Publications

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

2023
Working Paper

Feature Importance Disparities for Data Bias Investigations

By: Peter W. Chang, Leor Fishman and Seth Neel

It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details

Keywords: AI and Machine Learning; Analytics and Data Science; Prejudice and Bias

2023
Article

Provable Detection of Propagating Sampling Bias in Prediction Models

By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill

With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... View Details

18 Oct 2022
Research & Ideas

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

algorithm perpetuates this. Another source of bias is incomplete or unrepresentative information. A famous example is facial recognition. If I use mostly photos of white men to train the machine to learn... View Details

Keywords: by Rachel Layne

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

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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2025
Article

Humor as a Window into Generative AI Bias

By: Roger Samure, Julian De Freitas and Stefano Puntoni

A preregistered audit of 600 images by generative AI across 150 different prompts explores the link between humor and discrimination in consumer-facing AI solutions. When ChatGPT updates images to make them “funnier”, the prevalence of stereotyped groups changes. While... View Details

Keywords: AI and Machine Learning; Demographics; Prejudice and Bias

01 Apr 2019
What Do You Think?

Does Our Bias Against Federal Deficits Need Rethinking?

huge deficits stretching back and forward for a decade,” according to Kautz. “I believe that an emerging disinflationary era, driven by more intelligent machines and continuing globalization, is why they are right. Most importantly for... View Details

Keywords: by James Heskett

2021
Working Paper

Invisible Primes: Fintech Lending with Alternative Data

By: Marco Di Maggio, Dimuthu Ratnadiwakara and Don Carmichael

We exploit anonymized administrative data provided by a major fintech platform to investigate whether using alternative data to assess borrowers’ creditworthiness results in broader credit access. Comparing actual outcomes of the fintech platform’s model to... View Details

Keywords: Fintech Lending; Alternative Data; Machine Learning; Algorithm Bias; Finance; Information Technology; Financing and Loans; Analytics and Data Science; Credit

2023
Working Paper

Auditing Predictive Models for Intersectional Biases

By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill

Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we... View Details

Keywords: Predictive Models; Bias; AI and Machine Learning

October 2023
Teaching Note

Timnit Gebru: 'SILENCED No More' on AI Bias and The Harms of Large Language Models

By: Tsedal Neeley and Tim Englehart

Teaching Note for HBS Case No. 422-085. Dr. Timnit Gebru—a leading artificial intelligence (AI) computer scientist and co-lead of Google’s Ethical AI team—was messaging with one of her colleagues when she saw the words: “Did you resign?? Megan sent an email saying that... View Details

Keywords: Ethics; Employment; Corporate Social Responsibility and Impact; Technological Innovation; AI and Machine Learning; Diversity; Prejudice and Bias; Technology Industry

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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

SSRN

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

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

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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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March 2019
Case

Wattpad

By: John Deighton and Leora Kornfeld

How to run a platform to match four million writers of stories to 75 million readers? Use data science. Make money by doing deals with television and filmmakers and book publishers. The case describes the challenges of matching readers to stories and of helping writers... View Details

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April 2025
Article

Serving with a Smile on Airbnb: Analyzing the Economic Returns and Behavioral Underpinnings of the Host’s Smile

By: Shunyuan Zhang, Elizabeth Friedman, Kannan Srinivasan, Ravi Dhar and Xupin Zhang

Non-informational cues, such as facial expressions, can significantly influence judgments and interpersonal impressions. While past research has explored how smiling affects business outcomes in offline or in-store contexts, relatively less is known about how smiling... View Details

Keywords: Sharing Economy; Airbnb; Image Feature Extraction; Machine Learning; Facial Expressions; Prejudice and Bias; Nonverbal Communication; E-commerce; Consumer Behavior; Perception