Publications

Summer 2021
Article

Predictable Country-level Bias in the Reporting of COVID-19 Deaths

By: Botir Kobilov, Ethan Rouen and George Serafeim

We examine whether a country’s management of the COVID-19 pandemic relate to the downward biasing of the number of reported deaths from COVID-19. Using deviations from historical averages of the total number of monthly deaths within a country, we find that the... View Details

Keywords: COVID-19; Deaths; Reporting; Incentives; Government Policy; Health Pandemics; Health Care and Treatment; Country; Crisis Management; Outcome or Result; Reports; Policy

2019
Working Paper

Implicit Bias and the Accuracy of Explicit Social Judgment

By: J. Lees

May 2022
Case

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

By: Tsedal Neeley and Stefani Ruper

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 she accepted your resignation.” Heart... View Details

Keywords: Ethics; Employment; Corporate Social Responsibility and Impact; Technological Innovation

Educators

2003
Working Paper

Auditor Independence, Conflict of Interest, and the Unconscious Intrusion of Bias

By: Don A. Moore, George Loewenstein, Lloyd Tanlu and Max H. Bazerman

2003
Article

Don't Blame the Computer: When Self-Disclosure Moderates the Self-Serving Bias

By: Youngme Moon

Keywords: Information Technology; Prejudice and Bias

Article

Home Bias at Home: Local Equity Preference in Domestic Portfolios

By: Joshua D. Coval and Tobias J. Moskowitz

Keywords: Prejudice and Bias; Local Range; Investment

17 May 2018
Sharpening Your Skills

You Probably Have a Bias for Making Bad Decisions. Here's Why.

entrepreneurs, even when the content of the pitches is identical. And handsome men fare best of all. Why Employers Favor Men Why are women discriminated against in hiring decisions? The answer is more subtle than expected. Simple Ways to Take Gender View Details

Keywords: by Sean Silverthorne

November–December 2019
Article

Making Sense of Soft Information: Interpretation Bias and Loan Quality

By: Dennis Campbell, Maria Loumioti and Regina Wittenberg Moerman

We explore whether behavioral biases impede the effective processing and interpretation of soft information in private lending. Taking advantage of the internal reporting system of a large federal credit union, we delineate three important biases likely to affect the... View Details

Keywords: Soft Information; Lending; Banking; Information; Financing and Loans; Banks and Banking; Decision Making

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

May 28, 2018
Article

How Companies Can Identify Racial and Gender Bias in Their Customer Service

By: Alexandra C. Feldberg and Tami Kim

Research shows that minority customers — blacks and Asians — regularly receive worse customer service than whites in ways that are not immediately obvious to onlookers (or even managers). These results prompt a couple of questions for executives and managers. One, does... View Details

Keywords: Internal Audit; Customers; Service Delivery; Prejudice and Bias; Race; Gender; Organizational Change and Adaptation

Forthcoming
Article

Setting Gendered Expectations? Recruiter Outreach Bias in Online Tech Training Programs

By: Jacqueline N. Lane, Karim R. Lakhani and Roberto Fernandez

Competence development in digital technologies, analytics, and artificial intelligence is increasingly important to all types of organizations and their workforce. Universities and corporations are investing heavily in developing training programs, at all tenure... View Details

Keywords: Prejudice and Bias; Gender; Training; Recruitment; Personal Development and Career

2023
Working Paper

Setting Gendered Expectations? Recruiter Outreach Bias in Online Tech Training Programs

By: Jacqueline N. Lane, Karim R. Lakhani and Roberto Fernandez

Competence development in digital technologies, analytics, and artificial intelligence is increasingly important to all types of organizations and their workforce. Universities and corporations are investing heavily in developing training programs, at all tenure... View Details

Keywords: STEM; Selection and Staffing; Gender; Prejudice and Bias; Training; Equality and Inequality; Competency and Skills

SSRN

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

November 30, 2020
Editorial

Don't Focus on the Most Expressive Face in the Audience

By: Amit Goldenberg and Erika Weisz

Research has shown that when speaking in front of a group, people’s attention tends to gets stuck on the most emotional faces, causing them to overestimate the group’s average emotional state. In this piece, the authors share two additional findings: First, the larger... View Details

Keywords: Bias; Emotions; Perception

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

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