Fairness and Bias in AI

In more detail

Bias can come from training data, model architecture, or evaluation. Fairness ensures equitable treatment across demographics. Metrics include demographic parity, equalized odds, etc. Important for responsible AI.

Examples

• Hiring algorithms with gender bias
• Facial recognition accuracy disparities
• Language model stereotypes
• Lending bias