Phillip K. Dick foretold it
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Prof Ishanu Chattopadhyay leads the ZeD Lab at the University of Chicago, where he studies algorithms and data. He tells us about the AI he has developed that can forecast crimes being committed days before they actually occur.
Your algorithm successfully predicted crime in US cities a week before they happened. How did you build the algorithm?
The city of Chicago and the seven other cities that we looked at have started putting out crime event logs in the public domain. In Chicago, these are actually updated daily with a week’s delay.
These event logs contain information about what happened, what type of crime it was, where it happened, the latitude, longitude, and a timestamp. In Chicago, we also have information about if there were any arrests made when there were interactions with the police officers.
So we start with this event log and then digitise the city into small areas of about two blocks by two blocks – about 1,000 feet [300 metres] across. And in one of those tiles, we’ll see this time series of these different events, like violent crimes, property crimes, homicides and so on. This results in tens of thousands of time series that are coevolving.
What our algorithm does is look at these coevolving time series, then figures out how they are dependent on one another and how they’re constraining one another – so how they’re shaping one another. That brings up a really complex model.
You can then make predictions on what’s going to happen, say, a week in advance at a particular tile, plus or minus one day. In Chicago, for example, today is Wednesday. Using our algorithm, you can say that next Wednesday, on the intersection of 37th Street and Southwestern Avenue, there would be homicide.
How do you envisage the ways your algorithm could be used?
People have concerns that this will be used as a tool to put people in jail before they commit crimes. That’s not going to happen, as it doesn’t have any capability to do that. It just predicts an event at a particular location. It doesn’t tell you who is going to commit the event or the exact dynamics or mechanics of the events. It cannot be used in the same way as in the film Minority Report.
In Chicago, most of the people losing their lives in violent crimes is largely due to gang violence. It is not like a Sherlock Holmes movie where some convoluted murder is happening. It is actually very actionable if you know about it a week in advance – you can intervene. This does not just involve stepping up enforcement and sending police officers there, there are other ways of intervening socially so that the odds of the crime occurring actually goes down and, ideally, it never happens.
". It cannot be used in the same way as in the film Minority Report."
...yet.
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Prof Ishanu Chattopadhyay leads the ZeD Lab at the University of Chicago, where he studies algorithms and data. He tells us about the AI he has developed that can forecast crimes being committed days before they actually occur.
Your algorithm successfully predicted crime in US cities a week before they happened. How did you build the algorithm?
The city of Chicago and the seven other cities that we looked at have started putting out crime event logs in the public domain. In Chicago, these are actually updated daily with a week’s delay.
These event logs contain information about what happened, what type of crime it was, where it happened, the latitude, longitude, and a timestamp. In Chicago, we also have information about if there were any arrests made when there were interactions with the police officers.
So we start with this event log and then digitise the city into small areas of about two blocks by two blocks – about 1,000 feet [300 metres] across. And in one of those tiles, we’ll see this time series of these different events, like violent crimes, property crimes, homicides and so on. This results in tens of thousands of time series that are coevolving.
What our algorithm does is look at these coevolving time series, then figures out how they are dependent on one another and how they’re constraining one another – so how they’re shaping one another. That brings up a really complex model.
You can then make predictions on what’s going to happen, say, a week in advance at a particular tile, plus or minus one day. In Chicago, for example, today is Wednesday. Using our algorithm, you can say that next Wednesday, on the intersection of 37th Street and Southwestern Avenue, there would be homicide.
How do you envisage the ways your algorithm could be used?
People have concerns that this will be used as a tool to put people in jail before they commit crimes. That’s not going to happen, as it doesn’t have any capability to do that. It just predicts an event at a particular location. It doesn’t tell you who is going to commit the event or the exact dynamics or mechanics of the events. It cannot be used in the same way as in the film Minority Report.
In Chicago, most of the people losing their lives in violent crimes is largely due to gang violence. It is not like a Sherlock Holmes movie where some convoluted murder is happening. It is actually very actionable if you know about it a week in advance – you can intervene. This does not just involve stepping up enforcement and sending police officers there, there are other ways of intervening socially so that the odds of the crime occurring actually goes down and, ideally, it never happens.
". It cannot be used in the same way as in the film Minority Report."
...yet.
@George-K said in Phillip K. Dick foretold it:
Using our algorithm, you can say that next Wednesday, on the intersection of 37th Street and Southwestern Avenue, there would be homicide.
Somebody who understands this stuff better than I would have to comment, but:
Say there's a perpetrator, Joe, who commits liquor store robberies. He's never caught in the act. His modus operandi includes leaving a dandelion at the scene (because Joe is a little weird). So there's a new liquor store robbery and the police find a dandelion. Within minutes they track down Joe, who doesn't have a useful alibi. They arrest him -- success! Only it turns out that an enemy of Joe's set him up, doing the heist and leaving the dandelion. Joe goes to prison and the copycat goes free.
This model didn't work so well for Joe, did it?
