Overview
AI bias is not usually intentional; it is often a reflection of existing biases in the real world that are captured in the training data.
Sources of Bias
- Data Bias: Training data that overrepresents or underrepresents certain groups.
- Algorithmic Bias: The design of the algorithm itself favoring certain patterns.
- Human Bias: Biases of the developers who choose the data and define the goals.
Consequences
Can lead to unfair treatment in hiring, lending, law enforcement, and healthcare. Addressing AI bias is a core part of AI Ethics.