Crimefighting AI ‘sidekick’ could team up with police to crack down on suspects

You've heard of Starsky & Hutch, but how about Starsky & Digital Police Officer (D-PO)?

While it doesn't have anywhere near as much of a ring to it, this could be the buddy cop duo of the future thanks to advances in artificial intelligence.

Scientists at the Pacific Northwest National Laboratory have proposed creating an AI-powered crimefighting sidekick that can aid police officers in their interpretation of the law.

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The D-PO would act as an onboard AI to give police advice in interpreting the law, using everything from facial recognition to live camera feeds to tell them what to do next.

It could help police think about their actions by giving them situation reports and suspect profiles through an audio feed., and recommend the best course of action.

D-PO would even take over driving duties for officers so they could examine drone footage of a suspect.

This is the vision that researchers at the laboratory have for 'machine teammates', and they argue that while this technology is still "a long way off", it could one day serve as a model for the integration of artificial intelligence and policing.

They said: "Good machine teammates are proactive. They take initiative to accomplish tasks and direct their human teammates' attention to new developments when necessary.

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"Designing technology to support tasks without explicit guidance is another focus of human-machine teaming research."

It's unclear exactly how an AI-powered buddy cop would ever be implemented in reality, given the strict rules around facial recognition data as well as issues around racial discrimination bias in algorithms that have previously been used by the police and courts.

Earlier this year, researchers at the University of Chicago revealed a new AI algorithm which they claimed could predict crime in advance with a 'high degree' of accuracy.

Ishanu Chattopadhyay, a scientist involved in the tool, said: "Rather than simply increasing the power of states by predicting the when and where of anticipated crime, our tools allow us to audit them for enforcement biases, and garner deep insight into the nature of the (intertwined) processes through which policing and crime co-evolve in urban spaces."


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