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- January 2025
- Article
Everyone Steps Back?: The Widespread Retraction of Crowd-Funding Support for Minority Creators When Migration Fear Is High
By: John (Jianqui) Bai, William R. Kerr, Chi Wan and Alptug Yorulmaz
We study funding gaps on Kickstarter across multiple ethnic groups from 2009 to 2021. Scaling the concept of racially salient events, we quantify the close co-movement of minority funding gaps in crowd-funding to inflamed political rhetoric surrounding migration. The... View Details
- April 2024
- Article
Model-based Financial Regulations Impair the Transition to Net-zero Carbon Emissions
By: Matteo Gasparini, Matthew C. Ives, Ben Carr, Sophie Fry and Eric Beinhocker
Investments via the financial system are essential for fostering the green transition. However, the role of existing financial regulations in influencing investment decisions is understudied. Here we analyse data from the European Banking Authority to show that... View Details
- 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
- December 4, 2023
- Article
Stop Assuming Introverts Aren't Passionate About Work
By: Kai Krautter, Anabel Büchner and Jon M. Jachimowicz
Society often assumes that the only way to be passionate is to act extroverted, but that is simply not true. In their new research, the authors found that regardless of their actual level of passion, extroverted employees are perceived as more passionate than... View Details
- 2023
- Working Paper
Complexity and Hyperbolic Discounting
By: Benjamin Enke, Thomas Graeber and Ryan Oprea
A large literature shows that people discount financial rewards hyperbolically instead of exponentially. While discounting of money has been questioned as a measure of time preferences, it continues to be highly relevant in empirical practice and predicts a wide range... View Details
- June 2023
- Simulation
Artea Dashboard and Targeting Policy Evaluation
By: Ayelet Israeli and Eva Ascarza
Companies deploy A/B experiments to gain valuable insights about their customers in order to answer strategic business problems. In marketing, A/B tests are often used to evaluate marketing interventions intended to generate incremental outcomes for the firm. The Artea... View Details
Keywords: Algorithm Bias; Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; 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
- 2023
- Working Paper
Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness
By: Neil Menghani, Edward McFowland III and Daniel B. Neill
In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false... View Details
- 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
- 2024
- Working Paper
Everyone Steps Back?: The Widespread Retraction of Crowd-Funding Support for Minority Creators When Migration Fear Is High
By: John (Jianqui) Bai, William R. Kerr, Chi Wan and Alptug Yorulmaz
We study racial biases on Kickstarter across multiple ethnic groups from 2009-2021. Scaling the concept of racially salient events, we quantify the close co-movement of minority funding gaps to inflamed political rhetoric surrounding migration. The racial funding gap... View Details
- Article
Present Bias Causes and Then Dissipates Auto-enrollment Savings Effects
By: John Beshears, James J. Choi, David Laibson and Peter Maxted
Present bias causes procrastination, which leads households to stick with auto-enrollment defaults. However, present bias also engenders overconsumption. Separation from each employer generates a rollover of 401(k) balances to an individual retirement account (IRA)... View Details
- 2022
- Other Teaching and Training Material
Organizational Behavior Reading: Managing Differences
By: Robin Ely and Colleen Ammerman
This reading provides principles and practices managers can draw upon to leverage differences in social identities - such as gender and race - to create more effective work relationships, teams, and organizations. The Essential Reading's first section draws upon... View Details
- 2023
- Working Paper
Fintech to the (Worker) Rescue: Access to Earned Wages, Financial Health and Employee Turnover
By: Jose Murillo, Boris Vallée and Dolly Yu
Using novel data from a Mexican FinTech firm, we study the usage by workers of earned wages access, an innovative financial service offered by firms to their employees as a benefit. We find usage to be significant and concentrated towards the end of the pay cycle. We... View Details
Keywords: Fintech; Present Bias; Earned Wage Access; Wages; Employees; Retention; Well-being; Mexico
- Article
Fighting Bias on the Front Lines
By: Alexandra C. Feldberg and Tami Kim
Most companies aim for exceptional customer service, but too few are attentive to the subtle discrimination by frontline employees that can alienate customers, lead to lawsuits, or even cause lasting brand damage by going viral.
This article presents research... View Details
This article presents research... View Details
- 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
- 2021
- Book
Glass Half-Broken: Shattering the Barriers That Still Hold Women Back at Work
By: Colleen Ammerman and Boris Groysberg
Why does the gender gap persist and how can we close it? For years women have made up the majority of college-educated workers in the United States. In 2019, the gap between the percentage of women and the percentage of men in the workforce was the smallest on record.... View Details
- 2021
- Working Paper
Bollywood, Skin Color and Sexism: The Role of the Film Industry in Emboldening and Contesting Stereotypes in India after Independence
By: Sudev Sheth, Geoffrey Jones and Morgan Spencer
This working paper examines the social impact of the film industry in India during the first four decades after Indian Independence in 1947. It shows that Bollywood, the mainstream cinema in India and the counterpart in scale to Hollywood in the United States, shared... View Details
- 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
- 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
- 2020
- Working Paper
(When) Does Appearance Matter? Evidence from a Randomized Controlled Trial
By: Prithwiraj Choudhury, Tarun Khanna, Christos A. Makridis and Subhradip Sarker
While there is evidence about labor market discrimination based on race, religion, and gender, we know little about whether physical appearance leads to discrimination in labor market outcomes. We deploy a randomized experiment on 1,000 respondents in India between... View Details
- 2022
- Working Paper
Optimal Illiquidity
By: John Beshears, James J. Choi, Christopher Clayton, Christopher Harris, David Laibson and Brigitte C. Madrian
We calculate the socially optimal level of illiquidity in an economy populated by households with taste shocks and naive present bias. The government chooses mandatory contributions to accounts, each witha different pre-retirement withdrawal penalty. Collected... View Details
Keywords: Illiquidity; Commitment; Flexibility; Savings; Social Security; Retirement; Government Legislation; Taxation; Saving