Abstract
Probable cause determinations are problematic. Like all court decisions using totality-of-the-circumstances tests, it is difficult to use one decision - or even a few - to foresee a subsequent outcome. No human is capable of reading all the relevant Fourth Amendment opinions relevant to resolving any search and seizure issue. Machines may be capable of this task and to do so they will need to be able to identify particular types of suspicious factors from the various ways courts describe the factors. This project examines the ability of three machine learning models to examine the relevant text of opinions to identify the suspicious factors courts used to determine whether adequate suspicion existed from an intrusion protected by the Fourth Amendment.