What is AI Bias? Detection and Monitoring

What is AI bias?

AI bias occurs when models produce systematically unfair outputs that favor or disadvantage certain groups. This can stem from biased training data, flawed model design, or problematic deployment contexts.

Types of AI Bias

Training data bias: Underrepresentation or stereotypes in data
Algorithmic bias: Model architecture amplifies patterns
Deployment bias: Misuse in inappropriate contexts
Confirmation bias: Reinforcing existing beliefs

Detecting Bias

Test with diverse demographic inputs
Monitor policy violations for discrimination
Audit outputs for unfair patterns
Track metrics across user segments

Regulatory Requirements

Many jurisdictions now require bias monitoring:

EU AI Act requires bias assessments
NYC Local Law 144 for hiring AI
State insurance regulations
Fair lending requirements

How do I detect bias?

Monitor outputs across demographic groups, test with diverse inputs, track policy violations related to discrimination, and audit outputs for patterns of unfair treatment. Continuous monitoring catches bias that testing misses.