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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

Keywords: Machine Learning; Algorithms; Objective Perturbation; Mathematical Methods

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

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Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World

In industry after industry, data, analytics, and AI-driven processes are transforming the nature of work. While we often still treat AI as the domain of a specific skill, business function, or sector, we have entered a new era in which AI is challenging the very... View Details

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

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

Keywords: Targeted Advertising; Algorithmic Data; Bias; Advertising; Race; Gender; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States

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

Keywords: Decision Making; Judgment; Algorithms; Advice Taking

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

Keywords: Machine Learning Models; Recourse; Algorithm; Mathematical Methods

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

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

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

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

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

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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

Keywords: Artificial Intelligence; Algorithms; Computational Social Science

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

Keywords: Transparency; Marketing Research; Algorithmic Bias; AI and Machine Learning; Marketing

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

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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

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

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

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Article

Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)

By: Eva Ascarza and Ayelet Israeli

An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details

Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods

2024
Working Paper

Adjusting Prices 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

Keywords: Algorithmic Pricing; Ecommerce; Price Monitoring; Price; Competition; Retail Industry

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