For Heads-of · Practitioner
Fairness & Bias
The principle and practice of ensuring AI systems do not produce systematically different outcomes for individuals or groups based on protected characteristics or vulnerable attributes.
- fairness
- bias
- discrimination
- equity
Fairness
Fairness requires that AI decisions respect human dignity and equal opportunity. Definitions vary: demographic parity (equal rates across groups), equalized odds (equal error rates), or individual fairness (similar individuals treated similarly).
Bias
Bias is the systematic deviation from fairness. It can arise from training data, feature selection, optimization objectives, or deployment context. Bias is often unintentional but predictable.
Regulatory Landscape
EU AI Act treats bias in high-risk AI as prohibited; NIST AI RMF includes fairness/bias assessment; ICO AI guidance emphasizes mitigation.