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(1,099)
- News (185)
- Research (762)
- Events (8)
- Multimedia (14)
- Faculty Publications (497)
Show Results For
- All HBS Web
(1,099)
- News (185)
- Research (762)
- Events (8)
- Multimedia (14)
- Faculty Publications (497)
- 2018
- Working Paper
Moral Prospection: Cognitive Bias and the Failure to Predict Moral Backlash Toward an Organization
By: J. Lees
- 03 Jun 2022
- News
Research Shows Racial Bias Is Real. Are We Ready to Talk about It?
- 2023
- Working Paper
Complexity and Hyperbolic Discounting
By: Benjamin Enke, Thomas Graeber and Ryan Oprea
A large literature shows that people discount financial rewards hyperbolically instead of exponentially. While discounting of money has been questioned as a measure of time preferences, it continues to be highly relevant in empirical practice and predicts a wide range... View Details
Keywords: Hyperbolic Discounting; Present Bias; Bounded Rationality; Cognitive Uncertainty; Behavioral Finance
Enke, Benjamin, Thomas Graeber, and Ryan Oprea. "Complexity and Hyperbolic Discounting." Harvard Business School Working Paper, No. 24-048, February 2024.
- Article
Home Bias at Home: Local Equity Preference in Domestic Portfolios
By: Joshua D. Coval and Tobias J. Moskowitz
Coval, Joshua D., and Tobias J. Moskowitz. "Home Bias at Home: Local Equity Preference in Domestic Portfolios." Journal of Finance 54, no. 6 (December 1999). (Winner of Smith Breeden Prize. Best Paper For the best finance research paper published in the Journal of Finance presented by Smith Breeden Associates, Inc.)
- June 2010
- Article
Correspondence Bias in Performance Evaluation: Why Grade Inflation Works
By: D. A. Moore, S. A. Swift, Z. S. Sharek and F. Gino
Moore, D. A., S. A. Swift, Z. S. Sharek, and F. Gino. "Correspondence Bias in Performance Evaluation: Why Grade Inflation Works." Personality and Social Psychology Bulletin 36, no. 6 (June 2010): 843–852.
- March 2021
- Supplement
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Power Point Supplement to 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... View Details
Keywords: Targeted Advertising; Targeting; Algorithmic Data; Bias; A/B Testing; Experiment; Advertising; Gender; Race; 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
- 08 Jan 2013
- News
Study Suggests Fix for Gender Bias on the Job
- 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.)
- 16 Feb 2021
- News
To Reduce Gender Bias in Hiring, Make Your Shortlist Longer
- May–June 2024
- Article
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... View Details
Lane, Jacqueline N., Karim R. Lakhani, and Roberto Fernandez. "Setting Gendered Expectations? Recruiter Outreach Bias in Online Tech Training Programs." Organization Science 35, no. 3 (May–June 2024): 911–927.
- 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... View Details
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.)
- Summer 2021
- Article
Predictable Country-level Bias in the Reporting of COVID-19 Deaths
By: Botir Kobilov, Ethan Rouen and George Serafeim
We examine whether a country’s management of the COVID-19 pandemic relate to the downward biasing of the number of reported deaths from COVID-19. Using deviations from historical averages of the total number of monthly deaths within a country, we find that the... View Details
Keywords: COVID-19; Deaths; Reporting; Incentives; Government Policy; Health Pandemics; Health Care and Treatment; Country; Crisis Management; Outcome or Result; Reports; Policy
Kobilov, Botir, Ethan Rouen, and George Serafeim. "Predictable Country-level Bias in the Reporting of COVID-19 Deaths." Journal of Government and Economics 2 (Summer 2021).
- 17 May 2018
- Sharpening Your Skills
You Probably Have a Bias for Making Bad Decisions. Here's Why.
entrepreneurs, even when the content of the pitches is identical. And handsome men fare best of all. Why Employers Favor Men Why are women discriminated against in hiring decisions? The answer is more subtle than expected. Simple Ways to... View Details
Keywords: by Sean Silverthorne
- 2003
- Article
Don't Blame the Computer: When Self-Disclosure Moderates the Self-Serving Bias
By: Youngme Moon
Moon, Youngme. "Don't Blame the Computer: When Self-Disclosure Moderates the Self-Serving Bias." Journal of Consumer Psychology 13, nos. 1-2 (2003).
- 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... View Details
Boxer, Kate S., Edward McFowland III, and Daniel B. Neill. "Auditing Predictive Models for Intersectional Biases." Working Paper, June 2023.
- 2015
- Working Paper
Blinded by Experience: Prior Experience, Negative News and Belief Updating
By: Bradley R. Staats, Diwas S. KC and Francesca Gino
Traditional models of operations management involve dynamic decision-making assuming optimal (Bayesian) updating. However, behavioral theory suggests that individuals exhibit bias in their beliefs and decisions. We conduct both a field study and two laboratory studies... View Details
Keywords: Behavioral Operations; Egocentric Bias; Experience; Healthcare Operations; Prejudice and Bias; Behavior; Operations; Decision Making; Health Care and Treatment
Staats, Bradley R., Diwas S. KC, and Francesca Gino. "Blinded by Experience: Prior Experience, Negative News and Belief Updating." Harvard Business School Working Paper, No. 16-015, August 2015.
- November 30, 2020
- Editorial
Don't Focus on the Most Expressive Face in the Audience
By: Amit Goldenberg and Erika Weisz
Research has shown that when speaking in front of a group, people’s attention tends to gets stuck on the most emotional faces, causing them to overestimate the group’s average emotional state. In this piece, the authors share two additional findings: First, the larger... View Details
Goldenberg, Amit, and Erika Weisz. "Don't Focus on the Most Expressive Face in the Audience." Harvard Business Review (website) (November 30, 2020).
- 19 May 2015
- News
Harvard aims to take on gender bias with new initiative
- September 2018
- Article
Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopædia Britannica and Wikipedia
By: Shane Greenstein and Feng Zhu
Organizations today can use both crowds and experts to produce knowledge. While prior work compares the accuracy of crowd-produced and expert-produced knowledge, we compare bias in these two models in the context of contested knowledge, which involves subjective,... View Details
Keywords: Online Community; Collective Intelligence; Wisdom Of Crowds; Bias; Wikipedia; Britannica; Knowledge Production; Knowledge Sharing; Knowledge Dissemination; Prejudice and Bias
Greenstein, Shane, and Feng Zhu. "Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopædia Britannica and Wikipedia." MIS Quarterly 42, no. 3 (September 2018): 945–959.
- 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.)