Fairness

Fairness in AI refers to the property that a model's decisions or outputs treat individuals and groups equitably across protected characteristics such as race, gender, age, or religion. Multiple formal fairness definitions exist — demographic parity, equalized odds, individual fairness — and they are often mathematically incompatible with each other and with accuracy maximization. Practitioners must select a fairness criterion appropriate to the deployment context, then measure and mitigate disparities throughout the model lifecycle.