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All HBS Web
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- Faculty Publications (324)
Bias →
- 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
Second- versus Third-party Audit Quality: Evidence from Global Supply Chain Monitoring
By: Maria R. Ibanez, Ashley Palmarozzo, Jodi L. Short and Michael W. Toffel
To capitalize on the superior credibility and flexibility and lower cost of external assessments, many global buyers are shifting from using their own employee (“second-party”) auditors to relying more heavily (or entirely) on third-party auditors to monitor and...
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Keywords:
Auditing;
Audit Quality;
Working Conditions;
Sustainability;
Empirical Operations;
Empirical Service Operations;
Sustainability Management;
Corporate Accountability;
Agency Theory;
Corporate Social Responsibility and Impact;
Supply Chain Management
Ibanez, Maria R., Ashley Palmarozzo, Jodi L. Short, and Michael W. Toffel. "Second- versus Third-party Audit Quality: Evidence from Global Supply Chain Monitoring." Working Paper, June 2023.
- June 2023
- Simulation
Artea Dashboard and Targeting Policy Evaluation
By: Ayelet Israeli and Eva Ascarza
Companies deploy A/B experiments to gain valuable insights about their customers in order to answer strategic business problems. In marketing, A/B tests are often used to evaluate marketing interventions intended to generate incremental outcomes for the firm. The Artea...
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Keywords:
Algorithm Bias;
Algorithmic Data;
Race And Ethnicity;
Experimentation;
Promotion;
Marketing And Society;
Big Data;
Privacy;
Data-driven Management;
Data Analysis;
Data Analytics;
E-Commerce Strategy;
Discrimination;
Targeted Advertising;
Targeted Policies;
Pricing Algorithms;
A/B Testing;
Ethical Decision Making;
Customer Base Analysis;
Customer Heterogeneity;
Coupons;
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
- 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.
- 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...
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Boxer, Kate S., Edward McFowland III, and Daniel B. Neill. "Auditing Predictive Models for Intersectional Biases." Working Paper, June 2023.
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider...
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Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
- 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
- Article
Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations.
By: Edward McFowland III and Cosma Rohilla Shalizi
Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, that is, with a node’s network partners being informative about the node’s attributes and therefore its...
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Keywords:
Causal Inference;
Homophily;
Social Networks;
Peer Influence;
Social and Collaborative Networks;
Power and Influence;
Mathematical Methods
McFowland III, Edward, and Cosma Rohilla Shalizi. "Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations." Journal of the American Statistical Association 118, no. 541 (2023): 707–718.
- 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.
- 2023
- Working Paper
The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities
By: David S. Scharfstein and Sergey Chernenko
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in...
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Keywords:
Racial Disparity;
Paycheck Protection Program;
Measurement Error;
AI and Machine Learning;
Race;
Measurement and Metrics;
Equality and Inequality;
Prejudice and Bias;
Forecasting and Prediction;
Outcome or Result
Scharfstein, David S., and Sergey Chernenko. "The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities." Working Paper, April 2023.
- February 2023
- Teaching Note
Colette Phillips and GetKonnected: Creating Inclusive Ecosystems
By: Rosabeth M. Kanter and Ai-Ling Jamila Malone
Teaching Note for HBS Case No. 323-035.
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Keywords:
Diversity;
Ecosystem;
Inclusion;
People Of Color;
Network;
Racial Bias;
Gender Bias;
Entrepreneur;
Entrepreneurial Ecosystems;
Change;
Change Barriers;
Change Leadership;
Community;
Innovation;
Pandemic;
Impact;
Systemic Racism;
Minority-owned Businesses;
Social and Collaborative Networks;
Equity;
Race;
Small Business;
Prejudice and Bias;
Boston
- 2024
- Working Paper
Everyone Steps Back?: The Widespread Retraction of Crowd-Funding Support for Minority Creators When Migration Fear Is High
By: John (Jianqui) Bai, William R. Kerr, Chi Wan and Alptug Yorulmaz
We study racial biases on Kickstarter across multiple ethnic groups from 2009-2021. Scaling the concept of racially salient events, we quantify the close co-movement of minority funding gaps to inflamed political rhetoric surrounding migration. The racial funding gap...
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Bai, John (Jianqui), William R. Kerr, Chi Wan, and Alptug Yorulmaz. "Everyone Steps Back? The Widespread Retraction of Crowd-Funding Support for Minority Creators When Migration Fear Is High." Harvard Business School Working Paper, No. 23-046, January 2023. (Revised February 2024.)
- 2023
- Working Paper
The Benefits of Revealing Race: Evidence from Minority-owned Local Businesses
By: Abhay Aneja, Michael Luca and Oren Reshef
Is there latent demand to support Black-owned businesses? To explore, we analyze a new feature
that made it easier to identify Black-owned restaurants on a large online platform. We find that
labeling restaurants as “Black-owned” increased customer engagement and...
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Keywords:
Black-owned Businesses;
Race;
Prejudice and Bias;
Ownership;
Knowledge Dissemination;
Digital Platforms;
Consumer Behavior;
Food and Beverage Industry
Aneja, Abhay, Michael Luca, and Oren Reshef. "The Benefits of Revealing Race: Evidence from Minority-owned Local Businesses." Harvard Business School Working Paper, No. 23-042, January 2023. (Revised September 2023.)
- January 2023
- Article
Racial Diversity and Racial Policy Preferences: The Great Migration and Civil Rights
By: Alvaro Calderon, Vasiliki Fouka and Marco Tabellini
Between 1940 and 1970, more than 4 million African Americans moved from the South to the North of the United States, during the Second Great Migration. This same period witnessed the struggle and eventual success of the civil rights movement in ending institutionalized...
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Keywords:
Civil Rights;
Great Migration;
History;
Race;
Rights;
Prejudice and Bias;
Government Legislation
Calderon, Alvaro, Vasiliki Fouka, and Marco Tabellini. "Racial Diversity and Racial Policy Preferences: The Great Migration and Civil Rights." Review of Economic Studies 90, no. 1 (January 2023): 165–200. (Available also from VOX, Broadstreet, and VOX EU.)
- December 2022
- Article
Competition, Contracts, and Creativity: Evidence from Novel Writing in a Platform Market
By: Yanhui Wu and Feng Zhu
A growing number of people today are participating in the gig economy, working as independent contractors on short-term projects. We study the effects of competition on gig workers' effort and creativity on a Chinese novel-writing platform. Authors produce and sell...
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Keywords:
Gig Workers;
Platform-based Markets;
Novel Writing;
Creative Production;
Platform Bias;
Employment;
Digital Platforms;
Creativity;
Books;
Competition;
Contracts
Wu, Yanhui, and Feng Zhu. "Competition, Contracts, and Creativity: Evidence from Novel Writing in a Platform Market." Management Science 68, no. 12 (December 2022): 8613–8634.
- December 2022
- Article
Different Roots, Different Fruits: Gender-Based Differences in Cultural Narratives about Perceived Discrimination Produce Divergent Psychological Consequences
By: Leigh Plunkett Tost, Ashley E. Hardin and Francesca Gino
We examine whether narratives about, and the psychological consequences of, perceived gender discrimination differ between women and men. We argue that women and men have different dominant narratives about the reasons why people discriminate against people of their...
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Tost, Leigh Plunkett, Ashley E. Hardin, and Francesca Gino. "Different Roots, Different Fruits: Gender-Based Differences in Cultural Narratives about Perceived Discrimination Produce Divergent Psychological Consequences." Academy of Management Journal 65, no. 6 (December 2022): 1804–1834.
- 2024
- Working Paper
Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information
which an algorithm does not have access to that can improve performance. How can we help humans effectively use
and adjust recommendations made by...
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Keywords:
Cognitive Biases;
Algorithm Transparency;
Forecasting and Prediction;
Behavior;
AI and Machine Learning;
Analytics and Data Science;
Cognition and Thinking
Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence." Working Paper, February 2024.