Publications
Publications
Show Results For
- All HBS Web
(1,144)
- Faculty Publications (335)
Bias
→
- 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
- 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
- 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
- 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
- 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
- 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; Retail Industry; Apparel and Accessories Industry; United States
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- June 2020
- Article
How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections
By: Maria Ibanez and Michael W. Toffel
Accuracy and consistency are critical for inspections to be an effective, fair, and useful tool for assessing risks, quality, and suppliers—and for making decisions based on those assessments. We examine how inspector schedules could introduce bias that erodes... View Details