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Show Results For
- All HBS Web
(684)
- News (145)
- Research (424)
- Events (20)
- Multimedia (12)
- Faculty Publications (303)
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- 24 Jul 2023
- Research & Ideas
Part-Time Employees Want More Hours. Can Companies Tap This ‘Hidden’ Talent Pool?
many such workers are caregivers, excluded from full-time jobs because short-sighted employers don’t offer them the flexibility they need. Filtered out by hiring algorithms due to employment gaps or other hiring “red flags,” these willing... View Details
Keywords: by Kara Baskin
- September 2018 (Revised December 2019)
- Case
Zebra Medical Vision
By: Shane Greenstein and Sarah Gulick
An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making... View Details
Keywords: Radiology; Machine Learning; X-ray; CT Scan; Medical Technology; Probability; FDA 510(k); Diagnosis; Business Startups; Health Care and Treatment; Information Technology; Applications and Software; Competitive Strategy; Product Development; Commercialization; Decision Choices and Conditions; Health Industry; Medical Devices and Supplies Industry; Technology Industry; Israel
Greenstein, Shane, and Sarah Gulick. "Zebra Medical Vision." Harvard Business School Case 619-014, September 2018. (Revised December 2019.)
- October 2021 (Revised March 2022)
- Supplement
PittaRosso: Artificial Intelligence-Driven Pricing and Promotion
By: Ayelet Israeli and Fabrizio Fantini
PittaRosso, a traditional Italian shoe retailer, is implementing an AI system to provide pricing and promotion recommendations. The system allows them to implement changes that would affect both the top of funnel and bottom of funnel activities for the company: once... View Details
Keywords: Artificial Intelligence; Pricing; Pricing Algorithm; Pricing Decisions; Pricing Strategy; Pricing Structure; Promotion; Promotions; Online Marketing; Data-driven Decision-making; Data-driven Management; Retail; Retail Analytics; Price; Advertising Campaigns; Analytics and Data Science; Analysis; Digital Marketing; Budgets and Budgeting; Marketing Strategy; Marketing; Transformation; Decision Making; Retail Industry; Italy
- 25 May 2021
- Research & Ideas
White Airbnb Hosts Earn More. Can AI Shrink the Racial Gap?
White people who host rental properties on Airbnb earn significantly more per year than Black hosts, but a “race blind” pricing algorithm could help close that income gap, new research shows. Black hosts who rely on Airbnb’s View Details
- August 2022
- Supplement
Zalora: Data-Driven Pricing Recommendations
By: Ayelet Israeli
This exercise can be used in conjunction with the main case "Zalora: Data-Driven Pricing" to facilitate class discussion without requiring data analysis from the students. Instead, the exercise presents reports that were created by the data science team to answer the... View Details
Keywords: Pricing; Pricing Algorithms; Dynamic Pricing; Ecommerce; Pricing Strategy; Pricing And Revenue Management; Apparel; Singapore; Startup; Demand Estimation; Data Analysis; Data Analytics; Exercise; Price; Internet and the Web; Apparel and Accessories Industry; Retail Industry; Fashion Industry; Singapore
Israeli, Ayelet. "Zalora: Data-Driven Pricing Recommendations." Harvard Business School Supplement 523-032, August 2022.
- November 2021 (Revised December 2021)
- Supplement
PittaRosso (B): Human and Machine Learning
By: Ayelet Israeli
This case supplements the "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion" case, and provides major highlights on what happened at the company since the first case. View Details
Keywords: Artificial Intelligence; Pricing; Pricing Algorithm; Pricing Decisions; Pricing Strategy; Pricing Structure; Promotion; Promotions; Online Marketing; Data-driven Decision-making; Data-driven Management; Retail; Retail Analytics; Price; Advertising Campaigns; Analytics and Data Science; Analysis; Digital Marketing; Budgets and Budgeting; Marketing Strategy; Marketing; Transformation; Decision Making; AI and Machine Learning; Retail Industry; Italy
Israeli, Ayelet. "PittaRosso (B): Human and Machine Learning." Harvard Business School Supplement 522-047, November 2021. (Revised December 2021.)
- 15 Nov 2022
- Op-Ed
Why TikTok Is Beating YouTube for Eyeball Time (It’s Not Just the Dance Videos)
five times faster than the United Breaks Guitars video. The reason, it seemed, was that it spread not from person to person, but by algorithm. It spread because the algorithm noticed that if the song was served to people on TikTok, many... View Details
Keywords: by John Deighton and Leora Kornfeld
- September 2022 (Revised November 2022)
- Teaching Note
PittaRosso: Artificial Intelligence-Driven Pricing and Promotion
By: Ayelet Israeli
Teaching Note for HBS Case No. 522-046. View Details
Keywords: Artificial Intelligence; Pricing; Pricing Algorithm; Pricing Decisions; Pricing Strategy; Pricing Structure; Promotion; Promotions; Online Marketing; Data-driven Decision-making; Data-driven Management; Retail; Retail Analytics; Price; Advertising Campaigns; Analytics and Data Science; Analysis; Digital Marketing; Budgets and Budgeting; Marketing Strategy; Transformation; Decision Making; AI and Machine Learning; Retail Industry; Italy
- January 2024 (Revised February 2024)
- Case
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
The case examines Levi Strauss’ journey in implementing machine learning and AI into its financial forecasting process. The apparel company partnered with the IT company Wipro in 2017 to develop a machine learning algorithm that could help Levi Strauss forecast its... View Details
Keywords: Investor Relations; Forecasting; Machine Learning; Artificial Intelligence; Apparel; Corporate Finance; Forecasting and Prediction; AI and Machine Learning; Digital Transformation; Apparel and Accessories Industry; United States
Egan, Mark. "Data-Driven Denim: Financial Forecasting at Levi Strauss." Harvard Business School Case 224-029, January 2024. (Revised February 2024.)
- Research Summary
Understanding the Limitations of Model Explanations
The goal of this research is to understand how adversaries can exploit various algorithms used for explaining complex machine learning models with an intention to mislead end users. For instance, can adversaries trick these algorithms into masking their racial and... View Details
- 2022
- Working Paper
Machine Learning Models for Prediction of Scope 3 Carbon Emissions
By: George Serafeim and Gladys Vélez Caicedo
For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15... View Details
Keywords: Carbon Emissions; Climate Change; Environment; Carbon Accounting; Machine Learning; Artificial Intelligence; Digital; Data Science; Environmental Sustainability; Environmental Management; Environmental Accounting
Serafeim, George, and Gladys Vélez Caicedo. "Machine Learning Models for Prediction of Scope 3 Carbon Emissions." Harvard Business School Working Paper, No. 22-080, June 2022.
- November–December 2024
- Article
Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing
By: Kirk Bansak and Elisabeth Paulson
This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment... View Details
Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Operations Research 72, no. 6 (November–December 2024): 2375–2390.
- 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; Automation; Benefits; Compensation; Cost Reduction; Government; Fraud; Government Technology; Public Sector; Systems; Systems Integration; Unemployment Insurance; Waste Heat Recovery; AI and Machine Learning; Government Administration; Insurance; Decision Making; Digital Transformation; Employment; Public Administration Industry; United States; Michigan
Ghosh, Shikhar, and Shweta Bagai. "MiDAS: Automating Unemployment Benefits." Harvard Business School Case 825-100, November 2024. (Revised January 2025.)
- Teaching Interest
Big Data Analytics and Machine Learning
Big data in the context of marketing, management, and innovation strategy. Machine Learning algorithms and tools.
View Details
View Details
- Article
Testing Substitutability
By: John William Hatfield, Nicole Immorlica and Scott Duke Kominers
We provide an algorithm for testing the substitutability of a length-N preference relation over a set of contracts X in time O(|X|3⋅N3). Access to the preference relation is essential for this result: We show that a substitutability-testing algorithm with access only... View Details
Keywords: Substitutability; Matching; Communication Complexity; Preference Elicitation; Marketplace Matching; Communication; Mathematical Methods; Economics
Hatfield, John William, Nicole Immorlica, and Scott Duke Kominers. "Testing Substitutability." Games and Economic Behavior 75, no. 2 (July 2012): 639–645.
- May 2020
- Article
Scalable Holistic Linear Regression
By: Dimitris Bertsimas and Michael Lingzhi Li
We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting... View Details
Bertsimas, Dimitris, and Michael Lingzhi Li. "Scalable Holistic Linear Regression." Operations Research Letters 48, no. 3 (May 2020): 203–208.
- September 2020 (Revised July 2022)
- Supplement
Spreadsheet Supplement to Artea (B) and (C)
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting" View Details
- March 2022
- Article
Learning to Rank an Assortment of Products
By: Kris Ferreira, Sunanda Parthasarathy and Shreyas Sekar
We consider the product ranking challenge that online retailers face when their customers typically behave as “window shoppers”: they form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue... View Details
Keywords: Online Learning; Product Ranking; Assortment Optimization; Learning; Internet and the Web; Product Marketing; Consumer Behavior; E-commerce
Ferreira, Kris, Sunanda Parthasarathy, and Shreyas Sekar. "Learning to Rank an Assortment of Products." Management Science 68, no. 3 (March 2022): 1828–1848.
- 2019
- Article
An Empirical Study of Rich Subgroup Fairness for Machine Learning
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 100–109.
- Research Summary
Overview
Professor Ferreira's research primarily focuses on how retailers can use algorithms to make better revenue management decisions, including pricing, product display, and assortment planning. In the retail industry, anticipating consumer demand is arguably one of the... View Details