SignificanceSocial science research and policy increasingly rely on predictive risk assessment instruments (RAIs), including those using machine-learning algorithms. This paper shows that the relationships between risk factors and future arrest are unstable over time when measured across sequential birth cohorts. As a result, prediction models that rely on risk factors are prone to systematic and substantial error. Such cohort bias, arising from the dynamics of social change, requires algorithmic updating and accounting for social factors affecting entire cohorts. Cohort bias can generate inequality in criminal justice contacts distinct from racial bias and has implications not only for the tailoring of RAIs but also for efforts aiming to provide preventative interventions to high-risk groups targeted based on individual-level risk factors alone.