Using prison data to reduce incarceration

In Episode 5 of Series 7 of The Rights Track, Todd is in conversation with Amrit Dhir, Director of Partnerships at Recidiviz – a team of technologists committed to getting decision makers the data they need to drive better criminal justice outcomes. 

Transcript

Todd Landman  0:00 

Welcome to the Rights Track podcast, which gets the hard facts about the human rights challenges facing us today. In series seven, we're discussing human rights in a digital world. I'm Todd Landman, in this episode, I'm delighted to be joined by Amrit Dhir. Amrit is the Director of Partnerships at Recidiviz, a team of technologists committed to getting decision makers the data they need to drive better criminal justice outcomes. He has previously spent over a decade at the intersection of technology and new business development, working, for example at Sidewalk Labs, Google for Startups and Verily. Today, we'll be exploring the practical uses of technology and data in the criminal justice system. So Amrit, it's great to have you on this episode of the Rights Track. Welcome from California.

Amrit Dhir  0:44 

Thank you so much, I'm really glad to be here.

Todd Landman  0:46 

It's great for you to join us. And I want to start with a simple question. We had a guest - Sam Gilbert - on our last episode, we made this distinction between the sort of data for good and data for bad and there's a very large sort of argument out there about surveillance capitalism, the misuses of data, you know, behavioural microtargeting and all these sorts of issues. And yet I see that where you're working at Recidiviz there's a kind of data for good argument here around using technology and data to help criminal justice systems and the healthcare sector. So just briefly, could you tell us about this data for good and data for bad distinction?

Amrit Dhir  1:19 

Yeah, well, as with most things, I think it's difficult to pigeonhole anything into one of those camps, everything it seems, can be used for good or bad. And so data itself is not one or the other. I think it's about the use, I think that's what Sam was getting at with you as well. With Recidiviz, you know, what we've understood is that data that's been collected over a long period of time, especially in the context of the United States, and our unfortunate kind of race to mass incarceration, from basically the 1970s until about mid-2010s. We've collected a lot of data along the way, and we're not actually using or understanding that data. And so what we do at Recidiviz is we bring that data together, so make it something that can be better understood and better utilised, to help reduce prison populations to help drive better outcomes. So we're focused on taking data that's been, again, collected over quite a long period of time and consistently collected, but also making it better understandable.

Todd Landman  2:17 

So this sounds like big, messy, disparate, fragmented data, is that correct?

Amrit Dhir  2:22 

Most of those things, most of the time. It's definitely fragmented most of the time, it's not always necessarily what we'd call big. Because, you know, coming from Google, I think of big in the terms of, you know, search query type volume. So in corrections, it's not necessarily that big, but it is certainly messy, and it is certainly fragmented.

Todd Landman  2:42 

You know we had a guest on Rights Track, some while back, David Fathi from the American Civil Liberties Union, he explained to us the struc