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- April 3, 2024
- Article
How Automakers Can Address Resistance to Self-Driving Cars
By: Stuti Agarwal, Julian De Freitas and Carey K. Morewedge
Research involving multiple experiments found that consumers have biased views of their driving abilities relative to those of other drivers and automated vehicles. These findings have implications for the adoption of partly or fully automated vehicles, which one day...
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Keywords:
Technology Adoption;
Consumer Behavior;
Government Legislation;
Prejudice and Bias;
Auto Industry;
Technology Industry
Agarwal, Stuti, Julian De Freitas, and Carey K. Morewedge. "How Automakers Can Address Resistance to Self-Driving Cars." Harvard Business Review (website) (April 3, 2024).
- 2024
- Working Paper
Greenlighting Innovative Projects: How Evaluation Format Shapes the Perceived Feasibility of Novel Ideas
By: Jacqueline N. Lane, Tianxi Cai, Michael Menietti, Griffin Weber and Eva C. Guinan
Evaluation of novel projects is essential for scientific and technological advancement. However,
evaluator bias toward a project’s potential can obscure its limitations. This study investigates
evaluation formats by contrasting combined assessments of novelty and...
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Lane, Jacqueline N., Tianxi Cai, Michael Menietti, Griffin Weber, and Eva C. Guinan. "Greenlighting Innovative Projects: How Evaluation Format Shapes the Perceived Feasibility of Novel Ideas." Harvard Business School Working Paper, No. 24-064, March 2024.
- 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...
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Keywords:
Race;
Gender;
Marketing;
Diversity;
Customer Relationship Management;
Prejudice and Bias;
Customization and Personalization;
Technology Industry;
Retail Industry;
United States
Ascarza, Eva, and Ayelet Israeli. "Unintended Consequences of Algorithmic Personalization." Harvard Business School Case 524-052, March 2024.
- 2024
- Working Paper
Choosing and Using Information in Evaluation Decisions
By: Katherine Baldiga Coffman, Scott Kostyshak and Perihan O. Saygin
Most studies of gender discrimination consider how male versus female candidates are assessed given otherwise identical information about them. But, in many settings of interest, evaluators have a choice about how much information to acquire about a candidate before...
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- March 2024
- Article
Differences in Care Team Response to Patient Portal Messages by Patient Race and Ethnicity
By: Mitchell Tang, Rebecca Mishuris, Lily Payvandi and Ariel Dora Stern
Importance: The COVID-19 pandemic was associated with substantial growth in patient portal messaging. Higher message volumes have largely persisted, reflecting a new normal. Prior work has documented lower message use by patients who belong to minoritized racial...
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Keywords:
Health Pandemics;
Technology Adoption;
Prejudice and Bias;
Equality and Inequality;
Communication Technology;
Race;
Ethnicity;
Health Industry
Tang, Mitchell, Rebecca Mishuris, Lily Payvandi, and Ariel Dora Stern. "Differences in Care Team Response to Patient Portal Messages by Patient Race and Ethnicity." JAMA Network Open 7, no. 3 (March 2024).
- January 2024
- Case
Deion Sanders: The Prime Effect
By: Hise O. Gibson, Nicole Gilmore and Alicia Dadlani
In 2023, Deion Sanders, known as “Coach Prime,” became head football coach of the University of Colorado Boulder (CU). Sanders was tasked with leading CU’s struggling football program, which had only achieved one winning season in the last 15 years, back to glory. Many...
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Keywords:
Leadership Style;
Leading Change;
Management Style;
Race;
Prejudice and Bias;
Sports;
Experience and Expertise;
Sports Industry;
United States;
Colorado
Gibson, Hise O., Nicole Gilmore, and Alicia Dadlani. "Deion Sanders: The Prime Effect." Harvard Business School Case 624-001, January 2024.
- January 2024 (Revised May 2024)
- Case
Uncle Nearest: Creating a Legacy
By: Hise Gibson, Archie L. Jones, Nicole Gilmore and Ai-Ling Jamila Malone
Fawn Weaver, as a Black woman and industry outsider in a capital-intensive, highly regulated, competitive and male-dominated spirits industry, successfully overcame numerous obstacles to launch a premium American whiskey brand, Uncle Nearest in 2017, which became the...
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Keywords:
Advertising;
Business Startups;
Customer Focus and Relationships;
Decisions;
Forecasting and Prediction;
Age;
Ethnicity;
Gender;
Corporate Entrepreneurship;
Entrepreneurship;
Working Capital;
Innovation Leadership;
Innovation Strategy;
Intellectual Property;
Trademarks;
Leadership Style;
Growth and Development;
Growth and Development Strategy;
Marketing;
Product Marketing;
Product Launch;
Marketing Strategy;
Mission and Purpose;
Organizational Culture;
Private Ownership;
Performance Effectiveness;
Strategic Planning;
Problems and Challenges;
Prejudice and Bias;
Social Issues;
Strategy;
Competition;
Competitive Strategy;
Expansion;
Food and Beverage Industry;
Tourism Industry;
United States;
Tennessee;
France
Gibson, Hise, Archie L. Jones, Nicole Gilmore, and Ai-Ling Jamila Malone. "Uncle Nearest: Creating a Legacy." Harvard Business School Case 824-047, January 2024. (Revised May 2024.)
- 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...
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Keywords:
Passion;
Personality;
Extraversion;
Scale Development;
Personal Characteristics;
Perception;
Employees;
Prejudice and Bias
Krautter, Kai, Anabel Büchner, and Jon M. Jachimowicz. "Stop Assuming Introverts Aren't Passionate About Work." Harvard Business Review Digital Articles (December 4, 2023).
- November–December 2023
- Article
Look the Part? The Role of Profile Pictures in Online Labor Markets
By: Isamar Troncoso and Lan Luo
Profile pictures are a key component of many freelancing platforms, a design choice that can impact hiring and matching outcomes. In this paper, we examine how appearance-based perceptions of a freelancer’s fit for the job (i.e., whether a freelancer "looks the part"...
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Keywords:
Freelancers;
Gig Workers;
Demographics;
Prejudice and Bias;
Selection and Staffing;
Jobs and Positions;
Analytics and Data Science
Troncoso, Isamar, and Lan Luo. "Look the Part? The Role of Profile Pictures in Online Labor Markets." Marketing Science 42, no. 6 (November–December 2023): 1080–1100.
- October 2023
- Case
Making Progress at Progress Software (A)
By: Katherine Coffman, Hannah Riley Bowles and Alexis Lefort
In this case, the Human Capital team at Progress Software has identified that some employees have a hard time understanding how to advance within Progress. This realization leads the team to develop several major people-process innovations: the introduction of...
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- 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...
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- October 2023
- Supplement
Making Progress at Progress Software (B)
By: Katherine Coffman, Hannah Riley Bowles and Alexis Lefort
In this case, the Human Capital team at Progress Software has identified that some employees have a hard time understanding how to advance within Progress. This realization leads the team to develop several major people-process innovations: the introduction of...
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- September 29, 2023
- Article
Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI
By: Simon Friis and James Riley
When it comes to artificial intelligence and inequality, algorithmic bias rightly receives a lot of attention. But it’s just one way that AI can lead to inequitable outcomes. To truly create equitable AI, we need to consider three forces through which it might make...
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Friis, Simon, and James Riley. "Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI." Harvard Business Review (website) (September 29, 2023).
- September 2023
- Exercise
Irrationality in Action: Decision-Making Exercise
By: Alison Wood Brooks, Michael I. Norton and Oliver Hauser
This teaching exercise highlights the obstacle of biases in decision-making, allowing students to generate examples of potentially poor decision-making rooted in abundant and unwanted bias. This exercise has two parts: a pre-class, online survey in which students...
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Brooks, Alison Wood, Michael I. Norton, and Oliver Hauser. "Irrationality in Action: Decision-Making Exercise." Harvard Business School Exercise 924-007, September 2023.
- 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...
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Menghani, Neil, Edward McFowland III, and Daniel B. Neill. "Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness." Working Paper, June 2023.
- 2023
- Working Paper
Black Empowerment and White Mobilization: The Effects of the Voting Rights Act
By: Andrea Bernini, Giovanni Facchini, Marco Tabellini and Cecilia Testa
The 1965 Voting Rights Act (VRA) paved the road to Black empowerment. How did
southern whites respond? Leveraging newly digitized data on county-level voter registration
rates by race between 1956 and 1980, and exploiting pre-determined variation
in exposure to the...
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Bernini, Andrea, Giovanni Facchini, Marco Tabellini, and Cecilia Testa. "Black Empowerment and White Mobilization: The Effects of the Voting Rights Act." Harvard Business School Working Paper, No. 23-075, June 2023. (Revise and resubmit at the Journal of Political Economy. Also available on Vox EU and VoxDev. Featured on HBS Working Knowledge.)
- June 2023
- Article
Amplification of Emotion on Social Media
By: Amit Goldenberg and Robb Willer
Why do expressions of emotion seem so heightened on social media? Brady et al. argue that extreme moral outrage on social media is not only driven by the producers and sharers of emotional expressions, but also by systematic biases in the way people that perceive moral...
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Goldenberg, Amit, and Robb Willer. "Amplification of Emotion on Social Media." Nature Human Behaviour 7, no. 6 (June 2023): 845–846.
- May 9, 2023
- Article
8 Questions About Using AI Responsibly, Answered
By: Tsedal Neeley
Generative AI tools are poised to change the way every business operates. As your own organization begins strategizing which to use, and how, operational and ethical considerations are inevitable. This article delves into eight of them, including how your organization...
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Neeley, Tsedal. "8 Questions About Using AI Responsibly, Answered." Harvard Business Review (website) (May 9, 2023).
- 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...
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Keywords:
STEM;
Selection and Staffing;
Gender;
Prejudice and Bias;
Training;
Equality and Inequality;
Competency and Skills
Lane, Jacqueline N., Karim R. Lakhani, and Roberto Fernandez. "Setting Gendered Expectations? Recruiter Outreach Bias in Online Tech Training Programs." Harvard Business School Working Paper, No. 23-066, April 2023. (Accepted by Organization Science.)
- 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...
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Chang, Peter W., Leor Fishman, and Seth Neel. "Feature Importance Disparities for Data Bias Investigations." Working Paper, March 2023.