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(1,117)
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Show Results For
- All HBS Web
(1,117)
- News (190)
- Research (748)
- Events (8)
- Multimedia (18)
- Faculty Publications (498)
- 27 Jul 2023
- News
How to Really Deal with Implicit Bias at Work with Laura Huang
- 04 Mar 2017
- News
No simple fix to weed out racial bias in the sharing economy
- 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... View Details
- Article
Whites See Racism as a Zero-Sum Game That They Are Now Losing
By: Michael I. Norton and Samuel R. Sommers
Although some have heralded recent political and cultural developments as signaling the arrival of a post-racial era in America, several legal and social controversies regarding "reverse racism" highlight Whites' increasing concern about anti-White bias. We show that... View Details
Keywords: Racism; Zero-sum Game; Bias; Affirmative Action; Prejudice and Bias; Race; Social Issues; United States
Norton, Michael I., and Samuel R. Sommers. "Whites See Racism as a Zero-Sum Game That They Are Now Losing." Perspectives on Psychological Science 6, no. 3 (May 2011): 215–218.
- 10 Oct 2017
- News
Radical Transparency Can Reduce Bias — but Only If It’s Done Right
- 10 Jan 2013
- Working Paper Summaries
The Novelty Paradox & Bias for Normal Science: Evidence from Randomized Medical Grant Proposal Evaluations
- 2024
- Working Paper
Should I Stay or Should I Disclose? How Omission Bias Guides Our Disclosure Decisions
By: Elinora Pentcheva and Leslie John
- 2018
- Working Paper
Moral Prospection: Cognitive Bias and the Failure to Predict Moral Backlash Toward an Organization
By: J. Lees
- 2008
- Article
Warmth and Competence As Universal Dimensions of Social Perception: The Stereotype Content Model and the BIAS Map
By: A. J.C. Cuddy, S. T. Fiske and P. Glick
The stereotype content model (SCM) defines two fundamental dimensions of social perception, warmth and competence, predicted respectively by perceived competition and status. Combinations of warmth and competence generate distinct emotions of admiration, contempt,... View Details
Keywords: Perception; Competency and Skills; Prejudice and Bias; Emotions; Business Model; Behavior; Research; Competition; Status and Position; Cognition and Thinking; Groups and Teams
Cuddy, A. J.C., S. T. Fiske, and P. Glick. "Warmth and Competence As Universal Dimensions of Social Perception: The Stereotype Content Model and the BIAS Map." Advances in Experimental Social Psychology 40 (2008): 61–149.
- 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
- 19 Jun 2020
- News
Leaders, Stop Denying the Gender Inequity in Your Organization
Keywords: gender bias
- 29 Jun 2015
- News
Facebook COO Sheryl Sandberg offers Tsinghua University grads 4 sound lessons in leadership
Keywords: gender bias
- September 2019
- Case
Sonia Millar: Negotiating for the C-Suite
By: Joshua D. Margolis and Anne Donnellon
This case addresses the nuances of gender dynamics and career progression at the top of the organization, where even women who have strong leadership expertise, experience, and alliances with powerful male colleagues still get stuck. Told from the point of view of... View Details
Keywords: Executives; CEO; Promotion; Gender Bias; Personal Development and Career; Gender; Diversity; Power and Influence
Margolis, Joshua D., and Anne Donnellon. "Sonia Millar: Negotiating for the C-Suite." Harvard Business School Brief Case 920-555, September 2019.
- June 18, 2021
- Article
Who Do We Invent for? Patents by Women Focus More on Women's Health, but Few Women Get to Invent
By: Rembrand Koning, Sampsa Samila and John-Paul Ferguson
Women engage in less commercial patenting and invention than do men, which may affect what is invented. Using text analysis of all U.S. biomedical patents filed from 1976 through 2010, we found that patents with all-female inventor teams are 35% more likely than... View Details
Keywords: Innovation; Gender Bias; Health; Innovation and Invention; Research; Patents; Gender; Prejudice and Bias
Koning, Rembrand, Sampsa Samila, and John-Paul Ferguson. "Who Do We Invent for? Patents by Women Focus More on Women's Health, but Few Women Get to Invent." Science 372, no. 6548 (June 18, 2021): 1345–1348.
- Article
Overcoming the Outcome Bias: Making Intentions Matter
People often make the well-documented mistake of paying too much attention to the outcomes of others’ actions while neglecting information about the original intentions leading to those outcomes. In five experiments, we examine interventions aimed at reducing this... View Details
Keywords: Outcome Bias; Intentions; Joint Evaluation; Judgment; Separate Evaluation; Goals and Objectives; Prejudice and Bias; Judgments; Performance Evaluation; Outcome or Result
Sezer, Ovul, Ting Zhang, Francesca Gino, and Max Bazerman. "Overcoming the Outcome Bias: Making Intentions Matter." Organizational Behavior and Human Decision Processes 137 (November 2016): 13–26.
- Article
The Mixed Effects of Online Diversity Training
By: Edward H. Chang, Katherine L. Milkman, Dena M. Gromet, Robert W. Rebele, Cade Massey, Angela L. Duckworth and Adam M. Grant
We present results from a large (n = 3,016) field experiment at a global organization testing whether a brief science-based online diversity training can change attitudes and behaviors toward
women in the workplace. Our preregistered field experiment included an... View Details
Chang, Edward H., Katherine L. Milkman, Dena M. Gromet, Robert W. Rebele, Cade Massey, Angela L. Duckworth, and Adam M. Grant. "The Mixed Effects of Online Diversity Training." Proceedings of the National Academy of Sciences 116, no. 16 (April 16, 2019): 7778–7783.
- 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; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
- 08 Mar 2022
- News
Gender Equity at Work Advances at 'Glacial Pace,' New Harvard Survey Shows
Keywords: gender bias
- 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.