Machine Learning, Automated Suspicion Algorithms, and the Fourth Amendment

Abstract

At the conceptual intersection of machine learning and government data collection lie Automated Suspicion Algorithms, or ASAs, algorithms created through the application of 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 who will soon be asked to deal with ASAs. In the process, the Article reveals how that doctrine is inadequate to the task of handling these new technologies and proposes extra-judicial means of ensuring that ASAs are accurate and effective.

Keywords: machine learning, criminal procedure, fourth amendment, big data, automation, cyberlaw, computers, individualized suspicion