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(349)
- News (54)
- Research (175)
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- 17 Nov 2013
- News
Time to Tackle Workplace Gender Bias
- 13 Sep 2013
- News
Educate Everyone About Second-Generation Gender Bias
- October 2015 (Revised January 2017)
- Exercise
Gender at Work
By: Boris Groysberg and Colleen Ammerman
Groysberg, Boris, and Colleen Ammerman. "Gender at Work." Harvard Business School Exercise 416-026, October 2015. (Revised January 2017.)
- 21 Aug 2013
- News
Educate Everyone About Second-Generation Gender Bias
- 2024
- Working Paper
A Gender Backlash: Does Exposure to Female Labor Market Participation Fuel Gender Conservatism?
By: Paula Rettl, Diane Bolet, Catherine E. De Vries, Simone Cremaschi, Tarik Abou-Chadi and Sergi Pardos-Prado
The growing participation of women in the labor market has marked a significant societal transformation, coinciding with the rise of gender conservatism and far-right support. We study whether the economic consequences of labor market feminization and gender backlash... View Details
Keywords: Gender Bias; Gender Equality; Gender Inclusivity; Politics; Political Backlash; Political Culture; Conservatism; Gender; Government and Politics; Equality and Inequality; Prejudice and Bias; Labor
Rettl, Paula, Diane Bolet, Catherine E. De Vries, Simone Cremaschi, Tarik Abou-Chadi, and Sergi Pardos-Prado. "A Gender Backlash: Does Exposure to Female Labor Market Participation Fuel Gender Conservatism?" Harvard Business School Working Paper, No. 25-022, November 2024.
- 19 Jun 2020
- News
Leaders, Stop Denying the Gender Inequity in Your Organization
Keywords: gender bias
- 08 Mar 2022
- News
Gender Equity at Work Advances at 'Glacial Pace,' New Harvard Survey Shows
Keywords: gender bias
- 19 Nov 2019
- Op-Ed
Gender Bias Complaints against Apple Card Signal a Dark Side to Fintech
In late August, the Apple Card debuted with a minimalist look and completely “no fee” model, creating a frenzy of anticipation. Millions signed up to be alerted for the release. Designed to boost traffic to its slow-to-be-adopted Apple Pay system and increase consumer... View Details
- 05 Jan 2017
- Blog Post
Simple Ways to Take Gender Bias Out of Your Jobs
take the gender bias out of job listings: Simply rewrite them. “Our minds are stubborn beasts that are hard to change, but it’s not hard to de-bias the application process,” says behavioral economist Iris... View Details
Keywords: All Industries
- 10 Oct 2014
- News
Ending Gender Discrimination Requires More than a Training Program
- May–June 2021
- Article
How to Close the Gender Gap
By: Colleen Ammerman and Boris Groysberg
Most companies say they’re committed to advancing women into leadership roles. What they may fail to recognize, though, is that systemic barriers are holding women back. As a result, women remain disadvantaged at every stage of their employment and underrepresented in... View Details
Keywords: Gender Discrimination; Employment; Gender; Prejudice and Bias; Talent and Talent Management; Organizational Change and Adaptation
Ammerman, Colleen, and Boris Groysberg. "How to Close the Gender Gap." Harvard Business Review 99, no. 3 (May–June 2021): 124–133.
- Forthcoming
- 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 (forthcoming). (Pre-published online May 31, 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... 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.)
- September 2020 (Revised July 2022)
- Technical Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeting; Targeted Advertising; Pricing Algorithms; Ethical Decision Making; Customer Heterogeneity; Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Marketing Communications; Analytics and Data Science; Analysis; Decision Making; Ethics; Customer Relationship Management; E-commerce; Retail Industry; Apparel and Accessories Industry; United States
Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020. (Revised July 2022.)
- September 2020 (Revised July 2022)
- Exercise
Artea (D): Discrimination through Algorithmic Bias in 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: Targeted Advertising; Discrimination; Algorithmic Data; Bias; Advertising; Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea (D): Discrimination through Algorithmic Bias in Targeting." Harvard Business School Exercise 521-043, September 2020. (Revised July 2022.)
- 04 Dec 2017
- News