This paper investigates the inter-rater reliability of risk assessment instruments (RAIs). The main question is whether different, socially salient groups are affected differently by a lack of inter-rater reliability of RAIs, that is, whether mistakes with respect to different groups affects them differently. The question is investigated with a simulation study of the COMPAS dataset. A controlled degree of noise is injected into the input data of a predictive model; the noise can be interpreted as a synthetic rater that makes mistakes. The main finding is that there are systematic differences in output reliability between groups in the COMPAS dataset. The sign of the difference depends on the kind of inter-rater statistic that is used (Cohen's Kappa, Byrt's PABAK, ICC), and in particular whether or not a correction of predictions prevalences of the groups is used.

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