Engaging Diverse Stakeholders in Development

Algorithmic fairness frameworks emphasize participatory design involving gender-diverse groups. Input from various stakeholders uncovers subtle biases, informs fairness goals, and guides evaluation criteria, leading to AI systems that better respect gender equity.

Algorithmic fairness frameworks emphasize participatory design involving gender-diverse groups. Input from various stakeholders uncovers subtle biases, informs fairness goals, and guides evaluation criteria, leading to AI systems that better respect gender equity.

Empowered by Artificial Intelligence and the women in tech community.
Like this article?

AI and Bias: How Gender Affects Algorithms

Interested in sharing your knowledge ?

Learn more about how to contribute.

Sponsor this category.

More articles on AI and Bias: How Gender Affects Algorithms

See all

More articles from related categories

What Lessons Can We Learn from Collaborative Projects Between Baby Boomer, Gen X, Millennial, and Gen Z Women in Tech?
Generational Shifts in Women’s Career Expectations
What Can Gender Representation Statistics Tell Us About Pay Equity in Technology?
State of DEI in Tech: Annual Report
How Does Intersectionality Influence Strategies for Closing the Gender Pay Gap?
Tracking Gender Pay Gap Progress
What Recruitment Strategies Are Innovating to Attract Diverse Female Talent?
Top Companies for Women in Tech (By Category)