Unlocking Justice

Carrie and Megan talk with Dr Chad M Topaz, professor of complex systems at Williams College and cofounder of the QSIDE Institute, about his book Unlocking Justice: The Power of Data to Confront Inequity and Create Change.

• Yoda and BSL

Transcript

Carrie Gillon: Hi, and welcome to the Vocal Fries Podcast, the podcast about linguistic discrimination.

Megan Figueroa: I'm Megan Figueroa.

Carrie: And I'm Carrie Gillon.

Megan: It is June. 

Carrie: It is June, or almost June. It's June when this drops. 

Megan: Yeah, it's wild. 

Carrie: Yeah, the years are slipping by. 

Megan: Slipping by. Years are slipping by because next month is our ninth anniversary. 

Carrie: Oh, that's right. Yeah.

Megan: Yeah. It's wild.

Carrie: Oh my goodness.

Megan: So wild.

Carrie: I almost cannot believe it's been that long. When I think back to that time, it doesn't feel like it was that long ago, but then also it feels like it's forever ago. 

Megan: Right. Yeah. Because it was pre-COVID. There's a hard line there where it's pre-COVID and after, so it's wild. 

Carrie: Yeah, the before times.

Megan: The before times.

Carrie: Oh boy. Oh boy, boy. So I thought we should talk a little bit about this, the conversation piece that I saw on Bluesky by Victoria Elliot Bush from Queen Mary University of London. Why talking like Yoda can help you to master British Sign Language?

Megan: I love it. I don't know where this is going at all. I have no idea. I actually have a little guess of subject, object, verb or subject verb object.

Carrie: Yeah, that is what it's about. So the word order of Yoda, generally speaking, I guess is the same word order [crosstalk]

Megan: As a British Sign?

Carrie: As a British Sign, yeah. So we have things like, "Patience you must have, my young Padawan." So that's like, your path you must decide. English, generally, would be, you must have patience, my young Padawan, and you must decide your path. She says that Yoda's sentences have topics first. So patience you must have, patience would be the topic, I guess. Then your path you must decide, your path would be the topic. So this is topicalization. So when we do this in English, we can topicalize and put something in the front. So for example, "your path, you must decide" sounds a little bit more normal to me as an English speaker, where we're topicalizing your path. Patience, you must have sounded very weird outside of Yoda's speech. Yeah.

Megan: Do you have any examples of where we do topicalization in English? I'm trying to think of a topicalization. Would it be something like, "Dogs, they're just too good for this world", something like that?

Carrie: I don't know. Does that count as topicalization?

Megan: I don't know. I'm trying to....

Carrie: Here's my example. We were having this conversation about foods that people avoid. I made the point that out of all the options this person had said, "Well, gluten is a little bit different because some people legitimately cannot eat it, like legitimately. Some people are avoiding it for reasons that may or may not be legitimate, but there's certainly at least a subset where it's 100% legitimate, celiac, right?

Megan: Yeah,

Carrie: Then someone said, "Oh no, but some people need to limit the protein", and things like that. I'm like, "Limit maybe, but gluten, celiac need to avoid entirely." So gluten is topicalized there. 

Megan: Yeah, that makes a lot of sense and I'm looking at examples on just the Wikipedia page of topicalization. It's like "The boys roll rocks for entertainment" versus "For entertainment, the boys roll rocks."

Carrie: It's interesting. I guess that is topicalization. 

Megan: Yeah, yeah, sure. Yeah. What about "I won't eat that pizza" versus "That pizza, I won't eat"? Or "I am terrified of those dogs", "Those dogs, I'm terrified of." Yeah.

Carrie: It's pretty uncommon in English, but we can do it. We can do these examples that you have,
 the one I gave, but normally we don't. Normally we just have subject, verb, object, pretty straightforward. "Anna ate a biscuit." But if for some reason I wanted to make the biscuit more important, I could say, "The biscuit was eaten by Anna", so I could passivize but I could also say, "A biscuit Anna ate." It's just a little weirder. 

Megan: Right. It's weird for me. Yeah. 

Carrie: Yeah. In speech, you can do it. It's just that the context is to be right for it. Yeah, I'm not doing it properly. But you can, it's just harder. Whereas, "The biscuit was eaten by Anna", we just topicalize by passivation really easily. If we want to make Anna the topic, because if it's just the subject, "Anna ate the biscuit", it's not really doing anything interesting, we have to do something using a cleft, "It was Anna, who ate the biscuit." 

Megan: Yeah, to really highlight the topicalization of Anna.

Carrie: Yeah, she's the topic of the conversation. 

Megan: So in English when we topicalize, we have a bunch of different strategies. Yoda only really has one. You just front the topic to the beginning of the sentence. 

Carrie: "So patience you must have, my young Padawan." But it would be more natural for most English speakers to say, "Patience is what you must have, my young Padawan." So this person points out that BSL is often misconceived as having the same grammar as English, that's not true, it has its own syntax. It's not a branch of English, it's a separate language and it's also separate from other sign languages, which we've mentioned before. BSL is topic, comment, language. So it has to have the topic first and then everything else after. 

Megan: I wonder, is there a specific sign? Do you have to sign or do something to note that this is a topic for words?

Carrie: It's just first. 

Megan: It's just first? Okay. 

Carrie: So, like Yoda, it's the first thing, that's your topic. So if you're going to just sign the equivalent of Anna eats a biscuit, it would be the sign for Anna, the sign for eat, and then the sign for the biscuit. In English, that would be, ‘It was Anna who ate the biscuit.’ That's the most natural translation of what's actually being said. 

Megan: Yes. Okay.

Carrie: If you signed eat, biscuit, Anna, then the most natural English translation would be, what Anna did was eat the biscuit? 

Megan: Also, I can totally tell this was written by a British person. 

Carrie: Oh, it was a biscuit?

Megan: Yeah.

Carrie: Yeah, I didn't even think about it, but yes. So yeah, for the Americans out there, translate to cookie. 

Megan: Yeah.

Carrie: I just automatically translated because I knew it was a British person. Then finally, if you sign 'biscuit, Anna eat', the most natural English translation would be 'It was a biscuit that Anna ate.' So yeah, BSL and Yoda have something in common. 

Megan: Yeah, that's so cute. I just saw the man at Lauren and Grogu and I'm like, when is Grogu going to speak? I know he's still a baby. 

Carrie: Well, still not speaking or do you not want to give it away? 

Megan: Who knows?

Carrie: Fair enough. 

Megan: I don't want to give anything away.

Carrie: Turns out there is actually a topic marker. It doesn't seem to be mandatory, but they can be marked with so-called non-manual features. So a head nod or widen eyes during the topic sign, or a pause after signing the topic. 

Megan: Okay. 

Carrie: Yeah, that makes sense. 

Megan: Yeah, totally. Oh, it's just so cool. I'm just thinking about babies learning BSL and just all these things. Oh my God, it's just a miracle of nature that we learn to communicate. It's so cool. 

Carrie: Yeah, and not just that we can learn to communicate, we learn to communicate with such complicated languages.

Megan: Yeah. Like, to learn the reality that you can pause after the topic to emphasize in BSL, it's just so cool. These things, we learn them. 

Carrie: But she gives advice for anyone who's trying to learn BSL as a second language. Stop thinking in terms of word order. Think like Yoda. Talk like Yoda. Sign like Yoda. Quote, start with what is the most important part of the sentence and then provide details. 

Megan: Really?

Carrie: As I'm sure the wise Yoda would agree, sign language learn you should. 

Megan: Oh...

Carrie: That's so awesome.

Megan: I love it. Oh my gosh. It's so funny because when I know that it's like Yoda speaking, it sounds more natural to me where it's like, "Oh, yeah, that's not Yoda, obviously. How iconic, right? How iconic? 

Carrie: Yeah, oh, totally iconic and it's interesting. I think I've always misunderstood what was actually going on with Yoda's speech, and now I get it. It's a topic comment. 

Megan: Yeah, I've never thought of it. 

Carrie: I was thinking too English-ly.

Megan: Yeah, a crime we all commit, us English speakers. 

Carrie: Yeah, it's not our fault that our brains work like that, but anyway. 

Megan: Love it.

Carrie: Anyway, today's episode is a banger. 

Megan: It's a banger. Chad, I love you. It was so much fun. New best friend.

Carrie: It was a lot of fun. Yes, our new BFF.

Megan: Yeah, book out tomorrow. Get that book.

Carrie: Oh, we're dropping it on the exact same day. Look at us. 

Megan: Yeah. Well, we're dropping it on Monday. The book is out Tuesday.

Carrie: Oh, okay. Alright. One day early. Okay. 

Megan: Get that book.

Carrie: Yes, definitely do and enjoy.

Megan: Enjoy.

Voice Over: This month, we would like to thank Beth Clark for becoming our newest patron. Thank you so much. Every little bit helps us create this show and if anyone else would like to join, it's at www.patreon.com/vocalfriespod where you can get bonus episodes, stickers, and mugs. Thanks! 

Carrie: So today we're very excited to have Dr. Chad M. Topaz, who is a professor of complex systems at Williams College and co-founder of the QSIDE Institute, which uses data science to promote equity and justice. An award-winning educator and researcher recognized by the National Academy for the Sciences, the Association for Women in Mathematics, and the Society for Industrial and Applied Mathematics. He has written numerous studies at the intersection of data science, social justice, and public policy. His opinion pieces have appeared in the Chicago Tribune, the New York Daily News, the Philadelphia Inquirer, Inside Higher Ed, and other publications. He's also the author of Unlocking Justice, the Power of Data to Confront Inequity and Create Change. So welcome.

Chad M. Topaz: Thank you so much. 

Carrie: Thanks for being here. Yeah, I was very excited. First of all, it is very beautiful. I love the cover. 

Megan: It's amazing. 

Chad: Thank you. Credit for that goes to the good people at my publisher who have way better aesthetic sense than I do.

Megan: Yes, they often do, at least better than me as well. It's a very readable book.

Carrie: So readable. 

Megan: Yeah. I always love it when we can talk to an author who's read a book that's very readable. 

Carrie: And recommend it, right? To be like, "Listeners, this is actually a book that is consumable, but just a beautiful book." Thank you for writing about it.

Chad: Thank you for saying that. A big part of my life is as a teacher and I undertook this as  a teaching endeavor and I really wanted to try to bring the ideas to lots of people. So you just made my day. Thank you. 

Megan: Yay. Yeah. That's our goal too with all of this stuff. We just really want people to be able to engage with stuff that is normally behind some sort of psychological paywall, you know?

Chad: Right. Yeah. 

Megan: Why did you want to write this book and why now?

Chad: Wonderful question that has so many layers of answer. I want to give you some of those layers. So there's sort of a political impetus for writing the book, which is I feel like we're living in an era of backlash. We're having bans on the 1619 project. We're having rollbacks of DEI. We're having, "Don't say gay." We're having the expulsion of immigrants. We're having the increasing numbers of murders of people by ICE and the police. We have disappearing of reproductive. There's just so much and I feel that data is being hidden or distorted in exactly the places that we need it most to address some of these things. I guess there's also a personal pivot that I experienced that led me to write this book. 

So my training is as an applied mathematician, and that means I'm someone who is trained to use math to solve problems in other fields. I spent the first couple of decades of my career studying things like fluid physics or biology. Like, "Why are those locusts making that locust swarm?" Problems like this but I also always had this political side of me and this activist side of me and eventually I did some projects that brought the quantitative side of me and the activist side of me closer together. I realized, "Oh, this can be a thing for me." I co-founded QSIDE Institute with my husband and that also led me to write this. 

This will be the last thing I'll say that I think it's really important to recognize that the idea of bringing together data science and social justice is not new. This is not my idea. This is an idea that has been around for a long time, going back at least to heroes like W.B. Du Bois and my personal favorite Ida B. Wells, my greatest hero of all time, who said, "The way to right wrongs is to turn the light of truth upon them". So they see this book and of the work I do as like data science is one of many forms of that light of truth, but maybe a historically underutilized one.

Megan: That's beautiful. 

Carrie: Oh, 100%, yes.

Megan: Yes, and that leads perfectly into our next question, which is what is data and data science and what is social justice? 

Chad: Well, it's lovely being asked that and it's always additionally scary to answer because I will answer and someone will have a very different idea about the proper answers to these questions. So leaving room for all of those things, I think the most straightforward way I can say it is that data is just raw information. I think most often people think of data as being numbers, but data could be names, data could be dates, data could be pictures, data could be a satellite image, data could be a musical recording. It's just raw information and what data science is really about is bringing that information together with tools from mathematics, statistics, computer science, and other quantitative fields in order to solve real problems. I think social justice for me is just about breaking down walls so that everyone has equal access to the same resources, the same opportunities, the same rights.

Carrie: I love that so much and I was thinking when you said that raw data could be numbers and also be names. I have so many times on Bluesky had to remind people that the number 272 may not be biased, but data is always.

Chad: Always. Thank you for saying that. This is again, the way I've described this book is it sort of has two streams and one stream is how do we use data science to promote social justice stream? But then there's what I like to call the directors track, like, the way when you watch a DVD or whatever or Netflix, there could be the directors commentary. The director's commentary is like we also have to think about all of the ways in which data itself is inherently biased, inherently shaped by social forces, by the people who collect and work with the data, all of those things. Absolutely. So you will also get a lot of that in the book.

Megan: Which is directly related to what QuantCrit is. 

Chad: Oh, yes. So, QuantCrit is basically a data e numbers, e version of critical race theory. So one of the ways I describe QuantCrit in the book is to say, "QuantCrit is like a panoramic shot that zooms out and makes you think about all of the forces that have shaped data." To make this really concrete, there's a chapter in the book that's about what happened at Rikers Island in the early days of COVID. One of the bonkers things that came out of my research into that was discovering how they keep track of racial statistics [crosstalk]

Carrie: Yes, so bizarre.

Chad: ....in New York's public data. I don't want to spoil for readers what New York City thinks the three races are, but it might blow your mind. 

Carrie: It will. It definitely blew my mind. I was like, "What?"

Chad: Yeah. This is why I include a screenshot because no one believes me unless I show them the receipts. Yeah. 

Carrie: Yes. I guess this also leads us to the next question, which is why does data matter?

Chad: Yeah, one of the things I talk about in this book is the idea of humility, and I think it's really important to say that just because we have data on something, that does not magically fix problems. Publishing research does not magically fix problems. Handing people a pie chart does not magically fix problems. But I do think concretely, the first thing data can do is help us discover the exact locus. It can help us pinpoint exactly where injustice is happening. So let me again, give you an example. It is very well documented both by independent researchers and by the government itself that federal criminal sentencing is horrifically racially biased. No one who believes research disputes this but we only know this result in aggregate. 

So you can ask yourself, "Okay, where do criminal sentences come from?" They come from judges. So to me, the natural question is "Who are the judges giving racially disparate sentences?" It turns out that is extremely hard to know because of the way our judicial branch obfuscates the data. What can you do once you have that data? Well, it turns out federal judges have lifetime appointments. There's not a lot we can do about that. But one thing we could do is inform our lawmakers and encourage them not to elevate judges to higher judicial offices. To appellate courts and the Supreme Court, if those judges have sort of disturbing records when it comes to race and sentencing. 

A second thing we can do is we can really just educate those judges themselves. So when I did research on federal judicial sentences and started publishing it, one of the best things that's happened to me in my entire career was a couple of federal judges heard about this research and they wrote me and they said, "Hey, could you actually send me my personal statistics on race and sentencing in my courtroom? Because I've actually been asking my own court system for this analysis and I can't get it. I would love to know if I am showing some unintended bias in my sentencing decisions." 

This is why I tried to write a hopeful book and I think for me that's one of the most hopeful moments. It's like, "Oh, there are people out there, we all have bias, but there are people out there who would like to do better." Just to give one last example, when it comes to judges and sentencing, no federal judges are not elected, but in most state court systems, at some point, judges have to stand for election. If that's the case, when that's the case, if there's data about those judges, you can take that information right to voters at the ballot box and say, "You need to think about criminal sentencing and race when you cast your votes for judges."

Carrie: Yeah, that's so great and I'm happy to know there's at least some judges out there who are trying to be better people because that's the best that we can help for actually is that people do try to change themselves.

Chad: Absolutely. Yes. 

Megan: I really appreciate that you are giving actionable advice here. Like, what can we do in our realm of influence. Carrie and I have been discussing this while writing our own book. It's like, we want systems level change, but where are we positioned to make change at this point in our lives? Yeah. 

Chad: Right. So I don't know if I'm allowed to ask you questions, but now I want to know everything about your book. But also, let me just say that for me, part of this, I talk about this in the last chapter in the very closing of my book is Rebecca Solnit's idea of hope from Hope in the Dark. That  for me, there's not going to be some magic wand that magically fixes racism. That hope is just like leaving room for small victories that matter. That's the way that I tried to approach this book. Now tell me everything about your book. 

Carrie: I know. Oh my God. Okay, so our book is basically the conceit of our podcast. So linguistic discrimination exists and it's bad and you should stop. As an individual, you should stop. It's obviously a bigger problem. It is a systemic problem but we as individuals can only do so much. So we're speaking to our readers and our listeners, "Here's the small things that we can each do."

Megan: Yes, and we come at it with humility, with so much humility, saying that we are still learning. We have made these mistakes. 

Chad: Absolutely. Do you have one tip for me? Like, "Chad, here's a thing you could stop doing"?

Carrie: Well, I don't know. 

Megan: You have probably thought a lot about these things because you're in QuantCrit. 

Chad: You know what? I don't know what counts as linguistic discrimination because I'm not educated on this topic. I'm like an autistic person who will be honest when they don't know. So I have no idea. But I'm thinking about a moment in my teaching career when I was soon after my PhD teaching at UCLA and a student came up to me afterwards and very kindly said to me, "Please, stop addressing the class as "You guys". I was like, "I had never thought about that before. Wow, this was a learning moment for me. Thank you for telling me about that."

Megan: Yeah. So there are two major kinds of linguistic discrimination. We focus more on the other kinds. So the one you're talking about is like the way that we ourselves use language, but the other way is how we judge people. So most of what we talk about is how we judge people. So, I don't know, do you judge people for using vocal fry? Do you judge people for using African American vernacular English or whatever? My guess is probably not most of you know it if you're aware of it. But some things you might not be aware of yet and I just don't know where those are. 

Carrie: So this might actually be a really good example based on how in the criminal legal system and how the workings of that are. So an example, linguistic discrimination would be when prosecutors or defense attorneys are selecting juries, they will often not want people that use these features of African American English. So basically it's like racial discrimination, but they're using the language. So that's why it's so hard for us. It's like, these are system level problems. This is racism, but this is how it's coming out linguistically. 

Megan: Or they don't want people who can speak Spanish on the jury because then they'll understand the witness directly rather than listening to the translator, or sorry, the interpreter.

Carrie: Yeah. So it's everywhere, and that's why it's hard to answer your question.

Chad: Wow. Well, I can't wait to read it and I don't have a podcast. But if I did, I would totally have you on it. 

Megan: I love it. Thank you. But you were right in the middle of this conversation, Chad, when you wrote for the Philadelphia Inquirer, political language Hispanics are a distraction or worse. Can we talk a little bit about that one?

Chad: We can talk about that one. That one drove me crazy. So where do we start?

Megan: Yeah, so basically what is the problem that people perceive there is around this language?

Chad: Sure. So there was a piece, and now I have to think back in my head. I feel like this was in the late summer or very early fall. A group wrote an op-ed or wrote a piece that was basically like the problem with the left is we have to stop using all of these woke words was basically the conceit of this. Of course, I am in the place online that you mentioned where all the pink Okamis hang out on Bluesky where we get told how bad we are for having abandoned Twitter. 

Carrie: It happened again today.

Chad: Yes, indeed it did. This is like code red; I cannot even with this. Because there is a ton of research showing examples of how language, choice, and tempering this progressive language doesn't matter. This particularly comes from political campaigns, where there are studies that show these languaging nuances are not the thing that sways people. However, research also shows that what this languaging does is it can actually create more inclusion and acceptance for people. So there's a ton of upside in using this language. There's no downside. The research seems to be in thundering consensus about this point. So I just wrote this op-ed where I tried to do the thing I do, which is to bring some of this published research to people in an understandable way and call out something that I thought was just a really misleading stance for an organization to take. 

Megan: Yeah. So Chad, you are doing exactly what we do, try to do every day. You were combating the way that language is used to discriminate. I love it. So yeah. 

Carrie: It also drives me crazy too, because I've heard this argument over and over again of, "Well, if we say things in the exact right way, then the right wingers cannot attack us. I'm like, "No, they'll just make something up. There is no winning with them. So just ignore them." That's my opinion. Not ignore them in the sense of like don't pay attention to them at all. I just mean don't take any of their criticism seriously. 

Chad: Carrie, yeah, I'm getting double thumbs up. So one of my most maddening traits to people is my refusal to engage in debate with ideological opponents who don't actually want to listen. I spent a long time earlier in my life engaging in those debates. I finally had this realization, "What am I doing?"

Carrie: Exactly.

Chad: Like me, trying to educate people who purposefully, willfully, don't want to know is not actually a good use of my time. I should spend my time on actually making things better, not convincing some right-wingers who have made up their mind. These people find this maddening. I don't know. 

Carrie: Yeah, that's no point. You do want to have a conversation with people who might care at some point. 

Chad: Yes.

Carrie: But people who are completely closed off, there is no point. 

Chad: That's right.

Megan: Yeah. Also thinking, Andy Beshear, actually, on Bluesky, people were sharing how Andy Beshear had said something about justice involving individuals, that terminology. I just remember being so upset because everyone in the comments was, "No one uses that. Liberals aren't saying that." I was like, "I use that language because that's straight from boots on the ground. That's how  we call people that are involved in the justice system. If you're in those spaces with them." So it was like, first I was like, "Oh, I like Andy Beshear. What's he doing here? He's falling into the same trap as so many liberals are, people on the left are. Also, who are these people that have just, they're just not engaging in the same spaces? I don't know but I love that you have [crosstalk]

Carrie: They're telling on themselves. 

Megan: They're telling on themselves. That's exactly what it is, Carrie. Yep, but I love that you brought data into it, Chad. I loved it.

Chad: Thank you. Hearing this conversation, it sort of reminds me of a journey that I had to go on when writing this book because, okay, so in chapter 2 of the book, I write about my positionality and I am like a cis upper middle class white dude who had a super privileged upbringing who has never himself been anywhere near the criminal legal system, all this kind of thing. When I started doing work on incarceration, on policing, on sentencing, I was using words like 'convict, convicts', the defendant and all these sorts of things. People, to their credit, when I would write papers and submit them to journals, I would get back reviews on my papers that were like, "Okay, this is good work, but you really need to refine your language and use more personal first justice first language." I was like, that was also another learning moment for me that really required some humility for me to be like, "Okay, I didn't know I was doing this wrong. I'm going to do better now." 

Carrie: Speaking of that chapter, I actually had to stop reading it because you had a second encounter with a mass shooting or whatever. I was like, "How is this real life?" They're about the same age as me too and I'm like, I did not have this experience because I'm Canadian. It's like, "Ahh." 

Chad: Congratulations on being Canadian. It's awesome. 

Carrie: It's all luck. 

Chad: Yes, it's very bizarre that I grew up in a super white, super rich, super generally shielded suburb north of Chicago, Illinois. Also when I was in school, we had a mass shooting and then shortly thereafter, a murder of some residents by a guy that I took math class with at school. So it was just a bizarre part of my childhood. Yeah. 

Carrie: Yeah, like you say, you had such a pretty much upbringing and yet there's already so much violence and I was, "This is horrifying." So how much worse is it for other people? I did live in the States for a long time, but I still just haven't had that experience. I can't answer the next day, but... I was like, "What?"

Megan: Yeah. So we have more questions, but I thought, we're in this space right now where I thought it'd be cool to just, if you could help me walk through something that I found really annoying about how data was collected that got regurgitated. Because you bring up this idea of illusory truth effect. So can you explain what that is first? 

Chad: Sure. Illusory truth effect is the idea that by talking about something, you further amplify that thing, even if that thing is wrong.

Megan: Yeah. So as I was reading your book, I was just like, first, thank you for using Latine. I really appreciated seeing that. Also, I've been dealing with this because I am Mexican-American. So  I've had interviews where I've talked about Latinx and Latine and the pushback against it. One of the things that people pointing to is Pew research that came out in 2020, that said "Only 3% of Latino people use Latinx." So if you hear that, I wonder how you, as someone who is a data scientist, would approach this. What are your questions when you hear that? 

Chad: Oh, I would again, being a nerd as I am, I want to really understand the methodology of that study. Like, how exactly was the question asked? Who was it asked to? What was the sampling methodology? What are the error bars? Who did the research? When did they do the research? It's the whole QuantCrit thing. What is everything behind that one nice neat little statistic we hear? 

Carrie: Yeah, exactly.

Chad: What do you think? 

Carrie: The same question. I also was like, okay, 3%, I want to know exactly what you asked. I also want to know more data. So it says that 3% use Latinx for themselves. Does that actually match with the demographics of people that are non-binary or transgender in the Latina community? I don't know. So it's like [crosstalk]

Megan: That actually seems high for that. 

Carrie: So yeah, I have these further questions where it's like, I just want to dig more into that. But I wonder if you too, ultimately with QuantCrit, you just come back to the idea of it's just respectful. How do you do that in your head?

Chad: Well, again, for me, it goes back to the humility part. I talk about empathy and humility in the book and how we often get told that doing work across racial lines and understanding other groups, is how necessary empathy is. I don't exactly disagree with that, but also I think there's a form of empathy that's unintentionally arrogant. So I think you have to have it with a healthy dose of humility and just listening really hard and understanding that you will not always understand and trying to fix your mistakes when you make them. I think this is true too with labels for people. Labels can do harm, right? So I have gotten labels wrong. I've gotten terminology wrong and I have to just say, "Okay, this isn't about me, except insofar as do I want to be a person who does less harm. Yes, I would please like to be that person." Then you just have to try to listen more and do better next time and apologize.

Megan: Yeah.

Carrie: If the question really was, "Do you use it for yourself?" Of course, the percentage will be small. If the question was, "Do you use it at all?" Then you might expect the numbers to be higher and I don't actually know how it was worded. Do you know, Megan? 

Megan: So I guess I had a couple of questions. Again, Pew Research is trying to be accessible to people, but it also just erases so much at the same time. So I guess they asked questions around, "Have you heard of it? Do you use it?" So they say that 76% of people have not heard of Latinx who are quote-unquote US Latino adults. Although the polls called about one in four US Hispanics have heard of Latinx. So they go back and forth even with Latino and Hispanics in this.

Carrie: Yeah, those are different groups.

Megan: I do not identify as Hispanic. I just don't, but I do as Latina. But then it says that, "Have heard of Latinx, but don't use Latinx is 20%." So it's like, okay, but for yourself, there's just like, I have so many questions based on this. So I just want to ask you, Chad, because this book is so engaging and for people to better interact with data, I just wanted our listeners to hear how you would have approached this, because I think it's so important that we all start approaching data like this too, to be conscientious consumers of data as well.

Chad: Absolutely. So I do think adopting that QuantCrit approach, trying to understand where data comes from is really important. But also something else I try to emphasize is what has to go alongside data is people, people and their stories. So also ask people and listen to people. I did also want to say that this idea of Latina versus Hispanic, race versus ethnicity, this is an issue that comes up repeatedly in the book and has been the source, whether intentional or not, of much obfuscation of racial disparity. So this is a thing that we have to fix.

So again, just as an example, we study court cases in Broward County, Florida. Broward County is one of the most racially diverse counties in the entire country. Essentially for the particular study we were looking at in the court records, there were basically no Latina people, essentially none. We finally figured out that essentially they are only coding race and not ethnicity. So they decided maybe some people are Hispanic, but we're not going to record that. Anyone who's actually Latina is getting recorded as being white. So it makes it, of course, then impossible to detect what is the disparity that group is experiencing when they're just lumped in with all the white people. 

This is not only in Broward County, Florida. There's a horrific story out of Louisiana where ProPublica investigative journalism outfit ProPublica looked at hundreds if not thousands of traffic tickets that were given to people with the last names like Gonzales, Hernandez, Lopez. Names typically associated with being of Latin descent. All of these people were coded as white and the article title was, I'm paraphrasing here, but the article title was something to the effect of, "If everyone's white, there can't be any racism."

Carrie: Right. Yeah. Well, the other thing is that Hispanic and Latinx, whatever, are overlapping, but different groups.

Chad: Yes, exactly.

Megan: Right.

Carrie: So that also obfuscates you. So you can't just interchange them. 

Chad: No, Absolutely. 

Megan: Yeah. So this leads us into our next question. How can quantitative approaches do harm?
That's a good example where it's able to hide racial disparities if we don't do our data collection correctly. 

Chad: I think that's exactly right. I also think, how do I want to say it? Even aside from obfuscating disparities, I think mislabeling people can legitimately be a source of pain. So when we think we're asking about gender, and we are forcing people to check a binary sex at birth box of male, female, and then those are the statistics that we're repeatedly working with, that causes harm and erasure. I think the US census still asks, male, female. That is the measurement of binary sex at birth. This filters its way into our doctor's offices and our, I don't know, our DMVs and our who even knows what else. It puts people in boxes that are not actually boxes that they're living in. So this is another real concern I have is the harm caused to individuals by being misnamed and misclassified. 

Carrie: And funding, they can't get funding if we don't know what's happening. 

Chad: Absolutely true. Yeah, if we don't recognize the existence of a group, we certainly can't know what problems/challenges they're experiencing in society and how we might better support them. 

Megan: Cascading effects. Yeah. 

Carrie: Yes. I literally just did the Canadian Census....

Megan: Ohh...

Carrie:  Oh yeah, it still just asks sex at birth. 

Megan: Yeah. I just was looking it up because I couldn't remember how they asked that question. 

Chad: Yeah. Now, of course, we live in a time when there are also a lot of data privacy concerns.
So there's worries about identifiability and things like that. If the census has transgender and you're the one transgender person living in your particular census black group, do you want to tick that box? Well, in the past, maybe naively, I would not have worried about that because the census was very good at addressing issues like preventing identifiability in data. I think in the present day, no shade on the people who work in the Census Bureau, who I'm sure are by and large still great people with good ethics, but they're probably under a lot of pressure from higher levels of government. So this is actually a thing that concerns me.

Megan: Yeah, there's only so much data suppression you can do because at a certain point if you know data has been suppressed, that means you know that there's at least one. There's over zero.

Chad: Exactly. 

Megan: Yeah.

Carrie: Well, on the flip side, how can these approaches help?

Chad: Oh gosh. Well, I think again, one is sort of lobbying lawmakers or policymakers in places where there are practices and laws that are resulting in more disparities. Again, in my case, the things I'm thinking about are racial disparities in the criminal legal system, but I think the idea is more widely applicable. I think that for raising public consciousness and understanding what are the specific sources of inequity, data can really help us understand that. So again, whether that's what happened with disparities in accessing police in my own small hometown of Williamstown, Massachusetts, or whether it's figuring out which judges in New York City lean too heavily on bail or whatever, I think the data can really help us zoom in on the specific sources of disparity.

I think data can also just help raise awareness. I would love to see people be more vocal about what data can do. Going back to the conversation we had about the crazy three races in New York City, or how messy data from the government is that we have to fix the messiness in order to be able to work with the data, or how the federal judicial branch is obfuscating the names of judges in the data that puts out all of these things. You don't have to be a data scientist in order to talk about those things. You don't even have to like numbers to talk about those things. You just have to believe that the Constitution guarantees us access to certain kinds of information and that we should be able to access that information easily as it will help us understand our systems better. 

Megan: Absolutely. There was a point, I think, during this administration, the second one, where the census website was down, because that's a government website. I was like, "Oh, no, are they just going to remove it?" It was scary. The idea that they're taking away our access to data, that scares me so much.

Chad: It's terrifying. So I teach at my institution at Williams College, I teach a class called data for justice. This is a class that's aimed at students who have never taken statistics before, never programmed a computer before, never done anything and we're trying to teach some data science skills and data critical skills in a justice context from the ground up. One of the things that I teach in this class is how to access census data, because this comes up in so many justice problems that we work on. I went to start preparing my materials for the semester and  the computer code I have that interfaces directly with census data didn't work. I was freaking out. I was like, "Oh my goodness, this is so bad." 

Fortunately, I have the good fortune of co-instructing this class with a friend of mine, Reagan, who's a librarian on campus. She is a genius and she has alerted me to the efforts that librarians and others have been doing to archive government data sets that we're afraid might go away forever. Even though some of those have come back, like Census Access has come back, I will never again take it for granted that data I want to access will always exist. So this was a very important learning opportunity and from my heart, like huge thanks to all the people who are doing the work of cataloging and archiving that data. This is the kind of thing, it sounds so unglamorous. Like, I'm copying government data sites. It's so important.

Megan: So important.

Chad: It should be glamorous. So let's give those people a red carpet. 

Carrie: Yeah. 

Megan: Exactly. Librarians are awesome, right? Also, again, going back to your realm of influence, we can only do what we can do. That's a great example of that.

Chad: Exactly.

Carrie: You know what that reminds me of? The Key and Peel sketch of like, I think it was teachers, as if they were like football sports stars, you know?

Megan: Oh yeah. Yes.

Carrie: We need that for all these other kinds of jobs. 

Chad: Yes, 100%.

Megan: Exactly. Oh my goodness.

Carrie: You also talk a little bit about a paper that you're asked to present on data equity and diversity, but the organization called it too political. What did they mean? 

Chad: Well, the most honest answer I can give, Carrie, is I don't know.

Carrie: Okay, fair. 

Chad: I can guess, but maybe we can sort this out together. So here's the story, is I am a member of the Society for Industrial and Applied Mathematics or SIAM for short. I think in mathematics, like in a lot of other scholarly fields, we have various professional societies whose job is to try to promote research and education and community building in the field. SIAM, I have been involved with probably since 1997 or thereabouts, when I was a graduate student and I have to say they are a wonderful organization. They do so much good. 

I am so grateful to them to the point that when I go to SIAM conferences, I will run into an officer of the organization and extemporize flowery speeches about how much of a difference the organization has made in my professional trajectory. They're like, "Okay, Chad, I get it. Can I go get a cup of coffee now?" So I just need to start by saying, "This is an organization that has done a lot of good." But I think for this reason, the incident you're referring to was especially painful for me. 

So what happened was that I had a colleague who was organizing a session at the SIAM annual meeting last summer so summer of 2025. It was a session that was on data and equity and inclusion. Actually, I sort of anticipated you might want to talk about this. Is it okay if I read part of the abstract that I had written?

Carrie: Yeah, please.

Megan: Please., yeah.

Chad: So what I submitted was, at the time this abstract is being written, the United States is in the midst of a takeover. A small group of unelected officials and private actors have seized control of the government apparatus and are dismantling it. The administration is reopening Guantanamo, rounding up immigrants, dismantling scientific institutions, abandoning public safety, rolling back civil rights, and systematically erasing queer people. Now, the abstract goes on and gets into the math part and what we as mathematicians can do, but that framing is, in my opinion, factual. 

Those are all things I can give you news stories. I can show you how Congress people were being blocked by Doge from entering government buildings. Doge employees were private actors. So I think all of these things are supportable as fact. My abstract was accepted. Some months later, in the few weeks leading up to this conference, I received an email from a vice president of the organization that told me that my abstract was too political and outside the bounds of scientific discourse. I was just gobsmacked. I was like, these things are all facts. What is the problem here? 

So this set off a flurry of emails amongst myself and a dozen, if not more, people within the organization. I think because I have a very close working relationship with a lot of these people, I think they were trying to bring me into their headspace on this. One of the ways that this happened was people sending me suggested revisions of my abstract. This, while I am sure it came from a good place, or I hope it did, it did not sit well with me. So I will just give you an example. 

So again, my abstract started with, where was it? At the time this abstract is being written, the United States is in the midst of a takeover, small group of unelected officials taking over, were taking away civil rights, all the things. The revision that they proposed to me was, "At the time of this writing, the United States is experiencing a period of rapid institutional and societal change. These shifts raise important questions about the role of data and mathematical modeling."

Carrie: I hate that.

Chad: I was like, "That is not what I said. I know what I meant." So this was a very disappointing experience for me. It reminds me that organizations can be full of people who do good things and who have good values, but that an organization is not necessarily the same as individuals living within that organization. So, I don't want to say it taught me the following lesson, but it reminded me of the lesson that I really need to not listen to what organizations say, but watch what they actually do.

Carrie: 100%.

Chad: That's what matters.

Megan: Yes. That's such a good message. Yeah and the idea that what they rewrote for you, whoever it was, wasn't political? I'm like, that's also political, right? Because what aren't you saying?

Chad: Megan, I'm so glad you said that because many of my responses said, in fact, this rewriting is the political act. There is a large discourse over changing language like mine into language like yours. If this is an idea you're not familiar with, I would be glad to send you some reading on it. Is that a thing you are interested in? But no one wanted to take me up on my offer of a homework assignment, sadly. 

Carrie: Yeah, because they do very well what you were trying to say and they were like, "We're going to get in trouble. We have to soften this so that Trump doesn't come after us." 

Chad: Right.

Carrie: These people are scared and I sort of get it on one hand and on the other hand, I don't get it. You have to stand up to bullies.

Megan: Right. 

Chad: So for me, this is one of them places where the idea of positionality comes in. I don't know, this is a bit of a dicey joke, but I'm sometimes like, thank goodness I am gay. Because I sometimes think like, if I were not marginalized on some axis, I could have grown up to be even more of a jerk than some people already think I am. I know what it means to have my rights taken away. I know what it means to have to fight for my rights. I know what it means to have to stand up to bullies, because I have had to do this a lot in my lifetime. I think anyone who has any access to their identity that is marginalized in any way knows this lesson, right? Or they should at least know this lesson. 

Carrie: Yeah, I feel similarly. Like when I was a teenager, I had a realization, like, "Well, at least I'm not a white man." I just can't imagine how much harder it would be to learn anything. 

Chad: Yeah,

Megan: Yeah. I want to go back real quick too. I wrote it down because I was just struck by it, that your discussion was outside the bounds of scientific discourse. Can we just touch on the idea of what is actually scientific because I think it's embedded in this idea that data can be unbiased.

Chad: 100% and I would say it's not just that there's an illusion that data is unbiased, there's an illusion that science as a whole is unbiased. Science, like newsflash, science is done by people. Alan Turing was essentially tortured. People were put to death during the Inquisition. So the idea that there's just truth that is untarnished by what other people want is a falsity. So that doesn't mean that anything we say is inherently wrong. It's why we have to be honest about positionality. It's why we have to be honest about QuantCrit, all of those things. We cannot get rid of subjectivity. We just have to be honest about it. 

Megan: Yeah, and not be afraid of caveats, right? Not being afraid of this is the limitation of the data.  Yeah, that's not scary. That's the truth. Right? Yeah. 

Chad: Writing the book is one of the things that was both scariest, but also most exhilarating for me was just engaging with the complexity. So there's a chapter on federal judges, where I give some examples of judges who stand out in our data as having sentencing records that seem not in line with maybe some of their peers. I remember writing something to the effect of, "I have to be honest with you. These raw statistics that I'm providing you with are not a smoking gun, proving that these are racist people." I feel like that's in a way not what society always wants. Sometimes society wants a little bit sensationalist story. I'm like, "No, we need honesty where we're engaging with the complexity and what we really know and what is still left slightly not understood.

Megan: Yeah. It almost doesn't matter whether they're actually racist. What matters is the outcome anyway, right? So do they learn from what they're doing or do they not? Then maybe you can start making some inferences. 

Chad: Absolutely. There's a story I tell early in the book that's part of the story of how I came to this work and it's work I did not on the criminal legal system, but on diversity in art museums. I tell a story [crosstalk]

Carrie: It's so good.

Megan: So good.

Chad: Oh, thank you. I tell a story about doing work with the National Gallery of Art where they very generously invited some collaborators and myself to work with their data and try to study diversity within the walls of the museum and tell them some stories about that. It didn't look good. The museum, at the time, was almost completely works by white men and we got to present this to the curators and the curators, truthfully, we're like, "Well, we have these artistic themes we want to highlight and these are the artists in those themes. 

Also, a lot of our works are donated, and when we get those donations, those donations of artworks come with stipulations about how often they need to be shown and in what manner. So we can't control everything". I was like, "I understand that." But if you are a gate-kept artist who's a racial or ethnic or gender minority or otherwise marginalized person, I'm like, "You probably don't care, right?" So, solve the real problem. Like, the reasons can help us diagnose, but they shouldn't be an excuse. 

Carrie: Right. Yeah.

Megan: Absolutely.

Carrie: When I read that part, I was like, "Oh, this sounds so familiar." They want to feel better about themselves, but they don't actually want to do the work.

Megan: Right. Yeah. It's the first step in wanting to know if they're being biased. If it's like, "Oh yeah, they have to actually sit with what comes next. If it doesn't look good, which is hard. Again, like [crosstalk]

Carrie: It is hard. Yeah.

Chad: It's hard.

Carrie: Yeah. These things are hard and it is true that some donors do have these requirements. It's not that they're lying, but it's still frustrating because it's like, "But that's not enough."

Megan: Right. 

Chad: No, but also, I really want to make this tangible because my brain can't not make it tangible. My initial reaction was, "For any of the donors that are living or for their estates, have you gone back to them? Have you gone back to them to discuss?

Carrie: That's true.

Chad: Have you examined what latitude you actually have within the agreements that you already signed? Have you?" There's tangible things you can do. 

Megan: Absolutely. Yeah, absolutely and speaking of tangible things [crosstalk]

Carrie: ....tangible things you can do, how can people help fight injustice with data? 

Chad: Yeah. So a big message I want to send is you do not have to love numbers. You do not have to love data. You do not have to turn yourself into a data scientist. If those are things you want to do, I truly believe it's like, oh, remember the animated movie Ratatouille where it's like, anyone can cook. I have a firm belief that if structures permit, anyone can do math, right?

Carrie: Yes, I agree.

Chad: But unfortunately, structures do not always permit. So if you're listening to this and that's your game, you want to become a data scientist and you don't know how, call me, I'll tell you how. But that is not what I expect of people. I think regardless of how you feel about data, you can read stories about data. You can understand the importance of data accessibility. You can know what our rights are with regard to the data we are supposed to be able to access. You can raise awareness to your friends and family and social media about data. You can donate to organizations that use data to try to make things better, of which there are many mentioned throughout my book. 

So, at the end of each chapter, there is a little section called The Path Forward that tries to list specific things that people can do. Then I try to recapitulate those things in the final chapter of the book. So there's a laundry list where even if you yourself say you're not a numbers person, secretly you are or you could be. But even if you're not a numbers person, there's still so much you can do.

Carrie: Yeah. So everyone go buy the book. 

Megan: Yeah, please go buy the book. Feel empowered by it. Again, I love that these are actionable steps because I often feel like I can't move forward and I just stop and just sit. It's so overwhelming. So having actionable steps is hopeful, like you said in the beginning, it's hopeful. Having  a little hope goes a long way. 

Chad: Sure.

Megan: So, I just follow you on Bluesky, let's be best friends, Chad. I love chatting with you. This was fantastic. Before we let you go, do you have anything you want to leave our listeners with one last message?

Chad: Oh gosh. Yes on that last note. Have hope. We are living in such hard times, and if you wake up on any given day and you're having a hard time with the world, you are not wrong. So when I say I have hope, I don't mean to put on a smiley face and feel good. We don't control that about ourselves. I think what I mean is in the spirit of Rebecca Solnit, leave room for the possibility of the unknown and for things getting better. There are lots of people trying to make them that way, and I have faith that so many of your listeners are among those people. 

Megan: 100%. 

Chad: So, Megan and Carrie, to your previous point, yes, let's all be besties from now on, please. 

Carrie: Yes, please.

Chad: Yay.

Megan: Yay, and we always leave our listeners with one final message. 

Megan and Carrie: Don't be an asshole.

Chad: Noted and cosigned. 

Voice Over: The Vocal Fries Podcast is produced by me, Carrie Gillon, theme Music by Nick Granham. You can find us on Tumblr, Twitter, Facebook, and Instagram at Vocal Fries Pod. You can email us @vocalfriespod@gmail.com and our website is vocalfriespod.com.

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