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Big Data and Policing
When it comes to police and the public, technology doesn't always [thefreethoughtproject.com] have to mean Big Brother. In part of an interview on NPR's All Tech Considered [npr.org], Rayid Ghani of University of Chicago's Center for Data Science and Public Policy [uchicago.edu] described using data analytics to head off police misconduct.
From the interview:
The idea is to take the data from these police departments and help them predict which officers are at risk [uchicago.edu] of these adverse incidents. Instead of the systems today -- where you wait until something bad happens, and again, the only intervention at that point you have is punitive -- we're focusing on, "Can I detect these things early? And if I can detect them early, can I direct intervention to them -- training, counseling." ...
Stress is a big indicator there. For example, if you are an officer and you have been ... responding to a lot of domestic abuse cases or suicide cases, that is a big predictor of you being involved in an adverse incident in the near future.
He described one of the significant problems in this kind of data analysis, bias:
We often work with historical data, which means if the data was collected under some sort of a biased process -- so if people are giving loans and they're biased in who they give loans to. Or if people have been collecting data on police misconduct ... and if it was really hard to complain about police misconduct, then you're not going to have the right level of data -- you'll only have data from people who really, really, really wanted to come and complain.
If you use those to build your algorithms, what happens is that the computer finds more of only those types of things. So your future predictions will be extremely biased. So we spend a lot of time thinking about, how do we detect that bias, and then how do we correct for that bias so we don't make the wrong decisions?
On the role of data analytics:
What we believe is the role of data analytics is to help do a lot of early warning systems, to help do a lot of preventative things, to help allocate resources more effectively, and to sort of help improve policy in a much more evidence-based way than we've been doing before.
