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Show Results For
- All HBS Web
(670)
- News (144)
- Research (431)
- Events (20)
- Multimedia (12)
- Faculty Publications (311)
- 19 Dec 2023
- Research & Ideas
The 10 Most Popular Articles of 2023
life that includes rest, relationships, and a rewarding career. Is AI Coming for Your Job?In a post-AI world, where an algorithm can draft marketing copy—or even pop songs and movie scripts—anything seems possible. Harvard Business School... View Details
Keywords: by Danielle Kost
- 22 Feb 2024
- Research & Ideas
How to Make AI 'Forget' All the Private Data It Shouldn't Have
predictions about the world. And now, even though generative AI feels very different from making a simple prediction, at a technical level, that's really what it is. In order to train these predictive systems, you need lots of example data input and output pairs. The... View Details
- March 2022
- Article
Where to Locate COVID-19 Mass Vaccination Facilities?
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li and Alessandro Previero
The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near-end impact of the pandemic? In the United States in particular, the new... View Details
Keywords: Vaccines; COVID-19; Health Care and Treatment; Health Pandemics; Performance Effectiveness; Analytics and Data Science; Mathematical Methods
Bertsimas, Dimitris, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li, and Alessandro Previero. "Where to Locate COVID-19 Mass Vaccination Facilities?" Naval Research Logistics Quarterly 69, no. 2 (March 2022): 179–200.
- 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
Singh, Harvineet, Shalmali Joshi, Finale Doshi-Velez, and Himabindu Lakkaraju. "Towards Robust Off-Policy Evaluation via Human Inputs." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2022): 686–699.
- July 2024
- Article
How Artificial Intelligence Constrains Human Experience
By: A. Valenzuela, S. Puntoni, D. Hoffman, N. Castelo, J. De Freitas, B. Dietvorst, C. Hildebrand, Y.E. Huh, R. Meyer, M. Sweeney, S. Talaifar, G. Tomaino and K. Wertenbroch
Many consumption decisions and experiences are digitally mediated. As a consequence, consumer behavior is increasingly the joint product of human psychology and ubiquitous algorithms (Braun et al. 2024; cf. Melumad et al. 2020). The coming of age of Large Language... View Details
Keywords: Large Language Model; User Experience; AI and Machine Learning; Consumer Behavior; Technology Adoption; Risk and Uncertainty; Cost vs Benefits
Valenzuela, A., S. Puntoni, D. Hoffman, N. Castelo, J. De Freitas, B. Dietvorst, C. Hildebrand, Y.E. Huh, R. Meyer, M. Sweeney, S. Talaifar, G. Tomaino, and K. Wertenbroch. "How Artificial Intelligence Constrains Human Experience." Journal of the Association for Consumer Research 9, no. 3 (July 2024): 241–256.
- December 2016
- Article
Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud
By: Michael Luca and Georgios Zervas
Consumer reviews are now part of everyday decision making. Yet, the credibility of these reviews is fundamentally undermined when businesses commit review fraud, creating fake reviews for themselves or their competitors. We investigate the economic incentives to commit... View Details
Luca, Michael, and Georgios Zervas. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud." Management Science 62, no. 12 (December 2016): 3412–3427.
- 2015
- Working Paper
Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud
By: Michael Luca and Georgios Zervas
Consumer reviews are now part of everyday decision-making. Yet, the credibility of these reviews is fundamentally undermined when businesses commit review fraud, creating fake reviews for themselves or their competitors. We investigate the economic incentives to commit... View Details
Keywords: Information; Competition; Internet and the Web; Ethics; Reputation; Social and Collaborative Networks; Retail Industry; Food and Beverage Industry
Luca, Michael, and Georgios Zervas. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud." Working Paper. (May 2015. Revise and resubmit, Management Science.)
- 2021
- Working Paper
Time Dependency, Data Flow, and Competitive Advantage
Data is fundamental to machine learning-based products and services and is considered strategic due to its externalities for businesses, governments, non-profits, and more generally for society. It is renowned that the value of organizations (businesses, government... View Details
Keywords: Economics Of AI; Value Of Data; Perishability; Time Dependency; Flow Of Data; Data Strategy; Analytics and Data Science; Value; Strategy; Competitive Advantage
Valavi, Ehsan, Joel Hestness, Marco Iansiti, Newsha Ardalani, Feng Zhu, and Karim R. Lakhani. "Time Dependency, Data Flow, and Competitive Advantage." Harvard Business School Working Paper, No. 21-099, March 2021.
- 30 Nov 2010
- Working Paper Summaries
Sponsored Links’ or ’Advertisements’?: Measuring Labeling Alternatives in Internet Search Engines
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- Web
Africa - Global
Regina Wittenberg-Moerman To mitigate information asymmetry about borrowers in developing economies, digital lenders use machine-learning algorithms and nontraditional data from borrowers’ mobile devices. Consequently, digital lenders... View Details
- 28 Feb 2018
- HBS Seminar
Kartik Hosanagar, Wharton, University of Pennsylvania
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data,... View Details
- 2021
- Article
To Thine Own Self Be True? Incentive Problems in Personalized Law
By: Jordan M. Barry, John William Hatfield and Scott Duke Kominers
Recent years have seen an explosion of scholarship on “personalized law.” Commentators foresee a world in which regulators armed with big data and machine learning techniques determine the optimal legal rule for every regulated party, then instantaneously disseminate... View Details
Keywords: Personalized Law; Regulation; Regulatory Avoidance; Regulatory Arbitrage; Law And Economics; Law And Technology; Law And Artificial Intelligence; Futurism; Moral Hazard; Elicitation; Signaling; Privacy; Law; Governing Rules, Regulations, and Reforms; Information Technology; AI and Machine Learning
Barry, Jordan M., John William Hatfield, and Scott Duke Kominers. "To Thine Own Self Be True? Incentive Problems in Personalized Law." Art. 2. William & Mary Law Review 62, no. 3 (2021).
- 01 Dec 2023
- News
Thinking Ahead
Lab at the Digital Data Design Institute at Harvard; his research develops tools for machine learning that mitigate bias and enhance privacy. Generative AI poses a greater risk to privacy by its nature, Neel explains. A traditional machine-learning View Details
- 22 May 2019
- Blog Post
What is FIELD Global Immersion?
to travel based on where their home country is, and where they have extensive travel or professional experience. With these considerations in mind, country and team assignments (aka: Global Section assignments) are made via an algorithm... View Details
- 07 Feb 2022
- Research & Ideas
Digital Transformation: A New Roadmap for Success
algorithms can lead to unintended bias that harms certain employees and customers, and the company’s reputation (a bias story can go viral on social media within minutes). 5. Design for inclusive and agile problem-solving As they become... View Details
- Web
Research - Global
digital lenders use machine-learning algorithms and nontraditional data from borrowers’ mobile devices. Consequently, digital lenders have managed to expand access to credit for millions of... 2025 Working Paper How Does Wage Inequality... View Details
- 18 Feb 2025
- HBS Seminar
Andrey Simonov, Columbia University
- 03 Jan 2017
- First Look
January 3, 2017
Winter 2017 MIT Sloan Management Review Why Big Data Isn't Enough By: Chai, Sen, and Willy C. Shih Abstract—There is a growing belief that sophisticated algorithms can explore huge databases and find relationships independent of any... View Details
Keywords: Carmen Nobel