At the conceptual intersection of machine learning and government data collection lie Automated Suspicion Algorithms, or ASAs, which are created by applying machine learning methods to collections of government data with the purpose of identifying individuals likely to be engaged in criminal activity. The novel promise of ASAs is that they can identify data‐supported correlations between innocent conduct and criminal activity and help police prevent crime. ASAs present a novel doctrinal challenge as well, as they intrude on a step of the Fourth Amendment’s individualized suspicion analysis, previously the sole province of human actors: the determination of when reasonable suspicion or probable cause can be inferred from established facts. This Article analyzes ASAs under existing Fourth Amendment doctrine for the benefit of courts that will soon be asked to deal with ASAs. In the process, this Article reveals the inadequacies of existing doctrine for handling these new technologies and proposes extrajudicial means for ensuring that ASAs are accurate and effective.