Limited Diversity in Training Data

A fundamental reason behind AI bias is the lack of diversity in the datasets used for training. When an AI system is trained on data that predominantly represents a particular demographic, it struggles to accurately understand and make decisions about individuals outside of that demographic, leading to biased outputs.

A fundamental reason behind AI bias is the lack of diversity in the datasets used for training. When an AI system is trained on data that predominantly represents a particular demographic, it struggles to accurately understand and make decisions about individuals outside of that demographic, leading to biased outputs.

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

Training Data Bias

Interested in sharing your knowledge ?

Learn more about how to contribute.

More articles on Training Data Bias

See all

More articles from related categories

How Effective Are Current Methods in Detecting Bias in Training Data? A Critical Review
Training Data Bias
What Steps Can Our Community Take to Ensure Fair AI for Everyone?
Community Awareness of AI Bias
How Is AI Transforming Maternal Healthcare in Remote Areas?
AI for Social Good
What Does Truly Inclusive AI Design Look Like from a Woman's Perspective?
Inclusive AI Design