Algorithmic Bias in Criminal Risk Assessment: The Consequences of Racial Differences in Arrest as a Measure of Crime

There is great concern about algorithmic racial bias in the risk assessment instruments (RAIs) used in the criminal legal system. When testing for algorithmic bias, most research effectively uses arrest data as an unbiased measure of criminal offending, which collides with longstanding concerns that arrest is a biased proxy of offending. Given the centrality of arrest data in RAIs, racial differences in how arrest proxies offending may be a key pathway through which RAIs become biased. In this review, we evaluate the extensive body of research on racial differences in arrest as a measure of crime. Furthermore, we detail several ways that racial bias in arrest records could create algorithmic bias, although little research has attempted to measure the degree of algorithmic bias generated by using racially biased arrest records. We provide a roadmap to assist future research in understanding the impact of biased arrest records on RAIs.