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(658)
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Show Results For
- All HBS Web
(658)
- News (145)
- Research (423)
- Events (19)
- Multimedia (12)
- Faculty Publications (302)
- November 2024 (Revised January 2025)
- Case
MiDAS: Automating Unemployment Benefits
By: Shikhar Ghosh and Shweta Bagai
In 2015, the state of Michigan considered whether to nominate its Michigan Integrated Data Automated System (MiDAS) for a prestigious state technology award. Launched in 2013 amid severe budget pressures, the $47 million automated fraud detection system was designed to... View Details
Keywords: Artificial Intelligence; AI; Machine Learning Models; Algorithmic Data; Decision Making; Automation; Benefits; Compensation; Cost Reduction; Digital Transformation; Employment; Government; Fraud; Government Technology; Public Sector; Systems; Systems Integration; Unemployment Insurance; Waste Heat Recovery; AI and Machine Learning; Government Administration; Information Technology; Insurance; United States
- 16 Apr 2019
- News
Fewer small businesses expected to hire new employees in 2019
- 28 Mar 2018
- HBS Seminar
Tim O’Reilly, O’Reilly Media
- Article
How to Use Heuristics for Differential Privacy
By: Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
We develop theory for using heuristics to solve computationally hard problems in differential privacy. Heuristic approaches have enjoyed tremendous success in machine learning, for which performance can be empirically evaluated. However, privacy guarantees cannot be... View Details
Neel, Seth, Aaron Leon Roth, and Zhiwei Steven Wu. "How to Use Heuristics for Differential Privacy." Proceedings of the IEEE Annual Symposium on Foundations of Computer Science (FOCS) 60th (2019).
- 11 May 2021
- News
Law Firms Are Building A.I. Expertise as Regulation Looms
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising... View Details
- 09 Nov 2021
- HBS Seminar
Susan Murphy, Harvard-SEAS
- 2025
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.
- 2023
- Article
On the Impact of Actionable Explanations on Social Segregation
By: Ruijiang Gao and Himabindu Lakkaraju
As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse. To this end, there has been a growing body of research... View Details
Gao, Ruijiang, and Himabindu Lakkaraju. "On the Impact of Actionable Explanations on Social Segregation." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 10727–10743.
- 2010
- Chapter
Deferred Acceptance Algorithms: History, Theory, Practice
By: Alvin E. Roth
The deferred acceptance algorithm proposed by Gale and Shapley (1962) has had a profound influence on market design, both directly, by being adapted into practical matching mechanisms, and indirectly, by raising new theoretical questions. Deferred acceptance algorithms... View Details
- 05 Oct 2015
- Working Paper Summaries
Online Network Revenue Management Using Thompson Sampling
- 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.
- 17 Jan 2020
- News
Review: Competing in the Digital Age
- February 2011 (Revised February 2012)
- Case
Online Marketing at Big Skinny
By: Benjamin Edelman and Scott Duke Kominers
Describes a wallet maker's application of seven Internet marketing technologies: display ads, algorithmic search, sponsored search, social media, interactive content, online distributors, and A/B testing. Provides concise introductions to the key features of each... View Details
Keywords: Advertising Campaigns; Digital Marketing; Resource Allocation; Marketing Strategy; Performance Evaluation; Internet and the Web; Retail Industry
Edelman, Benjamin, and Scott Duke Kominers. "Online Marketing at Big Skinny." Harvard Business School Case 911-033, February 2011. (Revised February 2012.) (request a courtesy copy.)
Jeremy Yang
Jeremy Yang is an Assistant Professor of Business Administration in the Marketing Unit at Harvard Business School. He teaches Marketing in the MBA required curriculum. He develops data products for... View Details
- September 2017
- Article
It Doesn't Hurt to Ask: Question-asking Increases Liking
By: K. Huang, M. Yeomans, A.W. Brooks, J. Minson and F. Gino
Conversation is a fundamental human experience, one that is necessary to pursue intrapersonal and interpersonal goals across myriad contexts, relationships, and modes of communication. In the current research, we isolate the role of an understudied conversational... View Details
Keywords: Question-asking; Liking; Responsiveness; Conversation; Natural Language Processing; Interpersonal Communication; Behavior
Huang, K., M. Yeomans, A.W. Brooks, J. Minson, and F. Gino. "It Doesn't Hurt to Ask: Question-asking Increases Liking." Journal of Personality and Social Psychology 113, no. 3 (September 2017): 430–452.
- Forthcoming
- Article
Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by... View Details
Keywords: AI and Machine Learning; Analytics and Data Science; Forecasting and Prediction; Digital Marketing
Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation." Management Science (forthcoming).