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Show Results For
- All HBS Web
(1,117)
- News (190)
- Research (748)
- Events (8)
- Multimedia (18)
- Faculty Publications (498)
- 08 Jan 2013
- News
Study Suggests Fix for Gender Bias on the Job
- 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.)
- May 2022
- Case
Timnit Gebru: 'SILENCED No More' on AI Bias and The Harms of Large Language Models
By: Tsedal Neeley and Stefani Ruper
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 she accepted your resignation.” Heart... View Details
Neeley, Tsedal, and Stefani Ruper. "Timnit Gebru: 'SILENCED No More' on AI Bias and The Harms of Large Language Models." Harvard Business School Case 422-085, May 2022.
- 2003
- Working Paper
Auditor Independence, Conflict of Interest, and the Unconscious Intrusion of Bias
By: Don A. Moore, George Loewenstein, Lloyd Tanlu and Max H. Bazerman
- November–December 2019
- Article
Making Sense of Soft Information: Interpretation Bias and Loan Quality
By: Dennis Campbell, Maria Loumioti and Regina Wittenberg Moerman
We explore whether behavioral biases impede the effective processing and interpretation of soft information in private lending. Taking advantage of the internal reporting system of a large federal credit union, we delineate three important biases likely to affect the... View Details
Keywords: Soft Information; Lending; Banking; Information; Financing and Loans; Banks and Banking; Decision Making
Campbell, Dennis, Maria Loumioti, and Regina Wittenberg Moerman. "Making Sense of Soft Information: Interpretation Bias and Loan Quality." Art. 101240. Journal of Accounting & Economics 68, nos. 2-3 (November–December 2019).
- 29 Sep 2023
- News
Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI
- 16 Feb 2021
- News
To Reduce Gender Bias in Hiring, Make Your Shortlist Longer
- 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).
- 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
- 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 Take Gender View Details
Keywords: by Sean Silverthorne
- 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; Retail Industry; Apparel and Accessories Industry; Technology 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.)
- 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.)
- May 28, 2018
- Article
How Companies Can Identify Racial and Gender Bias in Their Customer Service
By: Alexandra C. Feldberg and Tami Kim
Research shows that minority customers — blacks and Asians — regularly receive worse customer service than whites in ways that are not immediately obvious to onlookers (or even managers). These results prompt a couple of questions for executives and managers. One, does... View Details
Keywords: Internal Audit; Customers; Service Delivery; Prejudice and Bias; Race; Gender; Organizational Change and Adaptation
Feldberg, Alexandra C., and Tami Kim. "How Companies Can Identify Racial and Gender Bias in Their Customer Service." Harvard Business Review (website) (May 28, 2018).
- 26 Apr 2018
- Video
2018 G&WS: A Conversation with David M. Porter on Implicit Bias
- 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.
- 27 Jul 2023
- News
How to Really Deal with Implicit Bias at Work with Laura Huang
- 04 Mar 2017
- News