Bias Detection in ML Models
Protected attributes, disparate impact, fairlearn metrics, equalized odds, demographic parity.
What is Algorithmic Bias
A model is biased when its predictions systematically disadvantage a group defined by a sensitive attribute (gender, race, age). Bias often comes from skewed training data and can cause real harm in hiring, lending, or healthcare decisions.
Sensitive Features
A sensitive feature is the attribute across which we measure fairness, such as gender or ethnicity. Fairness metrics compare model behavior across the groups this feature defines, even if the feature is not used as a model input.