The TEN7 Podcast – Episode 116

 

Meeting the Moment Part 3: Confronting Racism and Bias in Criminal Justice Data

Summary

In the third of a special five-part series, Julia Dressel, with special guest Tarra Simmons, discusses how Recidiviz is confronting racism and bias in criminal justice data.

Guests

Julia Dressel, software engineer at Recidiviz

Tarra Simmons, Washington State legislator who spent time in prison

Highlights

From Julia Dressel:

  • The massive growth of the criminal justice system in the United States makes it particularly difficult to break the cycles that have disproportionately ensnared people of color.
  • The racism that has been ingrained in our criminal justice system is perpetuated when we use statistics from the past to predict outcomes in the future.
  • We need to build a new system that is not burdened by mistakes of the past, but rather uses new data to show what works and how we can shrink the prison population safely and equitably.

From Tarra Simmons:

  • Tarra was inspired to pursue a law degree by the lawyers she met with while in prison, but once she was out she had to overcome multiple barriers to achieve that dream.
  • Prisons in many cases perpetuate violence, inflicting trauma on people who have been abused themselves, so understanding their stories is important as we look for a better way forward.

Links

Transcript

IVAN STEGIC: Hey everyone! Welcome to the TEN7 Podcast and the third episode of a special 5 part series called “Meeting the Moment: Using Data to Reimagine Criminal Justice

I’m your host Ivan Stegic.

This series is a partnership with Recidiviz, a nonprofit organization that is using data-driven tools to help guide change in the criminal justice system.

Recidiviz experts are helping us explore a wide variety of angles related to criminal justice and our prison system. We’re also looking for signs of hope, that maybe our country is finally ready to meet this challenge and find a new path forward.

Our mission at TEN7 is to “Make Things That Matter,” so this series fits with our values and our desire to do our part to make the world a better place.

In the first episode of the series, we learned that mass incarceration is a uniquely American problem, particularly targeting people of color and women.

In our second episode, we examined how data might help states start to reverse our reliance on mass incarceration, if we can improve the ways that data is gathered, shared and interpreted. In this episode, we’re going to examine the issue of bias in data and technology, and what we can do to prevent that bias from perpetuating the inequality in our criminal justice system.

But first, I want to continue with the story of Tarra Simmons, a recently elected state legislator in Washington who spent time in prison and who has a unique perspective on the challenges that face people with criminal records. We are leading off each episode of this series with Tarra’s voice as a reminder that criminal justice is not about numbers, it’s about human lives and it’s about hope.

Here is Tarra Simmons, continuing her story and discussing how she first got the idea to become a lawyer while she was serving time in prison.

TARRA SIMMONS: When I met the lawyers when I was in prison they were coming in, they were actually mostly law students and they were coming in to help the women with their family law cases. So, when you go to prison oftentimes, you’re losing custody of your kids and they were helping the women kind of advocate for their parental rights and so I met them through that. They told me that I would be a good lawyer because I was catching on quickly and I was a good advocate and things like that, and I was helping other women in the prison. I asked them, Do you think I could because of my criminal record? They didn’t know for sure but they did give me the name of a law professor to call when I got out and so I wrote his name down and I was out for probably eight months before I contacted him, and I asked him what did he think about my chances, and I went through my criminal record with him and he said, All of your criminal record was tied to trauma and substance use disorder, and you’re addressing those and so I think you would have a good chance. So he became a friend and mentor of mine and then I met other friends and mentors along the way including people who had criminal history and had done time in prison who became lawyers and collectively they really helped me gain entrance into law school.

IVAN: And so practicing law and being a graduate of a law degree are kind of two different things, right? You have to first get that certificate that you can hang up on a wall, you have to graduate, but then you have to sit for the State Bar exam, right? My understanding is you graduated and then you weren’t allowed to sit for the State Bar exam.

TARRA: Yes, it was devastating. It completely blindsided me because of the fact that two years before I went through that process, my dear friend, Professor Shon Hopwood, at Georgetown University in DC, he had robbed five banks at gunpoint and served 12 years in federal prison.

IVAN: [laughing] What?

TARRA: Yeah. And the Washington State Bar Association had allowed him to become an attorney here in Washington State. So, for me I really thought I had done everything. I had been honest and open, and I had helped so many people and I had over 100 letters of recommendation from not only sitting judges to prosecutors to people in my recovery community who I’d helped to supervisors who had been supervising me over the course of my law school career in different internships and externships and I had so much to look forward to and I had done everything. I had kept up with the obligations to keep my nursing license. I had done all of these random drug tests for five years. I had done everything. So it was really blindsiding to me that they voted to not allow me to sit for the bar exam. It was really, really devastating.

IVAN: So you couldn’t even sit for the exam. Even if you had taken the exam, they could’ve denied you after that, but you couldn’t even have an opportunity to take the exam. My understanding is that you then went to the State Supreme Court in Washington to fight this.

TARRA: Yes, and the person who ended up representing me in the State Supreme Court was none other than Shon Hopwood [laughing] who had become a lawyer in Washington State after robbing five banks at gunpoint and serving 12 years in federal prison. So the irony of that alone [laughing] was kind of amusing, and of course he did amazing advocacy on my behalf.

Something happened that day that never happens. Usually it takes the Supreme Court four or five months at least to issue an opinion, and we left the courthouse that day on November 16, 2017, and we thought we might hear back in April and came home and within hours the Supreme Court issued a unanimous decision allowing me to take the bar exam. So it was pretty special.

IVAN: That’s really amazing. And it set some sort of precedent in the law for others as well. Right?

TARRA: Yes. I think now we finally have a Supreme Court case that others can use when they’re advocating for their right to become an attorney.

IVAN: What did you do that evening when you found out about that result?

TARRA: Oh my gosh. It was just hours and hours of talking with all of the people who had supported me. So many people supported me, and so really, I spent the rest of that night with my family at the dining room table just fielding phone calls from reporters and from friends and supporters, and just letting them know how much we appreciated their support.

IVAN: We’ll hear more from Tarra in our next episode, but before we begin to talk about the issues related to bias in technology, Tarra did offer some thoughts on the ways that our criminal justice system tends to draw on the pain of the past and perpetuate it for the future.

TARRA: I would say that there really is nobody that struggles with being patient more than I do, because I literally have thousands of family members reaching out to me and sharing their grief and their trauma and their sadness, because their loved one is incarcerated. Hearing those stories over and over again, the pain of those individuals, but also survivors of crime too who have a lot of untreated trauma and pain, and I absolutely care about their issues as well, and their healing. I just don’t see them as being tied together.

I think that the way forward is continuing to break down the victim-offender binary issue. I think that that one is the political challenge that we really need to lean into. I’m really grateful to be working with a lot of survivors of crime right now who are advocating for transformational justice and for a new way of holding people accountable for harm and recognizing that prisons are another form of violence.

Prisons are state sanctioned violence, violent for previously sexually abused women that have to go to prison and be ripped away from their kids and be strip searched and be dehumanized, and to give birth shackled to a bed, to the way we are treating people, not just women, but the men that are in prison and the stories that they share around their parents putting cigarette butts out on them, and that the pain of these individuals. They’ve already had so much trauma and then we put them in these dehumanizing conditions where they’re put in segregation and locked in a cell for 23 hours a day.

We absolutely have to change this, and I think as long as we continue to highlight these stories, as long as we continue to work around breaking down what is violence and recognizing the criminal justice system is actually not helping, and showing the research and the data about recidivism, and how hard it is to get out of the cycle, how hard it is to get a job, how hard it is to get a place to live, and how that in itself is creating more recidivism.

IVAN: Tarra’s story illustrates how trauma can be passed from generation to generation, and how our criminal justice system has, unfortunately, helped continue these cycles rather than break them.

If we acknowledge that our criminal justice system has roots in racism… and if all of our data about criminal justice reflects this historical bias… how do we develop new technologies and programs to create a different system going forward?

That’s the question that our next guest is hoping to answer.

Julia Dressel is a software engineer at Recidiviz, and she joins us now.

JULIA DRESSEL: So, my role at Recidiviz is a software engineer and I work on a number of teams at Recidiviz but the core of my work is on the infrastructure, or data analytics infrastructure that we’re building at Recidiviz.

IVAN: And how did you manage to get to Recidiviz? What was the way you got there?

JULIA: This story starts actually many years ago, back when I was an undergrad in college and I am one of the few software engineers who was also a gender studies major when I was back in college. And studying both gender studies and computer science simultaneously got me super interested in situations where there was technology that was reinforcing any type of systemic bias or discrimination that we see in our society. So, when I was in school I ended up doing some research on some of the algorithmic risk assessment tools that are used in the criminal justice system and this is where I first started learning about bias built into these tools, but also started learning more about the problem of mass incarceration as a whole, and then a couple years ago I pretty randomly came across Recidiviz and was so excited because this was an organization that was working exactly on a problem that I was really passionate about and also it was working in a way where me as a software engineer could really contribute to what they were getting done at Recidiviz.

IVAN: Why are you so passionate about it. Obviously there’s a left brain, right brain thing going on here with gender studies and computer science, but what makes you so passionate?

JULIA: It’s a huge, huge problem that we have particularly in the United States. We incarcerate, I think, around 25% of the world’s prisoners even though we only have around 4% of the world’s population, and it’s this massive, massive system that has grown so dramatically over the past couple decades and impacts particularly people of color in this country, but impacts millions and millions of peoples lives, not only the people that are in the system but anyone related to or in the community of anyone who has gotten trapped in the system. And so, I have found this pull to do anything that can to make the system smaller, make it more fair and get people out of the cycles that they’ve fallen into through their interactions in the criminal justice system.

IVAN: I’d like to get some definitions out there, so that we’re comfortable with talking about words and our listeners are able to understand these words as well. Let’s start out by describing the context. What’s the technical definition of bias in technology?

JULIA: Bias is in general, not even just specific to technology, bias in general is when there’s a disproportionate weight in favor or against a certain thing. So, when we’re thinking about bias in technology, what we’re usually talking about is discrimination against a group of people that is happening because of some certain tool, because of how the tool’s being used usually. And so it’s a fact that there are technological tools where the use of that tool is having a disproportionate negative impact on a certain group of people.

IVAN: And where do we see this typically manifested in technology?

JULIA: It actually comes up in a lot of different types of technologies when you’re thinking about credit scores, algorithmic use of predictive analytics, trying to determine which type of student is going to be successful if they’re admitted to a college. We’ve seen algorithmic tools used for college admissions. We see algorithmic tools used a lot in determining who qualifies for a bank loan. It usually manifests in technology that’s trying to make a prediction about a certain individual, and the way that prediction is being made is having a disproportionate negative impact on a group of people.

IVAN: So that’s absolutely something we should care about because we might actually be affected by that bias and not even know about it, and it might be unfair.

JULIA: Absolutely.

IVAN: So why is it said to be built-in? That’s one of the things that we kind of glean from the title and from the context of this episode is, we’re talking about the built-in biases of technology.

JULIA: It’s said to be built-in because no matter how the given tool is used, no matter the intent of the person using it or the system using it, it’s going to have this disparate impact. And disparate impact is defined as when there is disproportionate negative impact to a certain protected class of a group of people. And so, we say that the bias is built-in to a technology when the kinds of decisions that it’s making and the kind of decision criteria that has been coded into how the tool is making a prediction, or making a decision. Those kinds of decisions are always going to on average impact a group of people more negatively than others. And so, it depends on the technology we can get into certain technologies, but basically why it’s built-in is that no matter what the application is of a given tool it’s going to have a disproportionate impact.

IVAN: So, all these words we keep hearing, machine learning, artificial intelligence, predictive analytics. Let’s start with machine learning, or ML. What is that?

JULIA: An analogy that I really like to use to describe machine learning, because it’s one of those words that’s thrown around a lot is, let’s say that we are trying to teach a machine to identify a picture of an apple. So we want to be able to show a photo to a machine and say Is this an apple? And for the machine to say, Yep, that’s an apple or no that’s not an apple. And how you train a machine learning model is you always have to give it data to learn patterns from.

So, let’s say we’ve got thousands of different pictures of fruits and vegetables and we’ve labeled what each of them are. So, we’ve labeled either this is an apple or this is not an apple and the machine learns, Okay, this is generally what an apple looks like. It’s kind of round, red, maybe has a leaf on the top. And then you show this machine a new picture that it hasn’t seen before and you say Is this an apple? And because you’ve loaded all of this data to teach the machine the patterns of what an apple usually looks like, it can make a guess, educated guess based on the patterns that it has learned whether or not the thing you’re showing it is an apple. So, it’s basically how a machine learns patterns that exist in a given data set and then uses what it’s learned about those patterns to make predictions about a new thing that it hasn’t seen before.

IVAN: Is that the artificial intelligence part or is that something else?

JULIA: Artificial intelligence uses machine learning a lot. Artificial intelligence usually we define as when you’re asking a machine to do a human-like task, and so, looking at a picture and saying Is this an apple, you know, maybe there’s a person out there who needs to be able to do that, but a more human-like task would be say picking apples. So if you have a robot and you want a robot to be able to go out to a tree and pick an apple, it needs to know if something is a leaf or something is an apple, and so, artificial intelligence is when you load potentially a robot or computer with machine learning so that it knows, Okay, this is what an apple looks like, and then you tell it, Okay, go pick all the things that look like an apple.

IVAN: So these things are not mutually exclusive, they’re building on top of each other. Okay. And then there’s this other term, predicted analytics. What does that mean?

JULIA: Predictive analytics is when you are using trends of what have happened, usually historically, in order to predict what is likely to happen in the future. So, we can stay on this apple analogy if it’s helpful [laughing].

IVAN: Yes, let’s do it. [laughing]

JULIA: Yeah, so, let’s say we own this huge orchard and we’re trying to predict, we’re trying to figure out how many apples we’re going to be able to sell next year, so predictive analytics would be having accurate historical data on This is the number of apples we usually sell, given the number of trees we have and we have figured out what the trends are historically with this orchard, and we use those trends, maybe we incorporate weather trends, seasonal trends, etc. to try to model and predict, Okay, given what the weather’s supposed to be like in the next season this is how many apples we expect to produce in the next year. So, it’s using trends of historical information to predict what’s likely to happen in the future.

IVAN: And I noticed you used the word accurate data when you described the data in the past that you’re going to use to do predictive analytics, and I would guess that data about which crops were successful and what the data for the apples look like, should be pretty unbiased, right? Unless there’s someone who is technically or artificially leaning towards giving the red apples more prevalence in the data somehow over the green apples, right?

JULIA: Yeah, so that’s usually a situation where you’re probably hopefully not going to have a lot of bias in the data collection but you could. Say some person doesn’t like green apples at all, and so they’re going through and they’re counting all the apples and they are like, You know what, I’m not going to count these green apples. I don’t think green apples count as apples. So that could be how a personal bias in what counts as x versus y could influence the accuracy of the data that’s collected.

IVAN: Okay, I think I understood that and that’s why I wanted to bring that in because I was thinking about what you described as bias earlier and how that might relate to this apple analogy. So there’s this other phrase historical descriptive analytics. What is that?

JULIA: So, historical descriptive analytics, if we just take out historical for a second, descriptive analytics is describe what exists. So, can you count the number of apples that were produced in October of 2020. That would be one descriptive data point. So, there’s nothing fancy going on with descriptive statistics basically, you’re just counting, you are categorizing things and you are counting what is in each category. So you might get a little fancy with like, Oh, this is the historical trend that has happened or these are the different rates of green apples to red apples, or something like that. But descriptive statistics is what we just think about as basic math that describes what has happened and when we think about historical descriptive statistics or descriptive analytics it’s being able to have a dataset on something that has happened over a given amount of time and pull numbers, pull metrics out of that data set.

IVAN: Got it. Okay, so, now that we got the definitions all out of the way and we understand this wonderful apple analogy, let’s talk about bias being an issue in technology that’s used in the criminal justice system. You mentioned the stats and we’ve talked about it in different episodes. The fact that the U.S. has about 4% of the world’s population but about 25% of the incarcerated population. Tell me about the bias in the technology that’s used in the criminal justice system and why it’s important to understand it in the context of the U.S. criminal justice system.

JULIA: What’s really important before you start talking about any kind of bias with technology that’s used in the criminal justice system is an understanding of the very, very specific racial context of the United States criminal justice system. And there’s some pretty alarming stats that I can go through to kind of paint this broad picture, but basically a black adult is almost six times more likely to be incarcerated than a white adult in the United States, and Hispanic adults around three times more likely than non-Hispanic white adults to be incarcerated and another really alarming one is that the Black and Hispanic people in our country are around 29% of the U.S. population but they make up actually around 57% of the prison population in the United States.

IVAN: Wow, that is a huge disparity there.

JULIA: Huge. So, that’s really important because that shows that we’ve got this system that historically has not treated people of different races equally, right? So we have this system that has over policed people of color, predominantly black people, and then over sentenced and over incarcerated. When I say over, I mean disproportionately we are incarcerating black people and other people of color in our country. So that’s a really important context to think about when you are trying to build any tool that’s going to use historical data about the criminal justice system to make any prediction about what’s going to happen next.

If we have accurate historical data about who has come into the criminal justice system you could see those trends. You could look at those trends and say Okay, wow, we have these groups of people who have been consistently incarcerated at higher rates than other groups of people. It's actually an accurate picture of what the system is doing, which is over-incarcerating certain groups of people.

IVAN: So given this context tell me about how the bias is affecting it.

JULIA: I think, not to overuse this apple analogy, [laughing] but I feel there’s actually something here that could be helpful. So, back to machine learning. We’re trying to say Machine, please be able to identify an apple from an image. And that learned what an apple looks like based on this huge data set that said apple, not apple, apple, not apple, and so, let’s say that there’s tons of images in this data set that are tomatoes that were labeled as apples, and it doesn’t really matter why those got the label apple but let’s say those tons of tomatoes that have been labeled as apples, and so this algorithm has learned Okay, this thing that looks kind of like an apple, okay, they’re telling me it’s an apple, great. This is also an apple. So then we show a picture of a tomato to this machine learning algorithm and it’s going to say, Oh, you’ve been telling me for decades that this is an apple so here this is an apple. And where that analogy kind of goes is that when you use historical data about the criminal justice system to build any type of machine learning algorithm it learns what categories of people fall back in the system over and over again. It learns the patterns that we’re giving it that say, This is what our system looks like and it learns those categories of people that have been incarcerated at higher and higher rates.

When you ask a machine learning algorithm that was trained on historical United States criminal justice data, Do you think this person will commit another crime in the future? So, you’re asking it a question within the context of the criminal justice system, it’s going to say, Oh, yeh this person looks a lot like all of the other people that have been stuck in the system over and over again so I think, yes, this person will probably end up back in prison.

IVAN: When in actual fact that person may have been mislabeled or mischaracterized because the data is biased.

JULIA: It’s basically that we in this incarceration system have incarcerated certain categories of people more than others and so if you have somebody that falls to that category it’s going to say Yes this person is likely to fall back in the system. So that just creates this self-perpetuating, defeating disparity machine where we can’t mitigate any of the existing discrepancies that are in the system if we’re relying on machines that are using historical data to describe those discrepancies in order to make predictions about what’s going to happen.

IVAN: So how do we fix that? Is it even possible?

JULIA: Usually tools, like predictive tools that are used in the criminal justice system, are asking a question of risk. They’re usually risk assessment tools. So there is a person that’s either pre-trial and so the Judge is deciding whether or not to let them out of jail before their trial, and there’s an algorithmic tool that is saying, Oh this person is high risk of committing another crime before their trial, or high risk of not showing up to their trial.

In the root of that question we are using an algorithmic tool to predict somebody’s likelihood of risk based on their proximity to categories that have been historically criminalized. And then we’re punishing that person, not for the crime that they have committed or for their actual threat to public safety, but we’re punishing them or making a decision about them based on what we think they might do based on how much they look like other people who haven’t had a chance to even get out of prison and succeed in public life before.

IVAN: So we’re making predictions based on flawed data.

JULIA: Exactly.

IVAN: How do you then go about making a prediction that’s unbiased based on biased data?

JULIA: One key to addressing this is to shift the kind of questions that we’re trying to ask. So, recognizing that we shouldn’t be making decisions based on a prediction of what we think someone might do, and instead we should be reacting to somebody’s material circumstances or what their actual needs are in the moment. Pretrial is a really great example of this where instead of keeping somebody in jail because they don’t have housing and therefore at a risk of recidivism because that is a risk factor, not having housing. Instead of saying, Oh, we’re going to punish you for this fact that you have a need that happens to be a factor that makes you more likely to recidivate in the future, instead asking a question of, Okay, what is this person's need and how can we support that person so that those needs are getting met? So it’s looking at a mental health situation and saying, Okay, let’s get this person treatment and resources, instead of Let’s further punish this person for the fact that we have historically criminalized poor mental health.

That’s one direction to take it, to shift the questions that we’re asking of technology and not assume that we can get some type of perfect and unbiased prediction of the future, but instead shifting what we’re trying to do in those certain contexts.

IVAN: So, as an organization you have to be keenly aware of the bias that you are potentially introducing in your software. What’s your role when you work with the state to show them how you’re addressing this bias?

JULIA: The role and what we’re really building at Recidiviz is an ability to show states a mirror of what’s currently happening, and they’ve been super, super receptive to this, and we’ve been able to show them, Here are the racial disparities that are currently existing in your system, and until the work that we’re doing, it was really hard to have an accurate understanding historically how a trend has changed over time, or to be able to ask a question of, This month what is currently happening in my system, and where are the racial disparities that are happening in the system?

IVAN: So you’re building a system to start taking data and to monitor the data in real-time and to reflect that data back to the states. And then presumably you’re then able to have a better chance of predicting what is likely to happen if they make a policy change.

JULIA: Exactly. So, we’ve got this one project that we’re working on where we’re looking at policy proposals in a given state legislature system and using accurate historical descriptive analytics of how people have moved through that system historically. We are predicting what the projected impact is of passing that given policy and what we can do is determine how that policy will impact the racial disparities of a given state's correction system. So, as we start to make changes to the system that currently exists, we can say, This is going to have a positive impact on the racial disparities in the system, or This is a dangerous change to make because it’s actually going to deepen the racial disparities in that system.

IVAN: So that’s actually a risk here, right? The risk that you might be trying to fix things but that you might be introducing additional bias into the system. So, how do we avoid that?

JULIA: The first step in avoiding that is having a very accurate understanding of what has gone on and what’s currently going on, and so we need to be able to know if a change is having a negative impact, we need to be able to know immediately that that is having an undesired impact and we need to pivot or change what we’re doing. So this includes super intentional but also slow rollouts of some of the tools that we built, and so, there’s a good example. There’s a certain tool we’re building where we’re rolling it out district by district and we need to make sure that we’re getting it right before it expands any further.

IVAN: What does that timeframe look like? Are we talking about years to do a rollout or is it shorter than that? How quickly is the feedback cycle happening for you?

JULIA: That’s a great question. What’s great is that we have very effectively shortened the feedback loop of the whole criminal justice system, or in the systems in the states that we’re working with because we’re collecting data in real-time from the State and producing analytics on what is currently happening and how changes have impacted what is currently happening in that State. So it depends on the product, but some products have a slow rollout maybe over a couple months and then we can look back to those months and say, Okay, cool, this is having the positive impact that we wanted, let’s expand a little bit farther.

IVAN: What does a negative impact do to your rollout and how do you address those issues?

JULIA: We actually have implemented what we call backstop metrics which is we have metrics that we’re keeping track of and we say, Okay, this is what’s really important and if anything starts to go towards this danger zone we need to take a pause and evaluate why that’s happening, potentially, pull what we’re implementing and rethink it and re approach it later, and this is pretty rare for Silicon Valley, I think for the techsphere is there’s usually not a high desirability to take a pause in what you’re building there’s a lot of the classic Facebook phrase of Move fast and break things. And we’re working in a context that is really important and affects a lot of people's lives. So we have to be very intentional and very careful about everything that we build so that we are only having a positive impact and decarceral impact on the system.

IVAN: Do you think that we’re going to get to a point where we are able to shift the discussion and get good clean data and sort of eliminate the bias that we are experiencing right now in the future?

JULIA: There’s such a tendency to say, Okay, well how can we get to a place where we’ve got clean data? How can we get to a place where we’ve got data that doesn’t have a bias? And what you’re really asking is, How can we get to a criminal justice system that doesn’t exhibit drastic racial bias at every step of the way. Right? You’re not going to get an accurate picture of the criminal justice system that is “clean” or “free of bias” if you don’t have a system that is equitable, that is much smaller than what we have currently or that is treating people equally.

IVAN: So let’s solve that big problem and get rid of bias is what you’re saying, and we do that by changing the system fundamentally?

JULIA: Yeah, and where I feel like Recidiviz really comes into play there is, we’ve got this huge system of mass incarceration, it’s been built over decades, and there is this bipartisan support, thank God, to shift what that system looks like, to decarcerate, to make it smaller, and as we start to chip away at this massive, massive system, we need to make sure that every change that we make is going in the right direction.

And so, having an ability to say, Okay, we’re about to make this change. Do we think it’s going to be good? Yes? Projected impact. Positive. Great, and then a year later or a month later whatever kind of the feedback loop is on that particular change, being able to evaluate, Did that have an expected impact? Yes. Amazing. Let’s replicate that in other states or let’s make even a more drastic change in that direction, etc.

So, being able to monitor and have accurate awareness of what is currently going on, how big the system is, what’s happening in the system, and then being able to say, These changes are working, these changes are not, in driving our impact all in one direction is really what we’re trying to do.

IVAN: I kind of want to close by asking you what you’re hopeful about and why you are still working on this?

JULIA: It’s a huge problem. What makes me hopeful is that every single decarceral change has a very human impact, and when we started Recidiviz a couple years ago we kept saying, If we get one person out of prison that will be a success. And so, even impacting one person's life in a positive way is a success and everything on top of that is just even more and more success for this organization and for our personal impact we want to have on this problem. Recidiviz exists explicitly so that we can ensure that the future of our criminal justice system does not look like its past, and as we make changes to the system we need to have the assurance that we’re making things better at every single step and not worse, and so I’m hopeful and very proud to work at Recidiviz and I’m hopeful in the impact that we are having on the system as a whole.

IVAN: Our series will continue next week with more from Tarra Simmons, and we’ll also speak with Serena Chang, a product manager at Recidiviz.

Serena will help us consider how technology might help change the culture of our criminal justice system and perhaps lead the way toward more effective, human-centered reform.

Here’s some of what Serena had to say:

SERENA CHANG: We’ve learned a lot of things that we never would’ve known if we had not talked to clients on supervision. So, we’re often trying to build tools for the parole and probation officers and talking to the clients helps us know, Are these things actually going to work or will they create these unintended impacts? One client that we were talking to said, The best thing that my parole officer could do for me is just acknowledge that I’m a human, not just a number and a crime. So that impacts a lot of design decisions, like we probably should be surfacing the client's name and not their department of corrections ID or number. There are lots of simple things like that, that we can completely overlook if we are not actually talking to the people impacted by the system.

IVAN: Join us next time for the fourth episode of our series, Meeting the Moment: Using Data to Reimagine Criminal Justice. We hope you’ll subscribe. You can find out more online at ten7.com/moment. Thank you for listening.

Credits

This is Episode 116 of The TEN7 Podcast. It was recorded on April 02, 2021 and first published on April 28, 2021. Podcast length is 42 minutes. Transcription by Roxanne Chumacas. Summary, highlights and editing by Brian Lucas. Music by Lexfunk. Produced by Jonathan Freed.

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Ivan Stegic

CEO
 
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Ivan Stegic

Words that describe Ivan: Relentlessly optimistic. Kind. Equally concerned with client and employee happiness. Physicist. Ethical. Lighthearted and cheerful. Finds joy in the technical stuff. Inspiring. Loyal. Hires smart, curious and kind employees who want to create more good in the world.