For Heads-of · Practitioner
Algorithmic Fairness
The principle that machine learning systems should treat individuals or groups equitably, not systematically disadvantaging people based on protected characteristics.
- fairness
- discrimination
- equity
- bias mitigation
Fairness Definitions
Multiple formal definitions exist: demographic parity (equal outcomes across groups), equalized odds (equal error rates), calibration (predictions accurate within each group), and individual fairness (similar cases treated similarly).
Tension Between Definitions
Different fairness definitions can be mathematically incompatible; satisfying one may violate another. Organizations must choose which definition aligns with their values and legal obligations.
Governance Requirement
Fairness analysis is increasingly expected by regulators and stakeholders. Organizations must measure fairness across demographic groups, identify disparities, and implement mitigations.