{"id":26145,"date":"2021-05-04T06:00:00","date_gmt":"2021-05-04T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=26145"},"modified":"2021-05-05T10:40:10","modified_gmt":"2021-05-05T17:40:10","slug":"challenges-of-predictive-analytics-for-law-enforcement","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2021\/05\/04\/challenges-of-predictive-analytics-for-law-enforcement\/","title":{"rendered":"Challenges of Predictive Analytics for Law Enforcement"},"content":{"rendered":"\n

The idea of predictive analytics in policing is certainly nothing new to law enforcement or its critics. Once a simple thought exercise (and the trope behind more than a few science fiction stories), we have come to a point in our technological evolution where certain tools utilized by agencies resemble, to some degree, those seen in stories like The Minority Report<\/em><\/a>. <\/em>Today, these powerful new capabilities draw a substantial amount of real-world controversy, which only grows as the technology fueling the debate more sophisticated.<\/p>\n\n\n\n

Fraught with racial, social, privacy and socioeconomic concern<\/a>, much of this controversy has centered on what happens when police use these tools to predict and prevent external crime. But there is a second, internal concern along the same lines presented to HR personnel and other hiring stakeholders within law enforcement agencies. Can the same types of tools legitimately help predict malicious or excessive use of force? Can they be used to root out other types of misbehavior within the ranks?<\/p>\n\n\n\n

So far, the answer to both questions appears to be yes<\/em>, but it\u2019s a highly qualified yes<\/em>. Like so many other technological tools \u201crevolutionizing\u201d the law enforcement world, treating these sensitive tools as the paramount authority can lead rank-and-file personnel to negative perceptions that damage the solution\u2019s actual value. <\/p>\n\n\n\n

Defining Predictive Analytics in the Law Enforcement Context<\/strong><\/p>\n\n\n\n

Let\u2019s say a law enforcement supervisor receives an email containing insights from an automated platform that a high-level HR manager just implemented. This document\u2014full of data on a third-year recruit\u2014suggests behavior patterns that have historically led to bigger misbehavior in other officers\u2019 careers: information that could introduce the department to liability or, worse, result in someone being harmed or killed. For example, the data points in the analysis may include a history of substance abuse on the recruit\u2019s part or a disproportionately high number of complaints that, while all dismissed, could be combined to tell a worrying story.<\/p>\n\n\n\n

This, at a high level, is what a predictive solution may look like in the average law enforcement workplace. There is little doubt a tool with such capacity has value for departments and the public they serve. As one FiveThirtyEight<\/em> piece on the matter<\/a> says, and as readers undoubtedly know, an algorithm can analyze data at a rate far faster than humans, and its lack of human biases allows it to make connections in data that whole teams of humans may overlook\u2014connections that are not intuitive but still seem to predict the outcome the solution is engineered to search for.<\/p>\n\n\n\n

The Chicago Police Department\u2019s Early Intervention Program, or EIP<\/a>, is perhaps the most notable culmination of these ideas so far. In this instance, the predictive platform beneath the policy runs largely by searching, at large scale, for connections determined by the University of Chicago data scientists who designed the solution. The EIP system was set up as the result of the murder of Laquan McDonald<\/a> at the hands of a Chicago police officer, to preemptively identify officers who may, by the data\u2019s prediction, be even more likely for incidents of misbehavior in the future. These factors, explained in detail on a Chicago Police Department webpage<\/a>, include tardiness and complaint thresholds (both sustained and non-sustained) during a defined time period.<\/p>\n\n\n\n

The Problems of Predictive Analytics<\/strong><\/p>\n\n\n\n

The concerns surrounding predictive tools can be distilled into two categories: <\/p>\n\n\n\n