Intersectional Fairness via Mixed-Integer Optimization

Name: Intersectional Fairness via Mixed-Integer Optimization
Uploaded: 2026-10-07T21:42:07.455212+00:00
Duration: 258 s
Description: The paper proposes a Mixed-Integer Optimization (MIO) framework to train classifiers that are fair across all combinations of protected groups (intersectional fairness). It proves that two measures of unfairness, MSD and SPSF, identify the same most unfair subgroups. The MIO approach uses 'lazy constraints' to efficiently handle the vast number of potential subgroups during training.

The paper proposes a Mixed-Integer Optimization (MIO) framework to train classifiers that are fair across all combinations of protected groups (intersectional fairness). It proves that two measures of unfairness, MSD and SPSF, identify the same most unfair subgroups. The MIO approach uses 'lazy constraints' to efficiently handle the vast number of potential subgroups during training.

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