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Many important decisions in societies such as school admissions, hiring, orelections are based on the selection of top-ranking individuals from a largerpool of candidates. This process is often subject to biases, which typicallymanifest as an under-representation of certain groups among the selected oraccepted individuals. The most common approach to this issue is debiasing, forexample via the introduction of quotas that ensure proportional representationof groups with respect to a certain, often binary attribute. Cases includequotas for women on corporate boards or ethnic quotas in elections. This,however, has the potential to induce changes in representation with respect toother attributes. For the case of two correlated binary attributes we show thatquota-based debiasing based on a single attribute can worsen the representationof already underrepresented groups and decrease overall fairness of selection.We use several data sets from a broad range of domains from recidivism riskassessments to scientific citations to assess this effect in real-worldsettings. Our results demonstrate the importance of including all relevantattributes in debiasing procedures and that more efforts need to be put intoeliminating the root causes of inequalities as purely numerical solutions suchas quota-based debiasing might lead to unintended consequences.

Quota-based debiasing can decrease representation of already underrepresented groups