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Empirical evidence suggests that algorithmic decisions driven by MachineLearning (ML) techniques threaten to discriminate against legally protectedgroups or create new sources of unfairness. This work supports the contextualapproach to fairness in EU non-discrimination legal framework and aims atassessing up to what point we can assure legal fairness through fairnessmetrics and under fairness constraints. For that, we analyze the legal notionof non-discrimination and differential treatment with the fairness definitionDemographic Parity (DP) through Conditional Demographic Disparity (CDD). Wetrain and compare different classifiers with fairness constraints to assesswhether it is possible to reduce bias in the prediction while enabling thecontextual approach to judicial interpretation practiced under EUnon-discrimination laws. Our experimental results on three scenarios show thatthe in-processing bias mitigation algorithm leads to different performances ineach of them. Our experiments and analysis suggest that AI-assisteddecision-making can be fair from a legal perspective depending on the case athand and the legal justification. These preliminary results encourage futurework which will involve further case studies, metrics, and fairness notions.

Compatibility of Fairness Metrics with EU Non-Discrimination Laws: Demographic Parity & Conditional Demographic Disparity