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      • 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
      Keywords: Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Decision Making; Ethics; Customer Relationship Management; Apparel and Accessories Industry; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
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      Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Teaching Note 521-035, September 2020. (Revised July 2022.)
      • 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
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      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
      Keywords: Targeted Advertising; Targeting; 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
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      Ascarza, Eva, and Ayelet Israeli. "Artea (A), (B), (C), and (D): Designing Targeting Strategies." Harvard Business School Teaching Note 521-041, September 2020. (Revised February 2024.)
      • 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
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      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
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      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
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      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
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      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
      Keywords: Gender; Race; Diversity; Marketing; Customer Relationship Management; Demographics; Prejudice and Bias; Apparel and Accessories Industry; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Spreadsheet Supplement to Artea (B) and (C)." Harvard Business School Spreadsheet Supplement 521-704, September 2020. (Revised July 2022.)
      • 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
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      Ascarza, Eva, and Ayelet Israeli. "Spreadsheet Supplement to Artea Teaching Note." Harvard Business School Spreadsheet Supplement 521-705, September 2020. (Revised June 2023.)
      • 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
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      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
      Keywords: Ethics; Framework; Decision Making; Prejudice and Bias
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      Hsieh, Nien-hê. "A Note on Ethical Analysis." Harvard Business School Background Note 321-038, August 2020. (Revised December 2020.)
      • 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
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      Trelstad, Brian, and Amy Klopfenstein. "Girls Who Code." Harvard Business School Teaching Plan 321-010, July 2020.
      • 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
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      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
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      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
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      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
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      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
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      Neeley, Tsedal, and Briana Richardson. "Shellye Archambeau: Becoming a CEO (B)." Harvard Business School Supplement 420-073, June 2020.
      • 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
      Keywords: Assessment; Bias; Inspection; Scheduling; Econometric Analysis; Empirical Research; Regulation; Health; Food; Safety; Quality; Performance Consistency; Governing Rules, Regulations, and Reforms
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      Ibanez, Maria, and Michael W. Toffel. "How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections." Management Science 66, no. 6 (June 2020): 2396–2416. (Revised February 2019. Featured in Harvard Business Review, Forbes, Food Safety Magazine, Food Safety News, and KelloggInsight. (2020 MSOM Responsible Research Finalist.))
      • Article

      The Impact of Penalties for Wrong Answers on the Gender Gap in Test Scores

      By: Katherine B. Coffman and David Klinowski
      Multiple-choice exams play a critical role in university admissions across the world. A key question is whether imposing penalties for wrong answers on these exams deters guessing from women more than men, disadvantaging female test-takers. We consider data from a... View Details
      Keywords: Behavioral Economics; Standardized Testing; Gender; Higher Education; Prejudice and Bias
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      Coffman, Katherine B., and David Klinowski. "The Impact of Penalties for Wrong Answers on the Gender Gap in Test Scores." Proceedings of the National Academy of Sciences 117, no. 16 (April 21, 2020): 8794–8803.
      • April 2020 (Revised June 2020)
      • Case

      Race and Mass Incarceration in the United States

      By: Reshmaan N. Hussam and Holly Fetter
      The late 20th century saw a dramatic shift in the criminal justice system of the United States. While incarceration rates had remained stable through the 1960s, they quintupled by the 2000s to 707 per 100,000, far exceeding that of all other nations in the world. By... View Details
      Keywords: Criminal Justice System; Incarceration; Race; Prejudice and Bias; United States
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      Hussam, Reshmaan N., and Holly Fetter. "Race and Mass Incarceration in the United States." Harvard Business School Case 720-034, April 2020. (Revised June 2020.)
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