Discrimination by generalisation: about judicial use and abuse of statistics in anti-discrimination cases

In this chapter, Juliana de Augustinis and Rob van Gestel show that statistics can be used in anti-discrimination court cases to (i) rationally explain and legally justify differences in treatment between groups of individuals because they, for example, pose different risks; (ii) provide input for the proportionality assessments that courts have to perform when unequal treatment may infringe upon fundamental rights; and (iii) demonstrate the disparate effects of seemingly neutral measures relative to a group (indirect discrimination) and shift the burden of proof. The authors conduct a comparative analysis of a number of “typical” anti-discrimination cases in the fields of a) insurance law; b) public health law; and c) labour law. Their analysis reveals that courts often face difficulties with the use of statistics in deciding whether allowing insurance companies, healthcare agencies and employers to take into account certain group features results in statistical fairness or (indirect) discrimination. As a result, courts in different jurisdictions may sometimes come to diverging or opposite conclusions based on similar statistical data because of (implicit) differences in their methodology of judicial decision-making.