As algorithms are increasingly enlisted to make critical determinations about human actors, the more frequently we see these algorithms appear in sensational headlines crying foul on discrimination. There is broad consensus among computer scientists working on this issue that such discrimination can be reduced by intentionally collecting and consciously using sensitive information about demographic features like sex, gender, race, religion etc. Companies implementing such algorithms might, however, be wary of allowing algorithms access to such data as they fear legal repercussions, as the promoted standard has been to omit protected attributes, otherwise dubbed “fairness through unawareness”. This paper asks whether such wariness is justified in light of EU data protection and anti-discrimination laws. In order to answer this question, we introduce a specific case and analyze how EU law might apply when an algorithm accesses sensitive information to make fairer predictions. We review whether such measures constitute discrimination, and for who, arriving at different conclusions based on how we define the harm of discrimination and the groups we compare. Finding that several legal claims could arise regarding the use of sensitive information, we ultimately conclude that the proffered fairness measures would be considered a positive (or affirmative) action under EU law. As such, the appropriate use of sensitive information in order to increase the fairness of an algorithm is a positive action, and not per se prohibited by EU law.

随着算法越来越多地被用来对人类行为者做出关键决定，我们越来越频繁地看到这些算法出现在耸人听闻的头条新闻中，谴责歧视。研究这个问题的计算机科学家们达成了广泛的共识，即可以通过有意收集和有意识地使用有关性、性别、种族、宗教等人口特征的敏感信息来减少这种歧视。然而，实施此类算法的公司可能会谨慎对待允许算法访问这些数据是因为它们担心法律后果，因为推广的标准是忽略受保护的属性，否则被称为“通过无意识实现公平”。本文询问，根据欧盟数据保护和反歧视法，这种谨慎态度是否合理。为了回答这个问题，我们介绍一个具体案例，并分析当算法访问敏感信息以做出更公平的预测时，欧盟法律如何适用。我们审查这些措施是否构成歧视，以及对谁构成歧视，并根据我们如何定义歧视的危害和我们比较的群体得出不同的结论。发现有关敏感信息的使用可能会出现一些法律索赔，我们最终得出结论，所提出的公平措施将被视为欧盟法律下的积极（或平权）行动。因此，适当使用敏感信息以提高算法的公平性是一种积极行动，本身并不受到欧盟法律的禁止。