Tag: algorithmic-fairness

Personalization fairness asks whether different users receive meaningfully different opportunities, prices, encouragement, or difficulty—and whether those differences are justified.

The COMPAS controversy showed that an algorithm can satisfy one statistical definition of fairness while violating another, making governance choices about error costs as important as the model's predictive accuracy.