A classifier decision is fair if it has a fair explanation (prime implicant without protected features, respecting constraints); the paper relates three such fairness notions for classifiers and studies the complexity of testing them.
Model interpretability through the lens of computational complexity
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Fairness of Classifiers in the Presence of Constraints between Features
A classifier decision is fair if it has a fair explanation (prime implicant without protected features, respecting constraints); the paper relates three such fairness notions for classifiers and studies the complexity of testing them.