Reasonably Suspicious Algorithms: Predictive Policing at the United States Border

Lindsey Barrett

As big data's promises of increased efficiency and serendipitous insights spread across a broad range of sectors, they are accompanied by new risks- some intuitive, some unpredictable. That dichotomy is heavily accentuated in the law enforcement context, where blithe application of new technologies to analogue doctrines poses a greater threat to individual rights. The potential for data analytics to add efficiency, accuracy, and accountability to existing procedures could be all the more beneficial. Predictive policing algorithms, which approximate the probability of crimes occurring in certain areas, or being committed by certain people, epitomize this dual dynamic. These algorithms have the potential to increase accuracy and efficiency, but they also threaten to dilute the reasonable suspicion standard and increase unintentional discrimination in a way that existing law is ill-equipped to prevent. This threat is of particular concern at the United States border. At the border, Fourth Amendment protections are generally weaker than in the interior due to the long-recognized governmental prerogative to investigate external threats poised to infiltrate the country. This article will argue that the use of predictive policing algorithms at the border should not be barred outright, as the government should permit potentially beneficial uses of the technology to develop. However, use of these algorithms should be carefully limited by statute to prevent the wholesale trammeling of privacy and civil liberties.

Reasonably Suspicious Algorithms: Predictive Policing at the United States Border

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Reasonably Suspicious Algorithms: Predictive Policing at the United States Border

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