Gender Equality in the Workplace
In celebration of Gender Equality Month and International’s Women’s Day, we’ve gathered articles from the MIT SMR library focused on furthering gender equality and progress in organizations, reducing bias in hiring and promotion, and supporting physical, mental, and emotional health within the workforce.
Getting Action-Oriented About Gender and Racial Equity at Work
Three recent books on workplace sexism and racism highlight concrete actions leaders can take to support diversity.
Promoting Equality from the Top
Culture
The Toxic Culture Gap Shows Companies Are Failing Women
Employers must recognize that women are 41% more likely to experience toxic culture in the workplace than men are.
Diversity & Inclusion
Women Are Stalling Out on the Way to the Top
Analysis shows that women in senior leadership are largely stuck in support functions, not moving into key operating roles.
Developing Strategy
Make Gender Equality a Value, Not a Priority
To make gender equality a reality, organizations need to look at values, not priorities.
Boards & Corporate Governance
Why the Influence of Women on Boards Still Lags
The number of women on corporate boards has risen substantially over the past decade, but the growth rate is slowing. Why?
Leading Change
Gender Diversity at the Board Level Can Mean Innovation Success
Recruiting women directors can pave the way for long-term support of innovation and creativity.
Supporting a Diverse Leadership Pipeline
Equality at Home and at Work
Breaking Bias in Hiring and Promotion
Protecting Women’s Health and Safety
Using Technology to Advance Gender Equality
AI & Machine Learning
Could AI Be the Cure for Workplace Gender Inequality?
Artificial intelligence is beginning to replace many of the workplace roles that men dominate.
Analytics & Business Intelligence
A Data-Driven Approach to Identifying Future Leaders
Some companies are using assessment tools to help identify employees with leadership potential.
AI & Machine Learning
Real Talk: Intersectionality and AI
In artificial intelligence, race and gender too often generate a bias double whammy.