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- All HBS Web
(1,128)
- Faculty Publications (337)
- October 2020 (Revised April 2022)
- Case
When Institutions Fail: HIV/AIDS in the 1980s
By: Tom Nicholas and Christian Godwin
During the early 1980s, young gay men in urban centers such as San Francisco and New York City began contracting a mysterious illness that would come to be known as HIV/AIDS. A diagnosis meant almost certain death, with a less than 1% survival rate. Conflicting... View Details
Keywords: Ethics; Policy; Government and Politics; Health Pandemics; History; Rights; Media; Organizations; Business and Community Relations; Religion; Social Psychology; Identity; Prejudice and Bias; Social Issues; Public Opinion; Journalism and News Industry; Journalism and News Industry; Journalism and News Industry; Journalism and News Industry; Journalism and News Industry; Journalism and News Industry; United States
Nicholas, Tom, and Christian Godwin. "When Institutions Fail: HIV/AIDS in the 1980s." Harvard Business School Case 821-002, October 2020. (Revised April 2022.)
- September 2020
- Case
The Black New Venture Competition
Black entrepreneurs encounter many unique obstacles when raising capital to start and grow a business. During their second year at Harvard Business School (HBS), MBA students Kimberly Foster and Tyler Simpson decided to do something to make a difference for... View Details
Keywords: Startup; Start-up; Startup Financing; Startups; Start-ups; African-American Protagonist; African-american Entrepreneurs; African-american Investors; African-Americans; African-American Women; Black Leadership; Black Inventors; Black Entrepreneurs; Harvard Business School; Harvard; Business And Society; Early Stage Funding; Early Stage Finance; Technology Entrepreneurship; Discrimination; Technology Ventures; Entrepreneurial Finance; Entrepreneurial Financing; Business Plan; Business Startups; Business Ventures; Financing and Loans; Business Growth and Maturation; Diversity; Gender; Race; Entrepreneurship; Venture Capital; Small Business; Leadership; Information Technology; Competition; Technology Industry
Mills, Karen, Jeffrey J. Bussgang, Martin Sinozich, and Gabriella Elanbeck. "The Black New Venture Competition." Harvard Business School Case 821-029, September 2020.
- 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; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020. (Revised July 2022.)
- September 2020 (Revised February 2024)
- Teaching Note
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
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 A/B testing analysis and... View Details
- 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; Apparel and Accessories Industry; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea (B): Including Customer-Level Demographic Data." Harvard Business School Exercise 521-022, September 2020. (Revised July 2022.)
- 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; Apparel and Accessories Industry; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea (C): Potential Discrimination through Algorithmic Targeting." Harvard Business School Exercise 521-037, September 2020. (Revised July 2022.)
- 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; Apparel and Accessories Industry; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea (D): Discrimination through Algorithmic Bias in Targeting." Harvard Business School Exercise 521-043, September 2020. (Revised July 2022.)
- 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
Keywords: Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; "Marketing Analytics"; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analytics; Data Analysis; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Targeting; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Algorithmic Bias; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; E-commerce; Analytics and Data Science; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised June 2023.)
- September 2020 (Revised July 2022)
- Supplement
Spreadsheet Supplement to Artea (B) and (C)
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting" View Details
- 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; Apparel and Accessories Industry; Apparel and Accessories 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
Keywords: Behavioral Economics; Coronavirus; Discrimination; Homophily; Labor Market Mobility; Limited Attention; Resumes; Personal Characteristics; Prejudice and Bias
Choudhury, Prithwiraj, Tarun Khanna, Christos A. Makridis, and Subhradip Sarker. "(When) Does Appearance Matter? Evidence from a Randomized Controlled Trial." Harvard Business School Working Paper, No. 21-038, September 2020.
- August 2020 (Revised December 2020)
- Background Note
A Note on Ethical Analysis
By: Nien-hê Hsieh
To engage in ethical analysis is to answer such questions as “What is the right thing to do?” “What does it mean to be a good person?” “How should I live my life?” Ethical analysis, on its own, is often not adequate for doing the right thing or being a good... View Details
Hsieh, Nien-hê. "A Note on Ethical Analysis." Harvard Business School Background Note 321-038, August 2020. (Revised December 2020.)
- August 2020
- Article
Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation
By: Prithwiraj Choudhury, Evan Starr and Rajshree Agarwal
The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic... View Details
Choudhury, Prithwiraj, Evan Starr, and Rajshree Agarwal. "Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation." Strategic Management Journal 41, no. 8 (August 2020): 1381–1411.
- July 2020
- Teaching Plan
Girls Who Code
By: Brian Trelstad and Amy Klopfenstein
This teaching plan serves as a supplement to HBS Case No. 320-055, “Girls Who Code.” Founded 2012 by former lawyer Reshma Saujani, Girls Who Code (GWC) offered coding education programs to middle- and high school-aged girls. The organization also sought to alter... View Details
Keywords: Communication; Communication Strategy; Spoken Communication; Interpersonal Communication; Demographics; Age; Gender; Education; Curriculum and Courses; Learning; Middle School Education; Secondary Education; Leadership Style; Leadership; Social Enterprise; Nonprofit Organizations; Social Psychology; Attitudes; Behavior; Cognition and Thinking; Prejudice and Bias; Power and Influence; Identity; Social and Collaborative Networks; Motivation and Incentives; Society; Civil Society or Community; Culture; Public Opinion; Social Issues; Information Technology; Applications and Software; Education Industry; Technology Industry; North and Central America; United States
- 2020
- Working Paper
When Do Experts Listen to Other Experts? The Role of Negative Information in Expert Evaluations for Novel Projects
By: Jacqueline N. Lane, Misha Teplitskiy, Gary Gray, Hardeep Ranu, Michael Menietti, Eva C. Guinan and Karim R. Lakhani
The evaluation of novel projects lies at the heart of scientific and technological innovation, and yet literature suggests that this process is subject to inconsistency and potential biases. This paper investigates the role of information sharing among experts as the... View Details
Keywords: Project Evaluation; Innovation; Knowledge Frontier; Negativity Bias; Projects; Innovation and Invention; Information; Diversity; Judgments
Lane, Jacqueline N., Misha Teplitskiy, Gary Gray, Hardeep Ranu, Michael Menietti, Eva C. Guinan, and Karim R. Lakhani. "When Do Experts Listen to Other Experts? The Role of Negative Information in Expert Evaluations for Novel Projects." Harvard Business School Working Paper, No. 21-007, July 2020. (Revised November 2020.)
- July 2020 (Revised January 2021)
- Case
Rosalind Fox at John Deere
By: Anthony Mayo and Olivia Hull
Rosalind Fox, the factory manager at John Deere’s Des Moines, Iowa plant, has improved the financial standing of the factory in the three years she’s been at its helm. But employee engagement scores—which measured employees’ satisfaction with working conditions and... View Details
Keywords: Agribusiness; Change Management; Experience and Expertise; Talent and Talent Management; Diversity; Gender; Race; Engineering; Geographic Location; Globalized Markets and Industries; Leadership Development; Leadership Style; Leading Change; Management Style; Management Teams; Organizational Culture; Personal Development and Career; Prejudice and Bias; Power and Influence; Status and Position; Trust; Agriculture and Agribusiness Industry; United States
Mayo, Anthony, and Olivia Hull. "Rosalind Fox at John Deere." Harvard Business School Case 421-011, July 2020. (Revised January 2021.)
- 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
Beshears, John, James J. Choi, Christopher Clayton, Christopher Harris, David Laibson, and Brigitte C. Madrian. "Optimal Illiquidity." Working Paper, July 2022.
- June 2020 (Revised September 2020)
- Case
Shellye Archambeau: Becoming a CEO (A)
By: Tsedal Neeley and John Masko
With the economy in a freefall, MetricStream is losing customers, hemorrhaging cash and struggling to make payroll. Several board members are threatening to quit. Others are pressing to sell the company even at dismally low valuations. It’s 2008 and lightning has... View Details
Keywords: Leadership; Race; Gender; Leadership Style; Risk and Uncertainty; Change; Prejudice and Bias; Decision Making; Personal Development and Career; Technology Industry; California
Neeley, Tsedal, and John Masko. "Shellye Archambeau: Becoming a CEO (A)." Harvard Business School Case 420-071, June 2020. (Revised September 2020.)
- June 2020
- Supplement
Shellye Archambeau: Becoming a CEO (B)
By: Tsedal Neeley and Briana Richardson
With the economy in a freefall, MetricStream is losing customers, hemorrhaging cash and struggling to make payroll. Several board members are threatening to quit. Others are pressing to sell the company even at dismally low valuations. It’s 2008 and lightning has... View Details
Keywords: Race; Gender; Leadership Style; Risk and Uncertainty; Change; Prejudice and Bias; Decision Making; Personal Development and Career; Technology Industry; California
Neeley, Tsedal, and Briana Richardson. "Shellye Archambeau: Becoming a CEO (B)." Harvard Business School Supplement 420-073, June 2020.