Despite widespread recognition of intersectionality in discrimination research, most algorithmic fairness evaluations remain limited to single-attribute group comparisons or individual-level prediction errors. This paper introduces a distributional approach to intersectional fairness that evaluates how machine learning models reshape the allocation of outcomes across intersecting social groups. We show that unconstrained models do not merely reproduce existing labour-market structures but systematically amplify intersectional disparities across occupations.
IAB-Discussion Paper 06/2026: Distributional Intersectional Fairness in AI-Supported Job Matching: Evidence on Amplification, Trade-Offs, and Residual Risk
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