Who Forecasted It Better? The Battle Between Human Cops and Machine Learning to Predict Future Offending

Dr Geoffrey Barnes

 

Advanced statistical techniques are getting better with every passing month, and can be enormously accurate in predicting some kinds of future events.  While these efforts aren’t very common in the criminal justice system – at least not yet – they bring with them an enormous amount of controversy.  Are these statistical methods really all that accurate?  Are they ethical?  Do they mirror the same biases that already exist in the criminal justice system?  Should criminal justice systems be based upon what we think offenders will do in the future, or should we focus only on what they have done in the past?  Previous research has already examined the base levels of accuracy that machine learning can produce when forecasting future offending.  But until now, we haven’t been able to compare these results to the kinds of predictions that ordinary police officers have been making (often quite informally) for the last hundred years.  This paper will examine several different predictive analytics models from a variety of policing jurisdictions, and examine how these forecasts differ from the predictions made by human police officers for the exact same cases.  Who is better at identifying future criminal behaviour?  Man or machine?  This paper will attempt to find out.


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