The hidden social impact of crime-stopping AI [Infographic]
Learn more about the use of artificial intelligence in law enforcement from the infographic below.
What happens when the tech that is supposed to help us actually ends up making us worse off? This is the question many are grappling within the wake of a rise in artificial intelligence-driven tools being used to fight crime. With one of the lowest crime rates ever seen, many are asking why we need crime-fighting AI in the first place.
The idea of “broken windows policing” is that if police patrol “heavy crime” areas and arrest people for minor offenses it will ultimately decrease the number of crimes committed because those who are more likely to commit more serious crimes will already be off the streets. This is based on the idea that crime is like a virus that can spread through a community, infecting those who otherwise wouldn’t be involved with a crime at all.
This has been the basis for the over-policing of minority neighborhoods for decades, and it feeds into the implicit bias programmed within every crime-related artificial intelligence algorithm.
Crime-fighting AI is programmed with historical crime data and it then extrapolates based on that data where crime is most likely to happen so police can increase patrols in that area. Unfortunately, because the data is inherently biased, the response to it is also inherently biased, which leads to further divisions in trust between law enforcement and the community.
AI-driven predictive policing practices have already been implemented by law enforcement agencies around the world. ICE has access to the predictive policing databases of more than 300 localities in Northern California, and currently, more than 50 municipalities around the United States are using PredPol technology. At least six countries have also implemented the use of this technology.
Learn more about the use of artificial intelligence in law enforcement from the infographic below. Are we prepared to face the consequences?
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