Compatibility of Fairness Metrics with EU Non-Discrimination Laws:
  Demographic Parity & Conditional Demographic Disparity
Lisa Koutsoviti Koumeri Magali Legast Yasaman Yousefi Koen Vanhoof Axel Legay Christoph Schommer
Empirical evidence suggests that algorithmic decisions driven by Machine
Learning (ML) techniques threaten to discriminate against legally protected
groups or create new sources of unfairness. This work supports the contextual
approach to fairness in EU non-discrimination legal framework and aims at
assessing up to what point we can assure legal fairness through fairness
metrics and under fairness constraints. For that, we analyze the legal notion
of non-discrimination and differential treatment with the fairness definition
Demographic Parity (DP) through Conditional Demographic Disparity (CDD). We
train and compare different classifiers with fairness constraints to assess
whether it is possible to reduce bias in the prediction while enabling the
contextual approach to judicial interpretation practiced under EU
non-discrimination laws. Our experimental results on three scenarios show that
the in-processing bias mitigation algorithm leads to different performances in
each of them. Our experiments and analysis suggest that AI-assisted
decision-making can be fair from a legal perspective depending on the case at
hand and the legal justification. These preliminary results encourage future
work which will involve further case studies, metrics, and fairness notions.