A tutorial and comparative review showing that fairness-aware ranking should consider multiple protected attributes together, and that intersectional correction need not destroy ranking utility.
Towards intersectionality in machine learning: Including more identities, handling underrepresentation , and performing evaluation
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A Tutorial On Intersectionality in Fair Rankings
A tutorial and comparative review showing that fairness-aware ranking should consider multiple protected attributes together, and that intersectional correction need not destroy ranking utility.