Big Think: Predictive policing: Data can be used to prevent crime, but is that data racially tinged? by Eric Siegel:
As predictive analytics advances decision making across the public and private sectors, nowhere could this prove more important – nor more risky – than in law enforcement. If the rule of law is the cornerstone of society, getting it right is literally foundational. But the art of policing by data, without perpetuating or even magnifying the human biases captured within the data, turns out to be a very tricky art indeed.
Predictive policing introduces a scientific element to law enforcement decisions, such as whether to investigate or detain, how long to sentence, and whether to parole. In making such decisions, judges and officers take into consideration the calculated probability a suspect or defendant will be convicted for a crime in the future. Calculating predictive probabilities from data is the job of predictive modeling (aka machine learning) software. It automatically establishes patterns by combing historical conviction records, and in turn these patterns – together a predictive model – serve to calculate the probability for an individual whose future is as-yet unknown. Such predictive models base their calculations on the defendant’s demographic and behavioral factors. These factors may include prior convictions, income level, employment status, family background, neighborhood, education level, and the behavior of family and friends.