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.

翻译：经验证据表明，基于机器学习（ML）技术的算法决策可能对法律保护的群体产生歧视，或衍生新的不公源头。本研究支持欧盟非歧视法律框架下的情境化公平性路径，旨在评估在公平性约束下，我们能在多大程度上通过公平性度量保障法律公平。为此，我们结合条件性人口统计学差异（CDD）概念，分析了非歧视与差别待遇的法律内涵，以及人口统计学均等（DP）的公平性定义。通过训练并比较不同带有公平性约束的分类器，我们评估在遵从欧盟非歧视法律所要求的司法情境化解释路径时，是否可能降低预测偏差。在三个场景下的实验表明，处理过程中的偏差缓解算法在每个场景中表现各异。实验与分析显示，AI辅助决策在法律层面的公平性取决于具体案例及其法律正当性。这些初步结果将为后续包含更多案例研究、度量标准及公平性概念的进一步工作提供动力。