Bloomberg profile of Richard Berk

Richard Berk is one of the founding fathers of automated risk assessment, and systems based on his work are being deployed in Pennsylvania and other locations. This Bloomberg profile of him has many interesting (and terrifying) nuggets. As always, you should read the whole thing (if Bloomberg’s horrible page rendering doesn’t trigger a headache), but here are some highlights.

What’s interesting in the system he designed is how it’s optimized for cost of incarceration, rather than for accuracy. In the particular case described in the article, this actually makes the system less harsh, because a finding of a problem triggers expensive therapy. On the other side though, there’s a political component: it’s far riskier to release someone who might commit a crime than it is to keep incarcerated someone who might be reformed. As Berk puts it:

The policy position that is taken is that it’s much more dangerous to release Darth Vader than it is to incarcerate Luke Skywalker

The problem of course is that incarcerating Luke Skywalker could turn him into a new Darth Vader, and I don’t know if this is factored into the analysis.

He also says later

Berk argues that eliminating sensitive factors weakens the predictive power of the algorithms. “If you want me to do a totally race-neutral forecast, you’ve got to tell me what variables you’re going to allow me to use, and nobody can, because everything is confounded with race and gender,” he said.

This seems a little binary to me. It’s not an either-or where you either have to keep all sensitive attributes or throw them all out. There are ways to quantify and even subtract out the influence of certain problematic attributes without having to throw out all the information: in fact, we have a paper on this!

As the article, Berk is heading to Norway:

Berk wants to predict at the moment of birth whether people will commit a crime by their 18th birthday, based on factors such as environment and the history of a new child’s parents. This would be almost impossible in the U.S., given that much of a person’s biographical information is spread out across many agencies and subject to many restrictions. He’s not sure if it’s possible in Norway, either, and he acknowledges he also hasn’t completely thought through how best to use such information.

The idea that data can be collected to make such predictions is certainly alluring and tempting. But everything we’re beginning to understand about predictions based on algorithms suggests that making such predictions in the absence of any understanding of the model behavior and why it’s making its decisions is a recipe for disaster.

I’ll note that the recidivism predictions typically work 6 months to 2 years out, and are not particularly accurate! Trying to predict 18 years out is rather scary.


2 thoughts on “Bloomberg profile of Richard Berk

  1. Obviously there are lots of thorny fairness issues when making predictions about recidivism. But I should say that Richard is quite aware of this, and thinks deeply about it. In fact, a group of us (including Richard and I, and also colleagues in the law school like Cary Coglianese, in economics like Mallesh Pai and Ricky Vohra, as well as in CS, including Sampath Kannan, Jamie Morgenstern, and Michael Kearns) are organizing a semester of inter-school discussions about issues surrounding algorithmic decision making, which should culminate in a workshop that we’re planning to invite you to once we get around to scheduling it. 🙂

    One point that Richard has made is that we live in a world in which the majority of crimes are committed against victims who are of the same race as the criminal. This means that for the most part, the costs of unfairness acrue to the same population as do the costs of crime. This complicates things, because it means that improving unfairness in sentencing at the cost of classification accuracy has a complicated and hard to reason about effect on some populations — its not clear what is the best point on the pareto curve.


    1. That’s excellent news that you have these efforts underway. We really need more of this, and the interdisciplinary nature of your effort is the right way to go.



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