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Show Results For
- All HBS Web
(694)
- News (145)
- Research (424)
- Events (20)
- Multimedia (12)
- Faculty Publications (308)
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- Article
Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting
By: Raymond H. Mak, Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani and Eva C. Guinan
Importance: Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global demand. One key physician task in RT planning involves tumor segmentation for targeting, which requires substantial... View Details
Keywords: Crowdsourcing; AI Algorithms; Health Care and Treatment; Collaborative Innovation and Invention; AI and Machine Learning
Mak, Raymond H., Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani, and Eva C. Guinan. "Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting." JAMA Oncology 5, no. 5 (May 2019): 654–661.
- Mar 2021
- Conference Presentation
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
By: Seth Neel, Aaron Leon Roth and Saeed Sharifi-Malvajerdi
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 both... View Details
Neel, Seth, Aaron Leon Roth, and Saeed Sharifi-Malvajerdi. "Descent-to-Delete: Gradient-Based Methods for Machine Unlearning." Paper presented at the 32nd Algorithmic Learning Theory Conference, March 2021.
- Article
The Pitfalls of Pricing Algorithms: Be Mindful of How They Can Hurt Your Brand
By: Marco Bertini and Oded Koenigsberg
More and more companies are relying on pricing algorithms to maximize profits. The use of artificial intelligence and machine learning enables real-time price adjustments based on supply and demand, competitors’ activities, delivery schedules, and so forth. But... View Details
Keywords: Algorithmic Pricing; Dynamic Pricing; Price; Change; Information Technology; Brands and Branding; Perception; Consumer Behavior
Bertini, Marco, and Oded Koenigsberg. "The Pitfalls of Pricing Algorithms: Be Mindful of How They Can Hurt Your Brand." Harvard Business Review 99, no. 5 (September–October 2021): 74–83.
- Article
Oracle Efficient Private Non-Convex Optimization
By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it.... View Details
Neel, Seth, Aaron Leon Roth, Giuseppe Vietri, and Zhiwei Steven Wu. "Oracle Efficient Private Non-Convex Optimization." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).
- 2020
- Working Paper
Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion
By: Ryan Allen and Prithwiraj Choudhury
Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented work performance. To reconcile these perspectives, we theorize that domain experience affects algorithm-augmented performance via two distinct countervailing... View Details
Keywords: Automation; Domain Experience; Algorithmic Aversion; Experts; Algorithms; Machine Learning; Decision-making; Future Of Work; Employees; Experience and Expertise; Decision Making; Performance
Allen, Ryan, and Prithwiraj Choudhury. "Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion." Harvard Business School Working Paper, No. 21-073, October 2020. (Revised September 2021.)
- September 2020 (Revised June 2023)
- Supplement
Spreadsheet Supplement to Artea Teaching Note
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to Artea Teaching Note 521-041. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and... View Details
- Research Summary
Overview
Dr. Logg studies how people can improve the accuracy of their judgments and decisions. Her main program of work examines when people are most likely to leverage the power of algorithms to improve their accuracy. Research on what she calls “theory of machine” is... View Details
- 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).
- October 2023 (Revised June 2024)
- Case
ReUp Education: Can AI Help Learners Return to College?
By: Kris Ferreira, Christopher Thomas Ryan and Sarah Mehta
Founded in 2015, ReUp Education helps “stopped out students”—learners who have stopped making progress towards graduation—achieve their college completion goals. The company relies on a team of success coaches to engage with learners and help them reenroll. In 2019,... View Details
Keywords: AI; Algorithms; Machine Learning; Edtech; Education Technology; Analysis; Higher Education; AI and Machine Learning; Customization and Personalization; Failure; Education Industry; Technology Industry; United States
Ferreira, Kris, Christopher Thomas Ryan, and Sarah Mehta. "ReUp Education: Can AI Help Learners Return to College?" Harvard Business School Case 624-007, October 2023. (Revised June 2024.)
- 2025
- Working Paper
Pricing with Bandits in the Long-tail: The Role of Competitive Monitoring
By: Ayelet Israeli and Eric Anderson
Most e-commerce retailers offer a long-tail of very low demand products. Individually, these items may have low sales but collectively they are critical to the overall e-commerce business model. Because of their minimal sales, pricing is a constant challenge. The... View Details
Israeli, Ayelet, and Eric Anderson. "Pricing with Bandits in the Long-tail: The Role of Competitive Monitoring." Working Paper, July 2025.
- September 2014
- Supplement
Netflix: Designing the Netflix Prize (B)
By: Karim R. Lakhani, Wesley M. Cohen, Kynon Ingram, Tushar Kothalkar, Maxim Kuzemchenko, Santosh Malik, Cynthia Meyn, Stephanie Healy Pokrywa and Greta Friar
This supplemental case follows up on the Netflix Prize Contest described in Netflix: Designing the Netflix Prize (A). In the A case, Netflix CEO Reed Hastings must decide how to organize a crowdsourcing contest to improve the algorithms for Netflix's movie... View Details
Keywords: Crowdsourcing; Prizes; Digitization; Algorithms; Recommendation Software; Disruption; Transformation; Collaborative Innovation and Invention; Technological Innovation; Knowledge Sharing; Applications and Software
Lakhani, Karim R., Wesley M. Cohen, Kynon Ingram, Tushar Kothalkar, Maxim Kuzemchenko, Santosh Malik, Cynthia Meyn, Stephanie Healy Pokrywa, and Greta Friar. "Netflix: Designing the Netflix Prize (B)." Harvard Business School Supplement 615-025, September 2014.
- March 2021
- Supplement
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Power Point Supplement to Teaching Note for HBS No. 521-021,521-022,521-037,521-043. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on... View Details
Keywords: Targeted Advertising; Targeting; Algorithmic Data; Bias; A/B Testing; Experiment; Advertising; Gender; Race; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
- August 2014
- Case
Netflix: Designing the Netflix Prize (A)
By: Karim R. Lakhani, Wesley M. Cohen, Kynon Ingram, Tushar Kothalkar, Maxim Kuzemchenko, Santosh Malik, Cynthia Meyn, Greta Friar and Stephanie Healy Pokrywa
In 2006, Reed Hastings, CEO of Netflix, was looking for a way to solve Netflix's customer churn problem. Netflix used Cinematch, its proprietary movie recommendation software, to promote individually determined best-fit movies to customers. Hastings determined that a... View Details
Keywords: Crowdsourcing; Prizes; Digitization; Algorithms; Recommendation Software; Disruption; Transformation; Collaborative Innovation and Invention; Technological Innovation; Knowledge Sharing; Applications and Software; Motion Pictures and Video Industry; Entertainment and Recreation Industry; Technology Industry; United States
Lakhani, Karim R., Wesley M. Cohen, Kynon Ingram, Tushar Kothalkar, Maxim Kuzemchenko, Santosh Malik, Cynthia Meyn, Greta Friar, and Stephanie Healy Pokrywa. "Netflix: Designing the Netflix Prize (A)." Harvard Business School Case 615-015, August 2014.
- Research Summary
Ethics & Politics of Emerging Technologies
In this stream of research, my collaborators and I investigate the ethical, political, and social implications of computational technologies.
In this work, I often collaborate with academic colleagues in computer science by helping to... 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
Zhang, Shunyuan, Flora Feng, and Kannan Srinivasan. "Marketing Through the Machine’s Eyes: Image Analytics and Interpretability." Chap. 8 in Artificial Intelligence in Marketing. 20, edited by Naresh K. Malhotra, K. Sudhir, and Olivier Toubia, 217–238. Review of Marketing Research. Emerald Publishing Limited, 2023.
- July–August 2023
- Article
Demand Learning and Pricing for Varying Assortments
By: Kris Ferreira and Emily Mower
Problem Definition: We consider the problem of demand learning and pricing for retailers who offer assortments of substitutable products that change frequently, e.g., due to limited inventory, perishable or time-sensitive products, or the retailer’s desire to... View Details
Keywords: Experiments; Pricing And Revenue Management; Retailing; Demand Estimation; Pricing Algorithm; Marketing; Price; Demand and Consumers; Mathematical Methods
Ferreira, Kris, and Emily Mower. "Demand Learning and Pricing for Varying Assortments." Manufacturing & Service Operations Management 25, no. 4 (July–August 2023): 1227–1244. (Finalist, Practice-Based Research Competition, MSOM (2021) and Finalist, Revenue Management & Pricing Section Practice Award, INFORMS (2019).)
- January–February 2020
- Article
Competing in the Age of AI
By: Marco Iansiti and Karim R. Lakhani
Today’s markets are being reshaped by a new kind of firm—one in which artificial intelligence (AI) runs the show. This cohort includes giants like Google, Facebook, and Alibaba, and growing businesses such as Wayfair and Ocado. Every time we use their services, the... View Details
Keywords: Artificial Intelligence; Algorithms; Technological Innovation; Business Model; Competition; Competitive Strategy; AI and Machine Learning
Iansiti, Marco, and Karim R. Lakhani. "Competing in the Age of AI." Harvard Business Review 98, no. 1 (January–February 2020): 60–67.
- January 2017 (Revised January 2017)
- Case
Susan Cassidy at Bertram Gilman International
By: Jeffrey T. Polzer and Michael Norris
In 2016, Susan Cassidy, VP of sales and marketing for the packaged foods division at CPG firm Bertram Gilman International, has to make a promotion decision. Should she choose the person she has been grooming for the position or another candidate recommended by central... View Details
Keywords: People Analytics; Algorithms; Promotion Decision; Human Resources; Business Processes; Consumer Products Industry; United States
Polzer, Jeffrey T., and Michael Norris. "Susan Cassidy at Bertram Gilman International." Harvard Business School Case 417-053, January 2017. (Revised January 2017.)
- December 1, 2021
- Article
Do You Know How Your Teams Get Work Done?
By: Rohan Narayana Murty, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna and Kartik Hosanagar
In a research study at four Fortune 500 companies, when managers were asked about their teams’ work, on average they either did not know or could not remember 60% of the work their teams do. This is a major problem because it can lead to unrealistic digital... View Details
Keywords: Leading Teams; Work Recall Gap; Machine Learning; Algorithms; Groups and Teams; Management; Technological Innovation
Murty, Rohan Narayana, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna, and Kartik Hosanagar. "Do You Know How Your Teams Get Work Done?" Harvard Business Review Digital Articles (December 1, 2021).
- September 2020 (Revised June 2023)
- Exercise
Artea: Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analytics; Data Analysis; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Targeting; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Algorithmic Bias; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; E-commerce; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised June 2023.)