A team of data and social scientists at the University of Chicago shared news recently claiming that they had developed an algorithm that can predict crime a week in advance. The announcement is the latest development in what has seemingly turned into law enforcement agencies’ white whale - using AI to prevent crime before it happens.
The chase to better predict and prevent crime through technology isn’t new. In fact, there exists a long list of failed AI programs that were scrapped, most of which due to the tech perpetuating racial bias.
Can the latest AI development deliver predictive policing without bias to help save lives?
The new algorithm
The scientists at the University of Chicago say that their algorithm learns patterns of the timing and locations of both violent and property crimes from public data. Learning those patterns enables the AI to “predict future crimes one week in advance with about 90% accuracy,” the team reports.
The team differentiates this AI from previous, controversial predictive policing tech because of how crime location data is analyzed. This model divides cities into equal blocks of 1,000 feet across to avoid bias attributed to neighborhood borders, while past tech relied on boundaries with existing stereotypes
The model was tested with data from 8 cities including Philadelphia, San Francisco, and Chicago. Interestingly, the scientists warn that the tech shouldn’t be used to direct law enforcement, but rather be a tool in the policing toolbox to address criminal activity.
How did past predictive policing AI fare? Two years ago, LAPD scrapped the predictive policing project Pred-Pol after civil liberties advocates argued that it perpetuated biased policing toward people of color.
Nearby Santa Cruz even banned predictive policing not long after. Another tactic for law enforcement agencies is to use facial recognition tech to catch criminals - but in 2018, a trial of the AI in London performed very poorly due to biased inputs.
Is it the tech’s fault?
Researchers argue that police data in the US has historically been biased because law enforcement disproportionately arrests individuals in low-income areas predominantly housing people of color. That suggests that predictive policing AI leverages a flawed dataset, with biased inputs creating biased outputs.
With that in mind, it’s unclear whether development of predictive policing AI is worth advancing until the data used to teach it is improved. In fact, this appears to be a flaw that the University of Chicago algorithm doesn’t solve.
Biased inputs create one problem and automated tech creates another. Perhaps the historic failures in predictive policing stem from the belief that a perfect AI can exist - or that the tech that we’re capable of developing now are a panacea for crime prevention.
Writers at JSTOR present a symptom of the problem: there’s a gap of clarity and accountability when tech fails. The tools developed to support law enforcement can either be developed in-house or by third-party builders, and especially in the case of contracted work, accountability is hard to come by.
The power of humans + tech
In reality, all AI ought to have some level of human oversight. It’s a belief we preach at Invisible, where we use both humans and machines to get the most out of each other (we call it worksharing).
We combine a human workforce and automation to carry out business processes for our clients. Here’s an example of how we used that combo to help a company find rare leads.
Interested in how we can leverage both humans and technology to help you meet business goals? Get a custom demo.
Tune in next week for more tech fails.