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  • September 2020 (Revised July 2022)
  • Technical Note

Algorithmic Bias in Marketing

By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeting; Targeted Advertising; Pricing Algorithms; Ethical Decision Making; Customer Heterogeneity; Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Marketing Communications; Analytics and Data Science; Analysis; Decision Making; Ethics; Customer Relationship Management; E-commerce; Retail Industry; Apparel and Accessories Industry; United States
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  • September 2020 (Revised July 2022)
  • Exercise

Artea (D): Discrimination through Algorithmic Bias in Targeting

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: Targeted Advertising; Discrimination; Algorithmic Data; Bias; Advertising; Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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  • September 2020 (Revised July 2022)
  • Teaching Note

Algorithmic Bias in Marketing

By: Ayelet Israeli and Eva Ascarza
Teaching Note for HBS No. 521-020. This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and... View Details
Keywords: Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Decision Making; Ethics; Customer Relationship Management; Retail Industry; Technology Industry; Apparel and Accessories Industry; United States
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  • September 2020 (Revised July 2022)
  • Exercise

Artea (C): Potential Discrimination through Algorithmic Targeting

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: Targeting; Algorithmic Bias; Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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  • Article

Mitigating Bias in Adaptive Data Gathering via Differential Privacy

By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
Keywords: Bandit Algorithms; Bias; Analytics and Data Science; Mathematical Methods; Theory
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  • September 29, 2023
  • Article

Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI

By: Simon Friis and James Riley
When it comes to artificial intelligence and inequality, algorithmic bias rightly receives a lot of attention. But it’s just one way that AI can lead to inequitable outcomes. To truly create equitable AI, we need to consider three forces through which it might make... View Details
Keywords: AI and Machine Learning; Prejudice and Bias; Equality and Inequality
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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
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  • March 2024
  • Case

Unintended Consequences of Algorithmic Personalization

By: Eva Ascarza and Ayelet Israeli
“Unintended Consequences of Algorithmic Personalization” (HBS No. 524-052) investigates algorithmic bias in marketing through four case studies featuring Apple, Uber, Facebook, and Amazon. Each study presents scenarios where these companies faced public criticism for... View Details
Keywords: Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Customization and Personalization; Technology Industry; Retail Industry; United States
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  • September–October 2021
  • Article

Frontiers: Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb

By: Shunyuan Zhang, Nitin Mehta, Param Singh and Kannan Srinivasan
We study the effect of Airbnb’s smart-pricing algorithm on the racial disparity in the daily revenue earned by Airbnb hosts. Our empirical strategy exploits Airbnb’s introduction of the algorithm and its voluntary adoption by hosts as a quasi-natural experiment. Among... View Details
Keywords: Smart Pricing; Pricing Algorithm; Machine Bias; Discrimination; Racial Disparity; Social Inequality; Airbnb Revenue; Revenue; Race; Equality and Inequality; Prejudice and Bias; Price; Mathematical Methods; Accommodations Industry
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  • September 2020 (Revised July 2022)
  • Exercise

Artea (B): Including Customer-Level Demographic Data

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: Targeting; Algorithmic Bias; Race; Gender; Marketing; Diversity; Customer Relationship Management; Demographics; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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  • 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
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  • September 17, 2021
  • Article

AI Can Help Address Inequity—If Companies Earn Users' Trust

By: Shunyuan Zhang, Kannan Srinivasan, Param Singh and Nitin Mehta
While companies may spend a lot of time testing models before launch, many spend too little time considering how they will work in the wild. In particular, they fail to fully consider how rates of adoption can warp developers’ intent. For instance, Airbnb launched a... View Details
Keywords: Artificial Intelligence; Algorithmic Bias; Technological Innovation; Perception; Diversity; Equality and Inequality; Trust; AI and Machine Learning
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  • 18 Oct 2022
  • Research & Ideas

When Bias Creeps into AI, Managers Can Stop It by Asking the Right Questions

algorithm generates fair outcomes. As the algorithm sorts through information to optimize its objective, BEAT detects and eliminates bias at key points in the training process.... View Details
Keywords: by Rachel Layne
  • 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
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  • 2023
  • Working Paper

The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities

By: David S. Scharfstein and Sergey Chernenko
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in... View Details
Keywords: Racial Disparity; Paycheck Protection Program; Measurement Error; AI and Machine Learning; Race; Measurement and Metrics; Equality and Inequality; Prejudice and Bias; Forecasting and Prediction; Outcome or Result
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  • 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
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  • 19 Nov 2019
  • Op-Ed

Gender Bias Complaints against Apple Card Signal a Dark Side to Fintech

bias in Goldman Sachs’s underwriting model. (Goldman developed and issued the card.) Adding fuel to the fire, Apple co-founder Steve Wozniak shared that the same thing had happened to him and his wife. Officials from the New York... View Details
Keywords: by Karen G. Mills; Financial Services
  • 16 Dec 2022
  • Research & Ideas

Why Technology Alone Can't Solve AI's Bias Problem

human toll to letting algorithms do the work. “Maybe there is a bias from people who have been traditionally hiring men.” Searches on popular recruiting sites might seem like a neutral way to find... View Details
Keywords: by Michael Blanding; Technology
  • 30 May 2024
  • Research & Ideas

Racial Bias Might Be Infecting Patient Portals. Can AI Help?

messages. That’s part of the reason the authors say there may be other factors besides direct racial bias driving the results—and a key reason that they are keen to explore this data in future research. One potential factor they... View Details
Keywords: by Ben Rand; Health
  • 12 Oct 2022
  • Research & Ideas

When Design Enables Discrimination: Learning from Anti-Asian Bias on Airbnb

they may inadvertently exacerbate. While Airbnb has addressed bias concerns with site changes in the past, further steps could be taken to bring more anonymity to the site, Luca says. Platform design and discrimination Renting out... View Details
Keywords: by Pamela Reynolds; Technology; Travel

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