The Fourth Amendment requires “reasonable suspicion” to stop a suspect. As a general matter, police officers develop this suspicion based on information they know or activities they observe. Suspicion is individualized to a particular person at a particular place. Most reasonable suspicion cases involve police confront-ing unknown suspects engaged in observable suspicious activities. Essentially, the reasonable suspicion doctrine is based on “small data”—discrete facts, limited information, and little knowledge about the suspect.
But what happens if this small data suspicion is replaced by “big data” suspicion? What if police can “know” personal information about the suspect by searching vast networked information sources? The rise of big data technologies offers a challenge to the traditional paradigm of Fourth Amendment law. With little effort, officers can now identify most unknown suspects, not through their observations, but by accessing a web of information containing extensive personal data about suspects. New data sources, including law enforcement databases, third-party records, and predictive analytics, combined with biometric or facial recognition software, allow officers access to information with just a few search queries. At some point, inferences from this personal data (independent of the observation) may become sufficiently individualized and predictive to justify the seizure of a suspect. The question this Article poses is whether a Fourth Amendment stop can be predicated on the aggregation of specific and individualized, but otherwise noncriminal, factors.
For example, suppose police are investigating a series of robberies in a particular neighborhood. Arrest photos from a computerized database are uploaded in patrol cars. Facial recognition software scans people on the street. Suddenly there is a match—police recognize a known robber in the targeted neighborhood. The suspect’s personal information scrolls across the patrol car’s computer screen—prior robbery arrests, prior robbery convictions, and a list of criminal associates also involved in robberies. The officer then searches additional sources of third-party data, including the suspect’s GPS location information for the last six hours or license plate records which tie the suspect to pawn shop trades close in time to prior robberies. The police now have particularized, individualized suspicion about a man who is not doing anything overtly criminal. Or perhaps predictive software has already identified the man as a potential reoffender for this particular type of crime. Or perhaps software has flagged the suspect’s social media comments or other Internet postings that suggest planned criminal or gang activity. Can this aggregation of individualized information be sufficient to justify interfering with a person’s constitutional liberty?
This Article traces the consequences of a shift from “small data” reasonable suspicion, focused on specific, observable actions of unknown suspects, to a “big data” reality of an interconnected, information rich world of known suspects. With more specific information, police officers on the streets may have a stronger predictive sense about the likelihood that they are observing criminal activity. This evolution, however, only hints at the promise of big data policing. The next phase will use existing predictive analytics to target suspects without any firsthand observation of criminal activity, relying instead on the accumulation of various data points. Unknown suspects will become known to police because of the data left behind. Software will use pattern-matching techniques to identify individuals by sorting through information about millions of people contained in networked data-bases. This new reality simultaneously undermines the protection that reasonable suspicion provides against police stops and potentially transforms reasonable suspicion into a means of justifying those same stops.
This Article seeks to offer three contributions to the development of Fourth Amendment theory. First, it demonstrates that reasonable suspicion—as a small data doctrine—may become practically irrelevant in an era of big data policing. Second, it examines the distortions of big data on police observation, investigation, and prediction, concluding that big data information will impact all major aspects of traditional policing. Third, it seeks to offer a solution to potential problems using the insights and value of big data itself to strengthen the existing reasonable suspicion standard.