Bridging the Gender Data Gap in AI

One core issue at the heart of AI bias against women is the gender data gap. Historically, data has often been collected from male-centric perspectives, leading to algorithms that fail to accurately predict or understand female needs and behaviours. To remedy this, researchers and developers must prioritize the collection and incorporation of female-focused data and scrutinize existing data sets for gender biases.

One core issue at the heart of AI bias against women is the gender data gap. Historically, data has often been collected from male-centric perspectives, leading to algorithms that fail to accurately predict or understand female needs and behaviours. To remedy this, researchers and developers must prioritize the collection and incorporation of female-focused data and scrutinize existing data sets for gender biases.

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

Fairness in AI Algorithms

Interested in sharing your knowledge ?

Learn more about how to contribute.

More articles on Fairness in AI Algorithms

See all

More articles from related categories

How Can We Leverage AI to Promote Gender Equality in the Workplace?
Fairness in AI Algorithms
Certainly! Below are 10 LinkedIn-style collaborative article titles tailored to the theme of detecting and mitigating AI bias, all posed as questions to engage the WomenTech Network audience
Detecting and Mitigating AI Bias
Why Is Our AI Biased? The Hidden Influence of Training Data
Training Data Bias
What Steps Should We Take to Ensure AI Design Is Inclusive of All Women?
Inclusive AI Design