How machine learning could reduce police incidents of excessive force
Jun 17, 2020, 3:43 PM
When incidents of police brutality occur, typically departments enact police reforms and fire bad cops, but machine learning could potentially predict when a police officer may go over the line.
Rayid Ghani is a professor at Carnegie Mellon and joined Seattle’s Morning News to discuss using machine learning in police reform. He’s working on tech that could predict not only which cops might not be suited to be cops, but which cops might be best for a particular call.
“AI and technology and machine learning, and all these buzzwords, they’re not able to to fix racism or bad policing, they are a small but important tool that we can use to help,” Ghani said. “I was looking at the systems called ‘early intervention systems’ that a lot of large police departments have. They’re supposed to raise alerts, raise flags when a police officer is at risk of doing something that they shouldn’t be doing, like excessive use of force.”
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“What we found when looking at data from several police departments is that these existing systems were mostly ineffective,” he added. “If they’ve done three things in the last three months that raised the flag, well that’s great. But at the same time, it’s not an early intervention. It’s a late intervention.”
So they built a system that works to potentially identify high risk officers before an incident happens, but how exactly do you predict how somebody is going to behave?
“We build a predictive system that would identify high risk officers … We took everything we know about a police officer from their HR data, from their dispatch history, from who they arrested …, their internal affairs, the complaints that are coming against them, the investigations that have happened,” Ghani said.
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“What we found were some of the obvious predictors were what you think is their historical behavior. But some of the other non-obvious ones … were things like repeated dispatches to suicide attempts or repeated dispatches to domestic abuse cases, especially involving kids. Those types of dispatches put officers at high risk for the near future.”
While this might suggest that officers who regularly dealt with traumatic dispatches might be the ones who are higher risk, the data doesn’t explain why, it just identifies possibilities.
“It doesn’t necessarily allow us to figure out the why, it allows us to narrow down which officers are high risk,” Ghani said. “… Let’s say a call comes in to dispatch and the nearest officer is two minutes away, but is high risk of one of these types of incidents. The next nearest officer is maybe four minutes away and is not high risk. If this dispatch is not time critical for the two minutes extra it would take, could you dispatch the second officer?”
So if an officer has been sent to a multiple child abuse cases in a row, it makes more sense to assign somebody else the next time.
“That’s right,” Ghani said. “That’s what that we’re finding is they become high risk … It looks like it’s a stress indicator or a trauma indicator, and they might need a cool-off period, they might need counseling.”
“But in this case, the useful thing to think about also is that they haven’t done anything yet,” he added. “This is preventative, this is proactive. And so the intervention is not punitive. You don’t fire them. You give them the tools that they need.”
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