni. "Chatbots and Mental Health: Insights into the Safety of Generative AI." Paper presented at the Business & Generative AI Workshop, Wharton School, AI at Wharton, San Francisco, CA, United States, September 8, 2023.
- Article
Why Boards Aren't Dealing with Cyberthreats
By: J. Yo-Jud Cheng and Boris Groysberg
Cheng, J. Yo-Jud, and Boris Groysberg. "Why Boards Aren't Dealing with Cyberthreats." Harvard Business Review (website) (February 22, 2017). (Excerpt featured in the Harvard Business Review. May–June 2017 "Idea Watch" section.)
- July 2024
- Article
AI, ROI, and Sales Productivity
Artificial intelligence (AI) is now a loose term for many different things and at the peak of its hype curve. So managers hitch-their-pitch to the term in arguing for resources. But like any technology, its business value depends upon actionable use cases embraced by... View Details
Cespedes, Frank V. "AI, ROI, and Sales Productivity." Top Sales Magazine (July 2024), 12–13.