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Speaker 1: Hello everyone, and welcome to Talk Nerdy. Today is Monday,

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June eighth, twenty twenty sixth, and I'm the host of

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the show, Doctor Kara Santa Maria, And as always, before

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we dive into this week's episode, I want to thank

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those of you who make Talk Nerdy possible. Remember, this

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show is and will always be one hundred percent free

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to download as long as I can keep it on

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the air. We're in our six hundred fifth episode, Wow,

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and many of you have been with us from the

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very beginning. I want to give a shout out to

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this week's top patrons. They include Chuck Blell, David J. E. Smith,

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Daniel Lang, Mary Neva, Bill Sorry, Will Defrain, David Compton,

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Brian Holden, Gabo, Jay Ulrica Hagman, Pus Squally, Jelati, Rika

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Maharaj and Joe Wilkinson. Thank you all so so much.

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All right, let's not waste any time and get into

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this week's episode. I had the opportunity to speak with

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doctor Chad M. Topaz. Chad is a professor of Complex

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Systems at Williams College and co founder of the q

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Side Institute, which uses data science to promote equity and justice.

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He's received several awards from different international and national communities

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all about the studies that he's written that he's published,

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and of course his opinion pieces as well that sort

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of exist at the intersection of data science, social justice,

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and public policy. His new book is right along that vein.

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It's called Unlocking Justice, The Power of Data to confront

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inequity and Create Change. So, without any further ado, here

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he is doctor Chad M. Topaz. Well, Chad, thank you

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so much for joining me today.

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Speaker 2: Thank you, Krotz. I'm honored to be here.

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Speaker 1: Yeah, it's going to be a fun chat. I'm looking

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forward to talking about your new book, Unlocking Justice, The

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Power of Data to confront inequity and create change. So

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before we get into Unlocking Justice, I want to learn

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a little bit more about you. I always like to

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kind of start off the episodes with a bit of background.

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So you are. I love your title. You're a professor

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of complex systems. Like, does every single person you talk

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to go like, what does that mean when you first

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tell them?

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Speaker 2: Pretty much?

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Speaker 1: Yes, So what department would a professor of complex systems

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be working in?

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Speaker 2: Well, my department is perhaps hilariously called the Department of humanities,

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which makes me sound even more confusing than I already do.

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But I would say care that people who work on

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complex systems come from like a range of different disciplines,

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and what we all have in common is wanting to

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understand the ways that macroscopic behaviors and patterns and structure

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and order come out of groups of things that are

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interacting with each other.

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Speaker 1: So are we talking everything from psychology to sociology, to

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economics to even like mathematics.

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Speaker 2: Absolutely, and my own background, I'm trained as a mathematician,

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that's my background. But yes, I think all of those

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disciplines have inroads into complex systems for sure.

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Speaker 1: So you are a pattern seeking creature. I love this,

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and so like, are we talking like big data here?

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Are we talking the sort of intersection between all of

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the pieces of information that we collect all the time,

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either actively and intentionally or passively through other systems, and

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how we can actually use that data for I guess

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good and not evil?

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Speaker 2: Yes, that's exactly that's exactly right, And I guess just

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to share maybe ten seconds worth of the story, you know,

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I mentioned I'm trained as a mathematician and I used

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to work on complex systems in uh, physics, in chemistry

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and in biology, and I found that work really rewarding.

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But I also had this very like political activist side

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to me, and I eventually sort of realized that the

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issues out in the world that I was really passionate

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about and a quantitative skill set that I had could

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be brought together to work on some of these justice

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issues that are you know, the things that make me

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mad when I'm when I wake up every morning and

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read the news.

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Speaker 1: Yeah here here, I absolutely love to hear it. So

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when we talk about these sort of uh, these areas

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where you had already been spending your time, you know,

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studying outside of academia, marching, you know, engaging in political activism,

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are we really talking about all of those obvious places

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and I guess not so obvious places where vulnerable people

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are disadvantaged by a system. So we're talking racial issues,

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gender queer issues, issues around poverty, just all the places

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where we can highlight that individual people have been I

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guess unfairly disadvantaged.

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Speaker 2: Yes, that's exactly right. And I think you've given a

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pretty great definition of what social justice is really about.

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I think I am a strange melange of privilege and

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lack of privilege, and that weird mix is kind of

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what has brought me to this table of working on

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social justice issues. So, on one hand, I am a

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Cis white man who grew up in a very privileged

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setting in the northern suburbs of Chicago. And on the

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other hand, I am a now married gay man who

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kind of came of age in the eighties and nineties

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and have put up with my fair share of things

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on account of that identity, and so that that is

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part of what has brought me to the table. But

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of course I am trying to use the places where

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I have privilege and use the places where I have

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voice to bring some attention to issues that maybe people

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like me don't don't always pay as much attention to.

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Speaker 1: You know, I think that's such an important point that

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you make, and it's one that I I guess I

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relate to really deeply. I mean, first and foremost the

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listeners probably know this, But to kind of parallel your description,

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so I guess I would be considered I'm a woman,

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and I'm I present, I am white, I present white,

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and I'm afforded all the privileges of whiteness, but I'm

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half Puerto Rican I guess I identify as queer, and

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I so I live in these but I'm also like,

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I have a PhD, like and so do you, right,

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So like there's there's like a huge amount of privilege

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that comes from having navigated these academic I guess structures

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as well, and so it does. It's an important I

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guess point about intersectionality that there are those whose voices

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are not elevated enough, and then there are those of

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us who are in positions where we can work to

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elevate those voices. And sometimes the very people doing that

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work are the people with the privilege, and are the

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people who whose colleagues and peers do not recognize why

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this work matters.

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Speaker 2: I think that's exactly right, and I write a little

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bit about this in the book. So I have a

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chapter that maybe comes off as memoir, but it's really

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meant to be about my own positionality. And I think,

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you know, positionality is a tool where the point of

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it isn't to so doubt on the way we think

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about things because of who we are. The point of

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positionality is to provide transparency and help everyone see how

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our backgrounds informs our views and the ways we think

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about things, and so on that note, I sort of

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talk about how I actually, you know, I do care

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about LGBTQUIA plus issues. I care about them deeply. But

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most of the work that I do in social justice,

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despite the fact that I'm on I'm my white man,

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is on issues of race and gender because those are

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axes of identity on which I have a ton of

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privilege and people listen to me because I'm a white man.

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Now there's there's a lot we can say about that

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because this potentially steers dangerously into savior territory, and so

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there's a lot that needs to be said about how

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one tries to avoid that. But that's that's the basic

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idea is. You know, I, because of my identities, I've

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been given a voice, and so I think, like many

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other people who care about things and share my identities,

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I'm just trying to use those tools absolutely.

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Speaker 1: And I mean, yeah, I hear you this idea of

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like it does steer dangerously into savior territory and also

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like appropriation, I think territory and at the same time,

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like I'm not gonna say, but I'm gonna say, and

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you know, I've had so many conversations let's say with

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my partner, for example, where he will discuss because you know,

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I identify as queer, my partner identifies as queer, and

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yet from the outside it looks like we're in a

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heteronormative relationship, and so he will discuss some of his

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male colleagues and peers, especially as white male colleagues and peers,

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like views and opinions, especially in this sort of post

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Trumpian world where we're living. I mean, just the other day, gosh,

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I hope he doesn't get mad at me for outing

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him for this one. The other day, he was saying

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how he was at this art thing because he's an artist,

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and folks were talking about, oh, it's just like men

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can't even get single solo shows anymore. They're all going

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to win, and he was like, are you fucking kidding me?

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And so we're always talking about how important it is

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for him to speak up in those moments because they

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may not listen to me. The very sensibility that underlie

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somebody bitching and moaning about women getting all of the

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art shows is going to make it so that my

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opinion weighs less to them than a male counterpart opinion

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and it's frustrating as that is that's the reality of

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the world we're in.

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Speaker 2: I think this is absolutely true, and you know I

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experienced this. People get mad at me because of the

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things I say in defense of other groups, and you know,

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I can handle it because it's actually very hard to

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hurt me, Carol. Like, I'm a upper middle class CIS

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white man who's like tenured and promoted at a fancy college.

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Like they're so little other than saying mean words. There's

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so little that that people can do to me, and

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in most ways, I'm really like not vulnerable. I Also,

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I wanted to circle back to the thing about art

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because this is so interesting to me. My book is

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really about social justice as it pertains to race in

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the criminal legal system. But I tell a story early

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on about how part of what brought me to doing

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this kind of work was another project that was specifically

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about demographic diversity in art museums. And so this is

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the story here, if I can share with you, is

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that I live in the Northern Berkshire, so in the

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northwest corner of the state of Massachusetts, and ten minutes

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from my house is the Massachusetts Museum of Contemporary Art, MassMOCA,

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and oftentimes people think, oh, a big art museum in

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Massachusetts is in Boston, but actually no, mass Moca is

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a giant museum and it's it's ten minutes away from

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me out here in western mass And soon after I

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moved here, in the summer of twenty seventeen, a new

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wing was opening at MassMOCA, and I happened to be

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friends with a bunch of artists sort of in person

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and online, and they were all discussing the fact that, like, wow,

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did anyone notice that all of the new artists, you know,

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shown in the new wing at mass Moca are white dudes?

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And I was like, I was like so naive care.

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I was like, oh, I'm like typing at my computer

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on Facebook because this was ten years ago. I was like, always,

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is diversity in the art world a problem? Question mark?

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And my friend, my friend, who's like an art scholar,

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is like, Chad, yes, like, get with the program. It's

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like a huge problem.

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Speaker 1: And I'm like, it's the problem literally everywhere. So why

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would it avoid that part of this, you know, the

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social miliaure exactly?

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Speaker 2: But I said, but but how big? And He's like big,

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And I'm like okay, but I am a numbers nerd.

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How big And he's like shrug, like no one actually knows.

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And I thought, okay, I actually know how to help

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figure this out. And I don't at all want to

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say that counting something is the thing that makes it real.

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That is not true at all. And it is a

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very key important message of my book that like, numbers

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are one way of understanding things, and they give us

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a lens, but they do not replace humans. There are

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other ways of knowing things, and ultimately it's human stories

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and human experiences matter. But that said, we were able

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to show numerically the extent to which major US art

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museums are just absolutely dominated in their collections by white men,

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and we were able to use this to open some

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conversations with museum curators and kind of force them to

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reckon with a truth that I think they may have

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been more able to deny previously.

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Speaker 1: Right without without the actual like statistics in front of them.

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And I think, you know, I had, in a micro

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version of that, a similar experience when I was getting

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all like huffy when we were texting about this. He

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was overseas at the time, and I was like, what

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do they mean, what is their argument. He was like, oh,

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just that it's so hard for like a dude, or

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especially like a white dude to get a solo art

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show because of like the the you know culture, the

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dei kind of culture we're living in. And I just

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like literally googled for two minutes and I was like,

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eighty seven percent of exhibited artists in major US museums

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are men, seventy percent of artists exhibited in major galleries

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are men. And how about this one ninety six percent

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of art sold at auction is my male artists?

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Speaker 2: M Yeah, I was like.

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Speaker 1: Oh no, those ninety six percent of men are having

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a hard time right now.

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Speaker 2: I know I could as a white man, I can

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make this joke. It's like white dudes will like lose

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one percent of what they have and complain about it

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before they go to therapy, right, Yes, yes.

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Speaker 1: They will do anything before therapy. Right, Like my favorite

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meme online is like dudes doing MMA or like all

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sorts of really aggressive do anything before therapy. No, it's

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it's such it's such a major concern. And again, like

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part of our privilege is being able to laugh about

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this right now, because I can imagine that there is

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a you know, a queer black female artist who's listening

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to this episode and who's like, there's like smoke coming

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out of her ears, or this is you know, causing

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like a triggering of some real trauma. And that's I

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think part of you know, I'm so glad we're having

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this conversation today because this is just a small thing

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by comparison. But I literally just sent this morning a

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message to somebody at the hospital where I work. I'm

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a psychologist in a large hospital about the fact that

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and I wasn't saying this is institutionwide, because it's absolutely not,

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but that there are certain pockets of individuals where consistently

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they refer to me as Kara when they refer to

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male physicians as doctor so and so, And I think

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part of it is the intersectionality of being a woman

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versus man. But then I was like, you know, they

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refer to the site some of the other psychiatrists as

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doctor so. And then I was like, oh, is it

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because I'm a psychologist and not a psychiatrist because my

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doctor is a PhD and not an MD. And I

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think that's another area, Like it's a weird nuanced area

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in hospitals where there is some amount of like inequity

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around titling, and I hate having to talk about it

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because it makes me sound petulant. And at this same time,

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it's not a good precedent because it's steeped in so

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much sort of gendered nonsense, just a lot of history

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around women having to work ten times as hard to

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get the exact same outcome as a man.

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Speaker 2: Completely agree. This is also a thing in my experience

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in academia. Right, So, I teach at a small liberal

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arts college where part of what we prize is like

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just really sort of like close knit relationships with our

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students where we work one on one with them and

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are very accessible to them. And it's that kind of experience.

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And in my teaching career, I've always gone by my

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first name, you know, college Chad. And at some point

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in my career a colleague of mine, who I respect

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greatly and who is a woman, said to me, like,

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you know, it's great that you can do that with

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your students, but like I don't actually have quite the

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same privilege of being able to do that, because my

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students already like have a hard time believing that I'm

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a professor or that I'm a doctor, or that I

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am someone who they should like pay some modicum of

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respect to. And it was really important for me to

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hear that and really important for me to think about that.

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And so now this is like something that I discussed

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with colleagues, like how are you comfortable like with me

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titling myself? And I also discuss it with my own students,

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like if you're going to call me by my first name,

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we can have that agreement, but you need to understand,

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like here are some of the structures at play. You

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can't do this with your other faculty. You need to

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follow like how they want to be addressed and realize

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the positions that they're in.

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Speaker 1: Yeah, that's when it's nuanced, right. And I remember, like

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I was grappling with my wording, and I think I

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said something like among colleagues and in the right context,

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I'm perfectly happy being called Kara, Like I don't mind

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that you call me kara, and that so and so

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calls me kara. But what I don't like is when

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in rich and correspondence in the same context you call

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me kara, and then you call so and so doctor

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so and so like that that's a horrific precedent, yep,

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because then people don't even know what my title is

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or what you know, it kind of what's appropriate within

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a business Meeta, It's like, oh my god. And I

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think that that's just one example of like so many

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under the radar areas of kind of unequal treatment that

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then translate to you know, disparities in the social or

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in the criminal justice system, disparities with global poverty. You know.

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It's like these little things are directly related to really

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really big things, aren't they.

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Speaker 2: Yes, I mean I think that many times. Uh, And

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I'm always I'm always a little bit careful with this

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word because when we say the word in microaggression, it

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makes it sound like the thing is unimportant, And I

336
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think that's not the meaning of the word microaggression, right.

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Speaker 1: It's more that like people don't realize they're doing it, right,

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not that it doesn't have like huge psychological impact.

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Speaker 2: Right, and that it's happening on a very localized scale

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around you, right, it's your personal experience. But I do

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think that the source of these microaggressions and the source

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of you know, the big structural disparities that we that

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we see out in the world. Those things are really

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tied together, undeniably.

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Speaker 1: Absolutely, And so I'm curious, you know, in your career

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thus far, whether we're talking about your uh, I guess,

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your doctoral dissertation back whenever that was all the way

348
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up through the the active research that you're engaged in now,

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or the work that your students do, or even your teaching,

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how has you know, big data taken I guess, how

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has that been positioned in some of these important questions

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that you've been asking. What are you kind of been

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focusing on throughout your career?

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Speaker 2: Yeah? Absolutely, So When I started out in my academic career,

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like as a graduate student, I worked in physics, I

356
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worked in fluent physics, Like I was so far from

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this stuff you can't even believe, and it was not

358
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very data driven. But you know, the past couple of

359
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decades have seen what we like to describe very commonly

360
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as the deluge of data or the proliferation of data.

361
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We have access to more data than we've ever had before.

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We also have access to more computing power than we've

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ever had before. And these things make new inquiries possible, right,

364
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like new routes of investigation that we could not do previously,

365
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and so really like the work that I described in

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my book is centered around using data relevant to the

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criminal legal system to expose and hopefully remedy injustice, specifically

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around race and specifically around issues such as policing and

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bail and criminal sentencing. Those are things that I've been

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really putting a lot of attention on. And I think

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there's two stories there. One is just like the story

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of the disparities and the discrimination and how data can

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help us. The other is sort of a meta story

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about data itself and why it is that even in

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this era of data, sometimes getting information about the way

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our criminal legal system is operating is way harder than

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it should be given what our rights are in this country.

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And so that's a whole other level to the book

379
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is like what are the forces that shape our access

380
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to data? What are the forces that shape biases inherent

381
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in the data? All of these sorts of things. And

382
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I mean, I'll pause here because I don't want to

383
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filibuster you with my speech, but I I have some

384
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absolutely bunkers stories about the way data has been made

385
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inaccessible to hide injustice and the ways that data itself

386
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has encoded racist things over the years.

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Speaker 1: Okay, I am like, I'm fascinated and excited to get

388
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into that because I have a feeling that's going to

389
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be what we spend the majority of this hour on.

390
00:23:25,400 --> 00:23:28,119
I wanted to flag beforehand. It's so funny you're talking

391
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about these different kind of layers and this sort of

392
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like meta kind of I guess, yeah, layer component to

393
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the book, and it's I was about to. I was

394
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like biting my tongue. I was like, I want to

395
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talk about process and content. And I got some feedback

396
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from a listener recently that was like that one episode

397
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where you were going on about process and I could

398
00:23:46,839 --> 00:23:48,519
not fall that was so boring and I was like,

399
00:23:48,599 --> 00:23:52,640
okay to but it's definitely something I think about a

400
00:23:52,640 --> 00:23:55,960
lot as a psychologist, and I often talk about with

401
00:23:56,000 --> 00:23:59,960
my students the trainees that I supervise. Is even like

402
00:24:00,079 --> 00:24:03,680
when we're in a therapy session, there is a difference

403
00:24:03,839 --> 00:24:08,240
between process and content, Like the content is the stuff,

404
00:24:08,599 --> 00:24:11,279
and sometimes in therapy people will get in the weeds

405
00:24:11,400 --> 00:24:14,160
talking about all the minutia and the details of the stuff,

406
00:24:14,200 --> 00:24:16,759
and that can be really important. That can be necessary

407
00:24:16,799 --> 00:24:19,799
to get something off your chest and to feel validated

408
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or to really do the work. And then the process

409
00:24:23,160 --> 00:24:28,000
is you know, from a psychological perspective, it's the how

410
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would I put this? It's the sort of top down

411
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umbrella gestalt of what that stuff represents. Oh okay, here's

412
00:24:38,200 --> 00:24:40,400
a pattern. I'm seeing that over and over. You're struggling

413
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with boundaries, Like we could talk about all the little

414
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thing and then she said this, and he said this,

415
00:24:44,839 --> 00:24:47,119
and she said but really, like I'm hearing over and

416
00:24:47,200 --> 00:24:49,880
over that, like you're trying to set boundaries for other

417
00:24:49,920 --> 00:24:53,079
people instead of setting boundaries for yourself. Let's like unpack that.

418
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And so a book like this, I was curious, how

419
00:24:57,440 --> 00:25:01,000
much did you struggle with Okay, do I talk about

420
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individual specific stories and examples, because that's the stuff that

421
00:25:05,039 --> 00:25:09,880
I think really gets people interested. That's the content, but

422
00:25:10,240 --> 00:25:16,200
in service to helping us understand these processes, because it's

423
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not just I guess, a conglomeration. It's not just like

424
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a laundry list of insults. It's not a laundry okay,

425
00:25:26,039 --> 00:25:27,799
And then here's this story, and then here's this story,

426
00:25:27,799 --> 00:25:31,039
and then like really, after a while, we have to

427
00:25:31,079 --> 00:25:34,640
recognize that a pattern is emerging from this data, and

428
00:25:34,720 --> 00:25:38,160
that there are systems at play, and I feel like that,

429
00:25:38,319 --> 00:25:45,240
for some reason is much harder for the citizenry to understand.

430
00:25:45,440 --> 00:25:48,799
They love saying that guy is racist, and they really

431
00:25:48,839 --> 00:25:52,119
struggle with the idea that a system could be racist.

432
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Speaker 2: Yes, I completely agree, and so that is a major

433
00:25:56,720 --> 00:26:00,640
mission of the book and the way that you're talking

434
00:26:00,680 --> 00:26:04,200
about content and process, I would say the sort of

435
00:26:04,240 --> 00:26:06,960
analogy I've made in the book is to think about,

436
00:26:07,000 --> 00:26:09,000
like when you watch a movie, and there's the movie,

437
00:26:09,039 --> 00:26:11,720
and then there's the director's track, which is like, what

438
00:26:11,960 --> 00:26:16,279
was the story going on behind the movie that informs

439
00:26:16,400 --> 00:26:20,079
why it is the way it is secretly, And that's

440
00:26:20,079 --> 00:26:21,680
the way I look at the book. And so there's

441
00:26:21,960 --> 00:26:26,119
these stories of injustice in the criminal legal system and

442
00:26:26,359 --> 00:26:29,880
us using data to address it, and then the director's

443
00:26:29,920 --> 00:26:33,400
track is the part that talks about why was getting

444
00:26:33,400 --> 00:26:36,359
the data so hard? Why are these the racial categories

445
00:26:36,440 --> 00:26:39,039
in the data? Wow, that's a really messed upset of categories,

446
00:26:39,440 --> 00:26:42,160
Like Wow, why is it that we have you genesists

447
00:26:42,200 --> 00:26:45,000
who were using data this way? Right? And so that

448
00:26:46,160 --> 00:26:49,440
the book is really an attempt to give those things

449
00:26:49,559 --> 00:26:52,920
more or less equal weight.

450
00:26:53,319 --> 00:26:57,279
Speaker 1: Yeah, speaking of you, I love that you use that example.

451
00:26:57,279 --> 00:26:59,240
I'd love to dig into that. I always like to

452
00:26:59,279 --> 00:27:01,319
tell the story of Like I was watching a talk

453
00:27:01,400 --> 00:27:04,319
once as an undergraduate when I was you know, had

454
00:27:04,359 --> 00:27:07,039
my little naive baby brain, and I was still learning

455
00:27:07,039 --> 00:27:08,960
so much. I mean, I'm still learning so much. But

456
00:27:09,119 --> 00:27:11,799
man was I learning so much back then. And there

457
00:27:11,880 --> 00:27:15,440
was a visiting scholar who told a story about the

458
00:27:16,279 --> 00:27:21,119
kind of the foundations of uh psychological statistic You know,

459
00:27:21,119 --> 00:27:25,039
a lot of our statistics was was first developed by psychologists,

460
00:27:25,079 --> 00:27:27,720
and a lot of it was developed with the express

461
00:27:28,160 --> 00:27:33,680
uh kind of intention of proving the superiority of white people.

462
00:27:33,960 --> 00:27:35,279
Speaker 2: Like this was, Yeah, it.

463
00:27:35,240 --> 00:27:37,759
Speaker 1: Was like an overt decision that was, Man, I'm going

464
00:27:37,799 --> 00:27:39,759
to develop an IQ test and I'm going to do

465
00:27:39,799 --> 00:27:42,079
it in such a way that it shows that white

466
00:27:42,079 --> 00:27:43,920
people are superior. And then I'm going to analyze the

467
00:27:44,000 --> 00:27:46,160
data using you know, these regression models and all these

468
00:27:46,200 --> 00:27:49,519
different tools see see white people better. Like this was

469
00:27:49,559 --> 00:27:52,400
a like eugenesis were all over early psychology. I mean,

470
00:27:52,720 --> 00:27:55,319
it's still in there, right and so, but they were

471
00:27:55,319 --> 00:27:58,519
also talking about genetics, and you know, genetics as founded

472
00:27:58,559 --> 00:28:01,000
in eugenics, and they showed this it's still to my

473
00:28:01,079 --> 00:28:04,440
days like burned in my memory. This image of I

474
00:28:04,440 --> 00:28:06,119
don't know many people who are listening to the show

475
00:28:06,160 --> 00:28:08,759
if you spent much time in academia. But you know,

476
00:28:08,839 --> 00:28:11,480
if you've ever visited a university library where all the

477
00:28:11,480 --> 00:28:15,400
periodicals are, they get bound up in books every however

478
00:28:15,440 --> 00:28:18,160
many years, right, so you don't just have the individual

479
00:28:18,279 --> 00:28:20,880
journals you have like this, You know, these years of

480
00:28:20,960 --> 00:28:22,920
journals in this hardcover and they all look the same

481
00:28:22,960 --> 00:28:26,200
and very fancy like law books almost. And it literally

482
00:28:26,240 --> 00:28:30,079
showed the changeover from this one journal going from being

483
00:28:30,119 --> 00:28:34,200
called the Annals of Eugenics to the Annals of Genetics

484
00:28:34,279 --> 00:28:37,720
the next year. And I was like whoa, And it

485
00:28:37,799 --> 00:28:42,400
was just so like telling you know, and yeah, I

486
00:28:42,440 --> 00:28:44,240
always love to tell that story. And I still I

487
00:28:44,240 --> 00:28:46,160
haven't been able to find the image since. So if

488
00:28:46,200 --> 00:28:48,599
anybody has that image, send it my way, because I

489
00:28:48,640 --> 00:28:51,279
want to put it in every talk ever. Okay, go ahead,

490
00:28:51,400 --> 00:28:54,440
tell me a little more about these eugenesis that you mentioned.

491
00:28:55,240 --> 00:28:59,759
Speaker 2: Oh well, I must I okay. I'm always going to

492
00:28:59,799 --> 00:29:03,960
say about the eugenicists is you know, we have this

493
00:29:04,039 --> 00:29:07,000
phrase like lies, damn lies and statistics, and I think,

494
00:29:07,039 --> 00:29:11,640
as you said, eugenics is infamous as like a really

495
00:29:11,720 --> 00:29:17,240
effective and extremely harmful way of misusing statistics, so that

496
00:29:17,519 --> 00:29:21,599
the eugenicists did a lot of conflating correlation with causation

497
00:29:22,359 --> 00:29:26,440
in ways to claim things like a criminality is an

498
00:29:26,480 --> 00:29:31,319
inherited trait, right, like not not caused by structural things

499
00:29:31,400 --> 00:29:34,319
like poverty, but like, oh, some families just be crime.

500
00:29:34,359 --> 00:29:40,039
And you know, eugenicists relied on biased data, so they

501
00:29:40,039 --> 00:29:46,039
would do things like collect data from prisons and asylums

502
00:29:46,400 --> 00:29:49,440
and then analyze the data and say like, oh, like,

503
00:29:50,160 --> 00:29:53,240
you know, there's these racial groups in these prisons and

504
00:29:53,279 --> 00:29:56,839
asylums and they're they're they have all of these you know,

505
00:29:57,839 --> 00:30:01,519
weaknesses shown in their statistics. But of course what this

506
00:30:01,640 --> 00:30:04,519
ignores is that these people have been plucked out of

507
00:30:04,559 --> 00:30:10,519
life and placed in circumstances to limit their education and opportunity.

508
00:30:10,799 --> 00:30:12,839
So they I mean, those are just two examples, but

509
00:30:12,920 --> 00:30:15,359
they yes, they did a lot of thing. Yeah, And

510
00:30:15,839 --> 00:30:17,720
I really want to be careful also that I shouldn't

511
00:30:17,720 --> 00:30:21,440
only use the past tense, because we think of eugenics

512
00:30:21,480 --> 00:30:23,759
as like being a thing that was like you know,

513
00:30:24,240 --> 00:30:27,359
back in the day and used as sort of justification

514
00:30:27,440 --> 00:30:31,440
for Nazi Germany. But we have plenty of eugenic ideologies

515
00:30:31,799 --> 00:30:34,640
that are rearing their heads to this day.

516
00:30:35,200 --> 00:30:37,920
Speaker 1: Absolutely, and it bears repeating, and I've said it a

517
00:30:37,920 --> 00:30:39,440
million times on the show, so this should not be

518
00:30:39,519 --> 00:30:43,720
new to you listener. We exported that ideology from the

519
00:30:43,839 --> 00:30:48,200
United States to Germany. Like these were not ideas that

520
00:30:48,279 --> 00:30:53,039
were somehow fundamentally German ideas. There was a massive eugenics

521
00:30:53,039 --> 00:30:57,160
movement in the United States, and many of those ideas

522
00:30:57,200 --> 00:31:00,799
which were seen at the time as positive, like it

523
00:31:00,839 --> 00:31:04,359
was all about like living better through good genes and

524
00:31:04,480 --> 00:31:08,799
healthy families. Like it was definitely positioned that way here

525
00:31:08,839 --> 00:31:13,759
in the US prior to the nineteen thirties, which I

526
00:31:13,799 --> 00:31:16,200
think is an important point to make. But you know,

527
00:31:16,200 --> 00:31:18,160
there's a modern example I think of, and I wonder

528
00:31:18,160 --> 00:31:20,279
if you've come across this. There's a there's a test

529
00:31:20,359 --> 00:31:23,880
in the in psychology called the MMPI and it's probably

530
00:31:23,880 --> 00:31:27,960
one of the most studied. It's Minnesota Multi Phasic Personality

531
00:31:27,960 --> 00:31:32,240
Industry industry not industry, God, why am I blanking on

532
00:31:32,279 --> 00:31:36,880
my words? The Minnesota Multi Phasic Personality Inventory. And it's

533
00:31:36,880 --> 00:31:39,960
this really intense test with a million different kind of

534
00:31:40,839 --> 00:31:44,240
scales inside of it, and it spits out now there's

535
00:31:44,240 --> 00:31:47,119
like computer ways to score it, but it can say, oh,

536
00:31:47,119 --> 00:31:49,079
this person has you know, a little hit over here

537
00:31:49,119 --> 00:31:51,079
on psychopathy, and over here it looks like there's some

538
00:31:51,119 --> 00:31:54,160
difficulty with social interaction, and over here there may be

539
00:31:54,240 --> 00:32:00,000
some you know, gendered self questioning, and over it's it's

540
00:32:00,000 --> 00:32:01,920
it's a fascinating test. It's one of the most well

541
00:32:01,960 --> 00:32:03,880
researched tests, and it does have a lot of good

542
00:32:03,920 --> 00:32:06,400
things going for it. But you know who consistently scores

543
00:32:06,440 --> 00:32:13,160
high on scales of paranoia Black people? And literally, now

544
00:32:13,359 --> 00:32:15,480
when we teach this test, we have to be like,

545
00:32:16,240 --> 00:32:20,799
this test is biased. See here, it's written from a

546
00:32:20,839 --> 00:32:24,480
white perspective where they ask questions like the police are

547
00:32:24,519 --> 00:32:27,519
out to get me, and if people say yes, they

548
00:32:27,559 --> 00:32:33,200
flag that as psychosis. Whoa yeah. So it's like all

549
00:32:33,279 --> 00:32:36,240
of this stuff has to be seen in a social

550
00:32:36,359 --> 00:32:41,240
context that understands inequity. But when it's written with an

551
00:32:41,240 --> 00:32:49,759
assumption that everyone's on a level footing, everything downstream gets

552
00:32:49,839 --> 00:32:51,119
really off.

553
00:32:51,880 --> 00:32:58,640
Speaker 2: Absolutely. Well, Kara, it's funny you had mentioned the police. Yeah,

554
00:32:58,799 --> 00:33:03,839
it's actually it's not funny. But one of the chapters

555
00:33:03,880 --> 00:33:09,400
of my book is about my own small town of Williamstown,

556
00:33:09,440 --> 00:33:14,079
Massachusetts town to meet thousand people out here in western

557
00:33:14,119 --> 00:33:17,799
mass in the Berkshires, where I learned a couple of

558
00:33:17,880 --> 00:33:21,720
years after arriving here that there had been a picture

559
00:33:21,799 --> 00:33:25,359
of Hitler posted in the police station for about twenty years,

560
00:33:26,319 --> 00:33:30,279
and this allow yes, and this led us on a

561
00:33:30,400 --> 00:33:34,319
journey to try to obtain data about what the police

562
00:33:34,480 --> 00:33:38,079
department was doing. And one of the things we learned

563
00:33:38,079 --> 00:33:41,559
from studying this data is that the rate at which

564
00:33:41,680 --> 00:33:46,920
different racial groups in town are accessing police services is vastly,

565
00:33:47,119 --> 00:33:51,160
vastly different, and for anyone paying attention, this should not

566
00:33:51,200 --> 00:33:53,920
come as a surprise, right, Like, if you are someone

567
00:33:53,960 --> 00:33:57,799
like me, who's a white man, who's not in groups

568
00:33:57,839 --> 00:34:01,200
that are targeted by the police, who's really not threatening

569
00:34:01,240 --> 00:34:04,759
interactions with the police, if I have a public safety concern,

570
00:34:05,480 --> 00:34:08,320
I might not hesitate too much to pick up the

571
00:34:08,360 --> 00:34:11,480
phone and report my concern. But if you are someone

572
00:34:11,519 --> 00:34:15,119
from a demographic group who's targeted by the police, right

573
00:34:15,159 --> 00:34:20,400
who experiences violence, including murder by the police, you might

574
00:34:20,480 --> 00:34:23,800
hesitate to pick up that phone and report a safety concern.

575
00:34:23,840 --> 00:34:26,400
You might worry that if you call, you will find

576
00:34:26,440 --> 00:34:30,079
yourself actually being the target of the police, and not

577
00:34:30,159 --> 00:34:32,800
whatever is the concern you called about, and this is

578
00:34:32,840 --> 00:34:36,079
something that we see showing up in our data that

579
00:34:36,079 --> 00:34:39,079
we obtained in this small town where we indeed had

580
00:34:39,119 --> 00:34:41,320
a Hitler picture in the police station.

581
00:34:42,039 --> 00:34:43,039
Speaker 1: Oh my goodness.

582
00:34:43,559 --> 00:34:43,880
Speaker 2: Yeah.

583
00:34:43,920 --> 00:34:46,719
Speaker 1: And I think, like until you actually look at those

584
00:34:46,840 --> 00:34:50,559
numbers again you mentioned before, and it's such an important point.

585
00:34:50,599 --> 00:34:54,679
I think it bears highlighting and repeating something is not true,

586
00:34:55,320 --> 00:34:58,559
only because we have statistics to back it up, but

587
00:34:58,639 --> 00:35:03,480
it definitely makes it harder to reject and deny and

588
00:35:03,599 --> 00:35:07,239
hide from once we have, you know, the numbers that

589
00:35:07,360 --> 00:35:10,920
back it up. I remember having a conversation with two

590
00:35:11,079 --> 00:35:14,639
very dear friends. We were all in like a very

591
00:35:15,000 --> 00:35:19,760
deep and I think sensitive conversation at the time. I

592
00:35:19,840 --> 00:35:21,639
mean this would have been like about a year ago,

593
00:35:21,760 --> 00:35:26,840
so so contextually it would make sense about sort of

594
00:35:27,239 --> 00:35:32,079
the issues the the anti semitism facing you know, Jewish

595
00:35:32,079 --> 00:35:35,519
folks in the world today and also the abject racism

596
00:35:35,559 --> 00:35:37,480
facing black folks in the world today. And it was,

597
00:35:37,719 --> 00:35:39,599
you know, a black not Jewish friend and then a

598
00:35:39,639 --> 00:35:43,559
white Jewish friend and all kind of having a conversation

599
00:35:43,599 --> 00:35:45,880
about this, and my Jewish friend made a point, and

600
00:35:46,119 --> 00:35:47,559
this was in the context of saying, we are not

601
00:35:47,559 --> 00:35:50,480
talking about the oppression Olympics here. Nobody is trying to out,

602
00:35:50,880 --> 00:35:57,519
you know, inequity each other. Right, But she said, you know,

603
00:35:57,559 --> 00:36:01,320
when I call the cops, they still come, and they

604
00:36:01,320 --> 00:36:06,360
come fast. And to me, that is a an indicator

605
00:36:06,920 --> 00:36:09,760
of how society is treating these two different groups like

606
00:36:09,800 --> 00:36:12,679
we yes, we have our massive issues, and yes I

607
00:36:12,719 --> 00:36:18,199
feel unsafe in this, in this sort of zeitgeist, but

608
00:36:19,239 --> 00:36:21,840
I am not as afraid. And I think that was

609
00:36:21,880 --> 00:36:25,719
a turning point in Trump's America. Is when you know,

610
00:36:26,000 --> 00:36:29,239
a white woman protester was shot point blank by ice.

611
00:36:29,480 --> 00:36:33,679
Everybody said, oh shit, the privilege that we thought was

612
00:36:33,719 --> 00:36:36,199
there isn't there. And then a white man you know,

613
00:36:36,440 --> 00:36:39,280
and so, uh, it's it's and and I think that

614
00:36:39,519 --> 00:36:43,400
brings it. That is such a perfect example of privilege

615
00:36:43,440 --> 00:36:47,119
becoming crystal clear and bringing the issues home for everybody.

616
00:36:47,519 --> 00:36:50,360
Is when somebody who who was like, well, but I

617
00:36:50,559 --> 00:36:52,679
you know you like you said before, like it's hard

618
00:36:52,679 --> 00:36:55,239
to hurt me, and then it's like all of a sudden,

619
00:36:55,239 --> 00:36:58,719
oh no. That is a real obvious indicator that we

620
00:36:58,760 --> 00:37:01,119
are all on the same team, fighting for the same thing,

621
00:37:01,239 --> 00:37:05,000
and yes, we are still afforded significantly more privileged. But

622
00:37:05,480 --> 00:37:07,480
when it comes down to it, it's like every punk

623
00:37:07,559 --> 00:37:09,800
rock lyric. You know, first they came for the then

624
00:37:09,880 --> 00:37:12,760
they came for they. Now who's left to defend us?

625
00:37:13,480 --> 00:37:13,880
Speaker 2: Indeed?

626
00:37:14,559 --> 00:37:17,159
Speaker 1: Yeah, whoof so so on? That?

627
00:37:17,320 --> 00:37:17,639
Speaker 2: Really?

628
00:37:17,679 --> 00:37:22,679
Speaker 1: Like you know, bone chilling, no, yes, lighthearted, this show is.

629
00:37:22,920 --> 00:37:25,159
Let's talk a little bit about some of those you know,

630
00:37:25,320 --> 00:37:28,199
as you said, like these kind of bonkers bananas stories

631
00:37:28,239 --> 00:37:30,760
that you came across, how you chose what you wanted

632
00:37:30,800 --> 00:37:32,239
to talk about in the book, and maybe you can

633
00:37:32,360 --> 00:37:34,920
highlight a few of them for us.

634
00:37:35,400 --> 00:37:39,840
Speaker 2: Sure. Well, my students ask me this too, They say, like, chat,

635
00:37:39,840 --> 00:37:42,280
how do you decide what like projects to work on?

636
00:37:42,719 --> 00:37:45,239
And honestly, like, I wake up early early every day

637
00:37:45,320 --> 00:37:48,800
and I drink coffee and I like am busy feeling

638
00:37:48,880 --> 00:37:51,840
I rate about things. And the answer is like, what

639
00:37:51,920 --> 00:37:55,880
I feel irate about is what I try to work on.

640
00:37:56,159 --> 00:37:59,519
Although I think it's I want to say that I

641
00:37:59,559 --> 00:38:04,760
want my iratenus to be inclusive, right right, I don't

642
00:38:04,760 --> 00:38:07,000
want to pick and choose the things that matter because

643
00:38:07,039 --> 00:38:09,840
of what you just said, and like we're not we're

644
00:38:09,840 --> 00:38:14,280
not free until we're all free, right in any case,

645
00:38:15,000 --> 00:38:19,679
I'll tell you a story so you may remember and

646
00:38:20,119 --> 00:38:23,760
the listeners may remember. A man called Paul Manifort. Paul

647
00:38:23,840 --> 00:38:28,119
Manifort was Donald Trump's like first campaign chair in Donald

648
00:38:28,159 --> 00:38:33,119
Trump's like a campaign for his twenty sixteen election. And

649
00:38:33,400 --> 00:38:37,760
Paul Manifort was incredibly accused of doing a whole bunch

650
00:38:37,760 --> 00:38:41,480
of illegal stuff and he was prosecuted and he was convicted.

651
00:38:41,920 --> 00:38:45,639
And one day I was reading the news and there

652
00:38:45,760 --> 00:38:48,960
was a headline that was, like, you know, the public

653
00:38:49,119 --> 00:38:55,599
and legal experts gasp when Manifort given forty seven months,

654
00:38:56,039 --> 00:39:00,599
like despite recommended seventeen to twenty four years sent And

655
00:39:00,639 --> 00:39:05,760
I was like, what's going on? I didn't and I

656
00:39:05,800 --> 00:39:08,480
have been like so shielded from the criminal legal system

657
00:39:08,800 --> 00:39:11,360
that I like didn't even know how criminal sentencing works

658
00:39:11,400 --> 00:39:13,800
because it's not a thing I've ever had to contend with.

659
00:39:15,000 --> 00:39:17,760
But I just thought, wow, this sounds like, this sounds

660
00:39:18,159 --> 00:39:23,000
bonkers that then there could be like guidelines that recommend

661
00:39:23,159 --> 00:39:26,679
decades worth of prison for this convicted man, and actually

662
00:39:26,760 --> 00:39:28,559
what he got was under four years, and in the

663
00:39:28,679 --> 00:39:31,159
end he ended up serving substantially less than that.

664
00:39:31,400 --> 00:39:33,760
Speaker 1: Even oh right, that doesn't surprise me either.

665
00:39:34,400 --> 00:39:38,440
Speaker 2: Yeah, So this sent me on a very long journey

666
00:39:38,480 --> 00:39:42,840
to educate myself about what we know about criminal sentencing

667
00:39:42,960 --> 00:39:47,039
in this country. And because Paul Manifort was prosecuted and

668
00:39:47,039 --> 00:39:51,480
convicted for federal crimes, I'm thinking in this story primarily

669
00:39:51,519 --> 00:39:55,360
of our federal court system. And I learned that it

670
00:39:55,440 --> 00:39:59,000
is very well known that there are inaggregate, gross racial

671
00:39:59,000 --> 00:40:03,320
disparities in criminal sentencing. So you sort of do apples

672
00:40:03,519 --> 00:40:09,039
to apples comparisons of black men to white men and

673
00:40:09,199 --> 00:40:15,079
or Hispanic Latin men to white men, that those minoritized

674
00:40:15,159 --> 00:40:20,199
racial groups receive much longer criminal sentences for the exact

675
00:40:20,280 --> 00:40:24,199
same crimes and even having the same criminal histories essentially

676
00:40:24,280 --> 00:40:26,719
just for the crime of not being white, right like,

677
00:40:26,800 --> 00:40:30,000
they are getting longer sentences. And it was like, wow,

678
00:40:30,280 --> 00:40:32,039
how can this be? And so then I had to

679
00:40:32,119 --> 00:40:35,320
learn about how criminal sentencing works, and I learned how

680
00:40:35,400 --> 00:40:39,480
much discretion judges actually have in choosing our criminal sentence.

681
00:40:39,519 --> 00:40:43,079
We have guidelines that recommend to sentences based on many,

682
00:40:43,119 --> 00:40:47,360
many factors. The US Criminal Sentencing Handbook is like six

683
00:40:47,480 --> 00:40:50,880
hundred plus pages long, so it's a very complicated set

684
00:40:50,880 --> 00:40:54,159
of rules for recommending criminal sentences. But in the vast

685
00:40:54,239 --> 00:40:58,679
majority of cases. With very few exceptions, judges have latitude

686
00:40:58,719 --> 00:41:01,239
to just choose the sentence.

687
00:41:01,239 --> 00:41:05,039
Speaker 1: Which is why it's important when judges are installed with

688
00:41:05,119 --> 00:41:06,360
a political agenda.

689
00:41:07,199 --> 00:41:11,320
Speaker 2: Heck, yes it is and so but but care. This

690
00:41:11,360 --> 00:41:14,280
is where I started losing my mind. So I thought, like, well,

691
00:41:14,320 --> 00:41:17,320
we know that there's these gross racial disparities, and we

692
00:41:17,400 --> 00:41:21,400
know judges are these people with vast discretion and very

693
00:41:21,400 --> 00:41:25,280
secure like lifetime appointment jobs. So like, who are the

694
00:41:25,400 --> 00:41:30,719
judges who are giving these bias sentences? I was like,

695
00:41:30,760 --> 00:41:33,880
we must know, surely we know. And it turns out

696
00:41:33,920 --> 00:41:37,039
we don't know. And it turns out that the reason

697
00:41:37,079 --> 00:41:40,880
we didn't know is because the data that is put

698
00:41:40,920 --> 00:41:45,360
out by the federal Judiciary about federal criminal sentences does

699
00:41:45,400 --> 00:41:49,039
not include the name of the judge who gives the sentence.

700
00:41:50,480 --> 00:41:54,880
This was strange to me because there is a Supreme

701
00:41:54,960 --> 00:41:58,440
Court case called Richmond Newspapers versus Virginia. I think this

702
00:41:58,480 --> 00:42:00,559
is from the year nineteen eighty and one of the

703
00:42:00,599 --> 00:42:03,679
outcomes of this case is that criminal courtrooms in the

704
00:42:03,800 --> 00:42:07,320
United States are open. We actually have a constitutional right

705
00:42:07,719 --> 00:42:11,400
to walk into criminal courtrooms and observe what's happening and

706
00:42:11,440 --> 00:42:14,400
what this means is that if you and I had

707
00:42:14,440 --> 00:42:16,280
all the time and money in the world, and if

708
00:42:16,280 --> 00:42:18,679
we were really well organized, we could get all of

709
00:42:18,679 --> 00:42:21,519
our friends together to go sit in all the courtrooms

710
00:42:21,719 --> 00:42:24,440
and record data, like here's the case that I saw,

711
00:42:24,800 --> 00:42:27,599
and here's what the crime was, and the person was convicted,

712
00:42:27,960 --> 00:42:33,079
and these are some demographics about the individual, and this

713
00:42:33,159 --> 00:42:36,920
is what the sentencing guidelines recommended as their punishment when

714
00:42:36,960 --> 00:42:40,679
they were found guilty, and here's the punishment that the

715
00:42:40,760 --> 00:42:43,239
judge actually gave them, and here's who the judge was.

716
00:42:43,480 --> 00:42:46,320
And theoretically we're allowed to know all of these things,

717
00:42:46,639 --> 00:42:49,239
and we could analyze all that data and figure out,

718
00:42:49,360 --> 00:42:54,639
like what courtrooms specifically are these harsh sentences for racial

719
00:42:54,719 --> 00:42:59,000
minoritize groups coming from. But the government isn't giving us

720
00:42:59,039 --> 00:43:03,719
that information. So I think it's a really poignant example

721
00:43:04,199 --> 00:43:07,760
of the difference between having a rite on paper and

722
00:43:07,800 --> 00:43:11,519
being able to exercise or operationalize that right. And so

723
00:43:11,559 --> 00:43:14,159
the quest that my collaborators and I had to go

724
00:43:14,239 --> 00:43:18,079
on was like figuring out how to reidentify the missing

725
00:43:18,199 --> 00:43:21,599
judge names in all of these hundreds of thousands of

726
00:43:21,599 --> 00:43:24,000
federal sentencing records, and that itself is a bit of

727
00:43:24,000 --> 00:43:26,519
a story. But that's that's one example of a story

728
00:43:26,519 --> 00:43:27,440
that really drew me in.

729
00:43:28,920 --> 00:43:32,079
Speaker 1: Yeah, I mean, oh gosh, there's so much to unpack there.

730
00:43:32,119 --> 00:43:35,400
I mean, do you obviously you wrote about that in

731
00:43:35,440 --> 00:43:42,119
the book. Do you bring these the awareness of these

732
00:43:42,199 --> 00:43:49,599
kind of like deep systemic issues into your curriculum? For example?

733
00:43:49,679 --> 00:43:52,239
Is this something that has really shifted the way that

734
00:43:52,280 --> 00:43:54,480
you speak in public but also teach your students.

735
00:43:55,239 --> 00:44:00,360
Speaker 2: Absolutely so. At my institution, I'm teaching two classes I created,

736
00:44:01,039 --> 00:44:05,400
and one is called Data for Justice, And I'm really

737
00:44:05,480 --> 00:44:10,239
all about like broadening the pathways into doing this kind

738
00:44:10,280 --> 00:44:13,679
of work. You know, math and statistics and computation are

739
00:44:13,800 --> 00:44:19,000
fields that have really systematically excluded racial and gender minorities,

740
00:44:19,079 --> 00:44:21,920
not to mention other groups, and I am about trying

741
00:44:21,960 --> 00:44:24,159
to broaden that pathway. So I teach this Data for

742
00:44:24,320 --> 00:44:28,199
Justice class that's designed for anyone who's never programmed a

743
00:44:28,239 --> 00:44:31,599
computer before and never done statistics before. And I'm really

744
00:44:31,679 --> 00:44:35,679
trying to teach students some fundamental justice sorry, some fundamental

745
00:44:35,760 --> 00:44:39,920
data science skills in a social justice context. And that

746
00:44:39,960 --> 00:44:43,920
includes both technical skills and those sort of like director's track,

747
00:44:44,079 --> 00:44:47,519
like thinking critically about data skills so yes, I bring

748
00:44:47,599 --> 00:44:50,239
these kinds of issues and stories in there, and then

749
00:44:50,239 --> 00:44:53,719
I teach a more advanced course that's Data for Justice

750
00:44:53,760 --> 00:44:57,119
Research Practicum, and all that course is is a semester

751
00:44:57,239 --> 00:45:02,679
long of students doing research projects, original project on various

752
00:45:03,119 --> 00:45:08,360
uh justice issues that they are passionate about. So it's

753
00:45:07,920 --> 00:45:11,679
it's really rewarding to get. I mean, I'm very very

754
00:45:11,719 --> 00:45:14,519
fortunate to work with the amazing students that I that

755
00:45:14,559 --> 00:45:15,760
I get to work with. They're great.

756
00:45:16,360 --> 00:45:18,840
Speaker 1: I love that. So so tell me, you know a

757
00:45:18,880 --> 00:45:21,800
little more about like the book itself. We talked about

758
00:45:21,840 --> 00:45:26,519
the positionality chapter, which uh you know, I discovered when

759
00:45:26,519 --> 00:45:28,199
I was working on my dissertation because I did like

760
00:45:28,239 --> 00:45:32,639
a qualitative like an existential hermeneutic, a sort of phenomena

761
00:45:32,960 --> 00:45:37,039
phenomenological dissertation, and I, i you know, was steeped in

762
00:45:37,159 --> 00:45:41,599
the like philosophy literature of how to write phenomenologically, and

763
00:45:41,639 --> 00:45:46,000
I remember working really hard on a similar chapter that

764
00:45:46,079 --> 00:45:48,920
was sort of like this is where I am coming

765
00:45:48,960 --> 00:45:51,800
from as I am doing this research, and these are

766
00:45:52,039 --> 00:45:54,239
the biases that I'm aware of. You know, I can

767
00:45:54,280 --> 00:45:59,159
only explicate what I can can see, but but I

768
00:45:59,199 --> 00:46:02,800
continue to dig deeper and make those things apparent so

769
00:46:03,039 --> 00:46:07,119
that you, dear reader and other researcher kind of can

770
00:46:07,239 --> 00:46:10,679
understand how I am positioned or situated within all of

771
00:46:10,719 --> 00:46:15,800
this context. So we discussed the positionality chapter, which of

772
00:46:15,840 --> 00:46:17,880
course is right there at the beginning. We talked a

773
00:46:17,920 --> 00:46:22,280
little bit about policing, of course Hitler's shadow, but how

774
00:46:22,320 --> 00:46:26,719
about some of these other aspects of criminal justice, bail, jail, sentencing,

775
00:46:27,039 --> 00:46:31,039
risk scores. You know, did you sort of collect a

776
00:46:31,079 --> 00:46:33,760
whole bunch of interesting stories and then say, how can

777
00:46:33,800 --> 00:46:36,880
I sort of lay these out end to end in

778
00:46:37,159 --> 00:46:41,320
a somewhat followable narrative or did you already have a

779
00:46:41,360 --> 00:46:43,280
structure in play? And then it like, wasn't that hard

780
00:46:43,280 --> 00:46:45,800
to just go, well, there's an example over there. There's

781
00:46:45,840 --> 00:46:48,480
an example over there, because it's literally everywhere.

782
00:46:49,239 --> 00:46:51,840
Speaker 2: Yeah, I didn't have a master plan for the set

783
00:46:51,880 --> 00:46:54,840
of projects that appears in this book. It's a set

784
00:46:54,960 --> 00:47:01,239
of criminal legal topics, and you just name those policing, bail, jail, sentencing,

785
00:47:01,519 --> 00:47:04,840
risk scores, and it's a set of things I worked on.

786
00:47:04,960 --> 00:47:07,480
But as I worked through them over the course of

787
00:47:07,519 --> 00:47:10,480
a number of years, I really saw these same common

788
00:47:10,519 --> 00:47:14,480
themes just merging over and over again, the racial disparities,

789
00:47:14,760 --> 00:47:18,280
the difficulty of getting data, the biases that are built

790
00:47:18,280 --> 00:47:20,840
into the data that we do have, all these kinds

791
00:47:20,880 --> 00:47:24,400
of things, and I just thought, like, there's a story

792
00:47:24,480 --> 00:47:27,679
here that needs to be told. And part of the

793
00:47:27,719 --> 00:47:30,960
way I've tried to weave these things and tell this

794
00:47:31,079 --> 00:47:33,760
story is this is a story of hope. So I

795
00:47:33,800 --> 00:47:39,840
think that these days, if if people in the US

796
00:47:39,960 --> 00:47:44,920
public like know about anything at the intersection of technical

797
00:47:45,000 --> 00:47:48,760
fields and social justice, it's like very likely to be

798
00:47:48,840 --> 00:47:53,360
something related to like the harms that algorithms can cause, right,

799
00:47:53,480 --> 00:47:57,079
like those ways that were surveiled, or like the ways

800
00:47:57,159 --> 00:48:01,199
that generative AI is biased and causing us problems, like

801
00:48:01,239 --> 00:48:03,480
these sorts of things, and these things are all true

802
00:48:03,480 --> 00:48:07,239
and extremely serious. What I wanted to show with these

803
00:48:07,280 --> 00:48:11,880
stories is the uh, the optimistic flip side of this,

804
00:48:12,400 --> 00:48:16,000
which is that when used carefully and in the right way,

805
00:48:16,719 --> 00:48:21,079
data science can be a powerful tool for justice. And

806
00:48:21,119 --> 00:48:23,159
this is really I have to give credit in the

807
00:48:23,239 --> 00:48:27,000
spirit of my hero, who's like the civil rights icon

808
00:48:27,119 --> 00:48:29,639
ID B. Wells, who said that the way to right

809
00:48:29,719 --> 00:48:32,320
wrongs is to shine the light of truth upon them.

810
00:48:32,440 --> 00:48:34,719
And so that's really what I'm after, is like data

811
00:48:34,760 --> 00:48:36,840
as a form of that light. So yeah, I had

812
00:48:36,840 --> 00:48:39,559
no master plan. It was like things I worked on

813
00:48:40,000 --> 00:48:42,119
and the common themes emerged as I did it.

814
00:48:42,719 --> 00:48:45,679
Speaker 1: Oh I love that. Yeah, And so how did you

815
00:48:45,760 --> 00:48:48,880
sort of approach the audience? You know, as as an

816
00:48:48,880 --> 00:48:52,199
individual who has been in academia for quite some time,

817
00:48:52,519 --> 00:48:55,079
you know, I often will talk to folks about that

818
00:48:55,320 --> 00:49:00,159
difference between writing in academic publication. I mean, other than

819
00:49:00,199 --> 00:49:02,639
the obvious difference, which is that one makes no money

820
00:49:02,760 --> 00:49:05,280
and the other one sometimes it makes money if it

821
00:49:05,320 --> 00:49:08,639
doesn't a way, but the difference are doing an academic

822
00:49:08,639 --> 00:49:12,760
publication and a trade publication. Writing for a more general audience. Obviously,

823
00:49:12,800 --> 00:49:15,679
I think all of our best professors already do that

824
00:49:15,719 --> 00:49:20,880
when they focus and put effort into their teaching, because

825
00:49:21,639 --> 00:49:25,719
undergraduates are a more general audience. And then of course

826
00:49:25,760 --> 00:49:28,199
as they get more and more specialized, you can become

827
00:49:28,239 --> 00:49:32,559
more specialized and specific in your language. So what was

828
00:49:32,559 --> 00:49:36,599
that process like for you? Taking these concepts and writing

829
00:49:36,639 --> 00:49:39,199
them in a way that is digestible.

830
00:49:40,760 --> 00:49:45,280
Speaker 2: It was a challenging and extremely fulfilling experience. So look,

831
00:49:45,440 --> 00:49:49,280
I am, as we have said, at a first and

832
00:49:49,360 --> 00:49:53,119
foremost teaching focused institution, I'm at a small Loberal Arts college.

833
00:49:53,320 --> 00:49:58,320
I love teaching. I love helping people understand new concepts,

834
00:49:58,440 --> 00:50:04,800
understand complicated cons and so this book is in that spirit.

835
00:50:04,920 --> 00:50:08,960
It's it's an effort to do that. I have always

836
00:50:09,199 --> 00:50:13,519
enjoyed public speaking, right, telling the public stories in ways

837
00:50:13,519 --> 00:50:17,000
that they can understand. I have enjoyed writing op eds,

838
00:50:17,039 --> 00:50:19,159
which is a different form of public writing. But again,

839
00:50:19,199 --> 00:50:20,960
it can't be technical, right, you want to get a

840
00:50:21,000 --> 00:50:23,599
message to people, like you can be nerdy, and I

841
00:50:23,599 --> 00:50:25,800
love being nerdy, but you can be nerdy in an

842
00:50:25,840 --> 00:50:28,360
accessible way. And that that's what I've been after, and

843
00:50:28,400 --> 00:50:30,360
it was the same thing with this book. Now that

844
00:50:30,480 --> 00:50:33,880
is not to say that I did it perfectly from

845
00:50:33,920 --> 00:50:36,400
the start. I most assurably did not. And this is

846
00:50:36,440 --> 00:50:40,480
the magic of wonderful editors, right right, So I'm drafted

847
00:50:40,519 --> 00:50:45,360
this book thinking like perhaps a little bit with a

848
00:50:45,360 --> 00:50:48,960
little bit of hubris, like, Okay, I've like told these

849
00:50:48,960 --> 00:50:51,199
stories in such plain language, this is going to be

850
00:50:51,239 --> 00:50:56,079
so understandable. And I hired this wonderful like presubmission freelance

851
00:50:56,239 --> 00:51:00,880
editor Nancy Rollinson, who's just brilliant, and she was like, Chad, like,

852
00:51:00,920 --> 00:51:03,119
I think perhaps this is not as accessible as you

853
00:51:03,199 --> 00:51:07,920
think it is. And so so Nancy, to her credit,

854
00:51:08,239 --> 00:51:11,440
like gave me pushed on me really hard in places

855
00:51:11,599 --> 00:51:14,920
where like, yes, I realize you think that you are

856
00:51:14,960 --> 00:51:18,119
teaching this concept clearly, but like you need to do

857
00:51:18,280 --> 00:51:21,400
better here. And then I have an editor at my

858
00:51:21,679 --> 00:51:25,840
publisher at Christian University Press. My editor, Diana Galuley, did

859
00:51:25,880 --> 00:51:29,239
an equally amazing job pushing on me in those places.

860
00:51:29,559 --> 00:51:31,559
So I think if I were to sit down and

861
00:51:31,559 --> 00:51:34,360
write this book again from scratch, I would understand better

862
00:51:34,400 --> 00:51:36,800
and I would be able to do it better from scratch.

863
00:51:37,079 --> 00:51:39,800
And so I'm always grateful when anything is a learning

864
00:51:39,840 --> 00:51:42,400
experience for me as well. Right, Like, I hope the

865
00:51:42,440 --> 00:51:45,000
book teaches the public something, but it taught me something.

866
00:51:44,760 --> 00:51:47,400
Speaker 1: To absolutely do you think that it's something you will

867
00:51:47,519 --> 00:51:50,519
endeavor to do again. It's you know, I talk to

868
00:51:50,639 --> 00:51:52,800
a lot of writers who and I think I'm sort

869
00:51:52,800 --> 00:51:55,480
of in that boat, like I've written a lot academically,

870
00:51:55,880 --> 00:51:58,280
but I am I just like I'm not rich. I

871
00:51:58,320 --> 00:52:02,159
keep telling my editor I'm not ready, Like sitting down

872
00:52:02,199 --> 00:52:04,039
to write a whole book is like the most daunting

873
00:52:04,119 --> 00:52:07,119
thing in the world. But then when I when I

874
00:52:07,159 --> 00:52:08,639
talk to folks who are like, I did it. I

875
00:52:08,679 --> 00:52:10,480
did the book. Then they're like, oh, I'm gonna do

876
00:52:10,519 --> 00:52:14,400
another one. Oh that's crazy. Do you do you feel

877
00:52:14,440 --> 00:52:16,840
like you've gotten bit by the bug? Or are you like, yeah,

878
00:52:17,000 --> 00:52:18,760
we're gonna put that on the shelf for a while.

879
00:52:18,800 --> 00:52:19,559
I need a break.

880
00:52:20,079 --> 00:52:22,280
Speaker 2: Okay, this is not a promise to you or to

881
00:52:22,360 --> 00:52:25,519
anyone listening, but I am probably I am probably in

882
00:52:25,599 --> 00:52:27,920
the bit by the bug camp. I love it, and

883
00:52:28,039 --> 00:52:31,119
it is part of this is because I am someone

884
00:52:31,239 --> 00:52:33,880
who could never decide what they wanted to be when

885
00:52:33,880 --> 00:52:36,400
they grew up. And apparently I'm still not grown up

886
00:52:36,440 --> 00:52:39,239
and I still don't know. And like, yes, I love

887
00:52:39,320 --> 00:52:42,280
doing math and I love doing data science, but I

888
00:52:42,360 --> 00:52:44,880
love communicating and I love writing just as much, and

889
00:52:44,920 --> 00:52:48,400
I love twenty other things just as much so. And

890
00:52:48,440 --> 00:52:49,960
there was a point in my wife where I thought

891
00:52:49,960 --> 00:52:52,039
I wanted to be a journalist as sort of my

892
00:52:52,039 --> 00:52:57,079
my profession. So I really, I really do enjoy the

893
00:52:57,159 --> 00:53:01,079
process of writing. I have some idea is for what

894
00:53:01,559 --> 00:53:03,639
a next book might look like, but I haven't settled

895
00:53:03,880 --> 00:53:06,119
on any of them viata and it's not developed yet,

896
00:53:06,119 --> 00:53:08,559
but I think I would like to do more.

897
00:53:08,679 --> 00:53:11,039
Speaker 1: Yes, oh, well, I won't hold you to it, but

898
00:53:11,079 --> 00:53:14,599
I am looking forward. So on that note, Chad, let

899
00:53:14,639 --> 00:53:16,440
me know, you know, we're kind of coming up to

900
00:53:16,480 --> 00:53:19,079
the close of the hour. Is there anything that you

901
00:53:19,119 --> 00:53:21,719
can think of that I mean, obviously there's so much

902
00:53:21,760 --> 00:53:24,079
we didn't touch on, but if there's any is there

903
00:53:24,119 --> 00:53:25,760
anything you can think of where you're like, I can't

904
00:53:25,760 --> 00:53:27,880
believe we got to talk about this one thing, like

905
00:53:28,280 --> 00:53:32,000
anything that you feel remiss in not bringing up before

906
00:53:32,559 --> 00:53:33,280
we end the hour?

907
00:53:34,440 --> 00:53:36,880
Speaker 2: Thank you for asking. I will say yes, and I'll

908
00:53:36,880 --> 00:53:40,639
make it brief. I really, as I said, wanted this

909
00:53:40,679 --> 00:53:43,079
book to be a book of hope. And one thing

910
00:53:43,119 --> 00:53:44,960
I tried to do at the end of each chapter

911
00:53:45,440 --> 00:53:48,400
is for any reader, even people who think they do

912
00:53:48,440 --> 00:53:50,880
not love numbers and do not love data, this book

913
00:53:50,920 --> 00:53:53,719
is still for them, and I try to give ways

914
00:53:53,960 --> 00:53:57,880
to help anyone highlight the importance of how data can

915
00:53:57,920 --> 00:54:01,079
be used for justice. And in the last chapter of

916
00:54:01,119 --> 00:54:03,519
the book, I talk about I don't know if you know,

917
00:54:03,519 --> 00:54:05,880
Rebecca Solm, that's book Hope in the Dark, which is

918
00:54:05,920 --> 00:54:11,719
like a wonderful treatise or meditation on like how we

919
00:54:11,800 --> 00:54:14,719
might think about hope and how part of Hope, Openness

920
00:54:14,719 --> 00:54:15,519
to the Unknown.

921
00:54:16,280 --> 00:54:18,239
Speaker 1: I need to read that because I've only read Men

922
00:54:18,360 --> 00:54:23,320
Explain Things to Me by So yeah, I've definitely read

923
00:54:23,360 --> 00:54:25,480
her Man Explaining book, but I should read.

924
00:54:25,320 --> 00:54:28,800
Speaker 2: This also an important book. And I guess look, we

925
00:54:28,840 --> 00:54:31,760
are we are. We have always had problems in this country,

926
00:54:31,920 --> 00:54:33,800
but we have a lot of problems right now. It

927
00:54:33,880 --> 00:54:37,320
is a very trying time. I think people wake up

928
00:54:37,480 --> 00:54:41,559
and more than ever feel the weight of reality on them,

929
00:54:42,079 --> 00:54:45,920
and so I have one of the one of the

930
00:54:45,960 --> 00:54:48,519
most important reasons I wrote this book was to try

931
00:54:48,519 --> 00:54:51,239
to give people a little bit more of a sense

932
00:54:51,239 --> 00:54:54,360
of agency in ways that they may not have felt before,

933
00:54:54,599 --> 00:54:56,159
and to provide them with a little bit of help.

934
00:54:56,800 --> 00:54:59,480
Speaker 1: Yeah. Oh, I love that so much. Everybody. The book

935
00:54:59,519 --> 00:55:03,079
is unlike Talking Justice. The Power of Data to confront

936
00:55:03,159 --> 00:55:07,719
inequity and create change by doctor Chad M. Topaz. Chad,

937
00:55:07,960 --> 00:55:09,840
thank you so much for being here with us today.

938
00:55:10,559 --> 00:55:13,360
Speaker 2: Thank you for having me. It's really been an honor

939
00:55:13,800 --> 00:55:16,079
to meet you and talk with you and your listeners.

940
00:55:16,519 --> 00:55:20,360
Speaker 1: And everybody listening. Thank you for coming back week after week.

941
00:55:20,519 --> 00:55:22,960
I'm really looking forward to the next time we all

942
00:55:23,000 --> 00:55:25,760
get together to talk. Ridy, thank you