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 Can We Uncover Hidden Biases in AI Systems?
Detecting and Mitigating AI Bias
How Can AI Empower Women Entrepreneurs in Developing Countries?
AI and Social Impact
Can Community-Led Initiatives Be the Solution to AI Bias?
Community Awareness of AI Bias
What Challenges Do Women Face in Achieving Inclusive AI Design?
Inclusive AI Design