Race & Genetics in America
Transcript for Student Voices
Brandon Ogbunu/Raghav Pant
You’re listening to Student Voices, a podcast featuring student-led interviews of Brown University faculty based on the Race & in America panel discussion series, curated by the Center for the Study of Race and Ethnicity in America in partnership with the Office of the Provost.
Raghav Pant: I'm Raghav Pant, I’m a Bachelor of Science candidate in the Department of Applied Math at Brown, and I'm speaking with Professor Brandon Ogunbu today about the recent panel discussion on Race & Genetics in America. I want to start by saying thank you to Dr. Ogunbu, as well as Dr. [Lorin] Crawford and Dr. [Sohini] Ramachandran for taking the time to present that talk today and speak about their thoughts on the issue of race and genetics as a whole.
I wanted to begin with a couple of questions regarding my own personal interests in applied math and the ways in which that has an intersection with the topics that we discussed the other day. One of the first things that popped into my head, particularly as you discussed the nature [of] placing people in categories, and the word you used is binning, throughout the course of discussion, one of the first things that popped into my head is the ways in which mathematical modeling techniques -- whether they be infectious disease modeling, whether it be Cisco learning, machine learning models -- might use a similar approach in terms of finding ways to characterize the population as a whole. And as somebody who has grown up with the idea that mathematics and STEM fields are inherently objective, that they're difficult to be biased from the start, what your thoughts were on how these kinds of modeling techniques can potentially be biased from the start or even be prone to biases as they're implemented?
Professor Brandon Ogbunu: First off, Raghav, thank you so much for the introduction and thank you for taking time to talk to me. What a fantastic question. I think what you've articulated is something really important. While the Race & Genetics seminar was very firmly about the problems about categorization within the genetic framework and in biology, you are absolutely correct. The problem of how we categorize people and things and bend them into groups and study them transcends biology and is absolutely very central in modern conversations about the way we build our algorithms and the way we study populations of things in other realms. From economics to sociology to other realms, where we do things, where we're interested in the behavior of people. For example, a really big one that's in polling and political science where we're interested in why certain groups are voting certain ways. One of the things we're continuously finding in these contexts is that groups like “African Americans” or “Asian Americans” are very, very heterogeneous. And that doesn't mean that we don't learn things from studying them under those titles, but we find out there's a lot of surprising things when you take the magnifying glass and you study them closer. That there's a lot of diversity, that motivations and experiences are different, and I think that's something that transcends any one field of study.
Raghav Pant: Right. That makes a lot of sense and I think, you know, as a junior, and now I've talked to people over the course of my undergraduate studies who feel strongly that it's not necessarily the tools and the studying and the modeling techniques that are the issue, but rather the implementation of the issues. Does improvement in this process lie in changing the way we approach the problems fundamentally, or rather being able to educate the people and being able to kind of change the way that we implement these techniques in an informed way?
Professor Brandon Ogbunu: It's a good question. Is the change that is necessary fundamental? Right? When it comes to genetics, for example, or race or algorithmic justice, is it fundamental? I would say that it is, but I would say that that fundamental change doesn't mean that we have to tear it down. I think it just means we have to tweak how we think about the process of understanding what a population is. So in the context of race in genomics, for example, in genetics, right, I think the tools we have to study genes and genomes are really, really good. And I think there's a lot of examples in nature of the way those things have enabled us to understand a lot of things about the way species divergence happens and the relationship between populations. We've seen this in the context of SARS-COVID-2. We're now able to essentially track the evolution of the virus in real time. This is amazing stuff. So the tools are really, really good, the problem is when we take those concepts and we try to understand complicated areas where they don’t quite apply. So the notion that you can understand populations of SARS-COVID-2 and then the notion that you can translate that over to understanding populations of homosapiens is trickier because what's happened with homosapiens as a species is dictated by all these other complicated forces that undermine us thinking about human beings as these kind of simple automatons, these kind of organisms that we could simply model abstractly. I think human experience is a very colorful one. You have to understand the thing that you're thinking, but you have to understand culture and war and power and gender and all of these things that have had a very serious signature on what has happened with homosapiens. I think similarly when it comes to mathematical models, for example, you're trying to understand the way an epidemic works. I think you have to be very, very careful when you think about what does it mean to be a susceptible host, an individual who can get a disease? As we've learned from the modern pandemic, different age populations have different risk and different age populations might be transmitting at different rates. You have to be able to appreciate that level of difference if you're going to build a responsible mathematical model.
So I think across the board: more knowledge, more domain expertise is the key. I think when you think about the problems, right, when it comes to binning things and oversimplifying nature, I think the consistent theme is that we like to cut corners and we like to kind of, we want shortcuts, we want to be able to do something cheap and simple to be able to get us to an answer. And that's just not gonna to happen. It's gonna be hard! Life's hard! [laughs] Understanding life's hard. Modeling life’s hard. You want to understand the way a species like homosapiens with its complicated history evolved and the way it works and how we’re related to one another and how our traits manifest? It's gonna be tough! Get over it. Same thing with tracking the epidemic, same thing with an algorithm that we use to, I don't know, for whatever the kind of suite of human behaviors and policies and things that we were trying to implement in algorithms. It's gonna be difficult.
Raghav Pant: Right. That makes a lot of sense and I think you bring up COVID-19 as a very timely example of something that has had a lot of disparate impact over different groups of society in the United States as well as elsewhere. And I think it's an important consideration to ask how modeling might have contributed to underestimating the impact that it might have had in different communities. To move on beyond the scope of mathematical modeling and society-wide as a whole, I know you discussed just now heterogeneity of the human population and kind of understanding the human experience and the diversity of that human experience.
One of the things that I've observed as an undergraduate is sometimes a bit of a hesitancy to engage honestly and openly with discussions around race, whether it be in the field of genetics and population genetics or just in general. And maybe it's something that I was privileged enough to be raised with the perspective that we can engage openly and think honestly and speak honestly about the realities of our situation being, for myself, a first generation Indian American in America. And for you, I guess the question is in STEM and elsewhere, is this progress? And does making sure that science avoids these biases in the future, does it come from engaging more openly with the questions of the differences between people or is it more of a question of trying to homogenize, trying to move away from the question of race and just consider the human experience more broadly?
Professor Brandon Ogbunu: Well, you know, I think it's a mixture of those two things. I think first you have to define what we mean by “open dialogue,” right? I think open dialogue can be, of course it's a very positive thing because that allows us to get to the bottom of what the issue is. We have to have open dialogue. I think United States history is one that hasn't been good about getting to the crux of what the problems really are and how we arrived at the places that we've been. I think this is a constant struggle even in the modern political sphere. The problem with the phrase “open dialogue” and “we just need to be open-minded” is that the conversation also needs to be safe, right? I think a lot of people use “open” to mean that I can say whatever I want to about somebody and you can't be hurt by it. Like that's not true. I think, for example, I think racist ideas are not on the table. Those have been destructive. Those have crafted, not only have they cost a lot of lives, they've crafted so many deeply broken things about the way societies run. They've undermined human progress in these really important ways. So what I'm saying is I think open dialogue about human differences does not necessarily have to have anything to do with race. So you can have an open dialogue about, like the example I used during the seminar about why I'm not an NFL running back. I think there's very clear signals as to why I don't sprint a certain speed or why me and LeBron James had different life trajectories athletically. I think those things are very, very clear and I think open dialogue about why that is, but I think when you start talking about things that are much more framed by policy and much more framed by history and demography, with regards to why aren't there more women in mathematics, for example. I think it gets blurrier because then you start to say something like, “Oh, well, we need to be open about the dialogue about why there aren’t more women in mathematics,” but really what you're doing is you're just justifying why there's an achievement gap or you're trying to defend the fact that these fields have been unwelcoming from generation to generation. So if one is open, dialogue is really, really healthy, but it cannot be used to justify wrongdoing or bias that has plagued a lot of the world for many generations.
Raghav Pant: Right. I think the phrase “open dialogue” is thrown around sometimes by people who might not have the best intentions and that relates to something that you talked about during the presentation last week which I found very interesting, kind of stuck with me, is this notion that genetics as a field has been co-opted by white supremacists and other groups of that sort to justify their own racist viewpoint of the world. And I think the quote that came out that really stuck with me was “Science should get to a point where, if somebody wants to be a racist, science is not gonna be able to help them out.” And I think that kind of touches more broadly upon ideas that we're having about how science has historically always been co-opted by people who have those kinds of mal intentions in mind. The clearest example in my head is [Charles] Darwin's theory of evolution, something so fundamental to biology, used all of a sudden to justify centuries of colonialism, or at least a century of colonialism, in Africa and Asia on the basis of social Darwinism.
And so the question is, and we've seen similar things over the course of the past year with COVID-19, politicians, policy makers, using science, bending science to fit their own political viewpoints on the situation. And it kind of goes back to my earlier question about science, mathematics, STEM fields as a whole being inherently tools and the question is how do you prevent them from being weaponized in the way that you're describing?
Professor Brandon Ogbunu: Great question. Amazing question. I think there will always be people who have an agenda and want to legitimize treating other people poorly. And that might be Asian Americans, Latinx populations or what have you. And that might be Yankees versus Red Sox. Human beings, we find ways to dislike each other, or not even dislike each other, just to kind of label each other. And that might be who we are and that's okay. Like I said, the key is do we do that to deep destructive ends? And I think that's the one we can prevent. I think race reasoning at the policy level like has been used in the Trump administration, for example, to encourage immigrants from Northern Europe and to discourage immigrants from Africa, is broken and racist and has no place ever in any policy. Ever. So I think one of the ways we do it is if there's a zero tolerance for that type of reasoning, for a certain type of policy. That's one of the things we can do. Now I think they'll always kind of try to bend science and I think that’s been happening recently. I think the science has not supported white supremacist ideas at all and I think they’ve been trying to bend things further and further, and get more and more magical in how they use science to support them and misinterpret graphs of certain kinds. I mean, that's kind of the most famous one now, their misreading of a certain type of PCA plot to say that we are essentially different. But what I'm saying is I think that they're retreating into, further and further, more absurd science to justify it. We talked about the drinking of milk thing, which is the stupidest thing I've ever seen in my life, right? That white supremacists are drinking milk on video and, like, flexing their muscles to basically demonstrate that, “Well, we're European and Europeans kind of have this propensity to be able to digest this milk and so that's a sign of our whiteness.” That's the stupidest thing I've ever seen in my life. And as sad and destructive as that is, and it is, when I see that I can't help but laugh because that's the sign that the person that you're arguing with is losing. You know what I'm saying? Because they have to retreat to this kind of stuff and so my point is let's keep that pressure on. Let's keep communicating with the truth, which is the science. Let's keep communicating and revealing all these interesting and fascinating things about the world so that racists have to keep doing stupid stuff like drink milk in order to prove their valor in their whiteness.
And that's the goal. And I think that's the best we can do because I can't make people like each other. I can't do that. I'm a Yankees fan, I don't like Red Sox fans, right? [laughs] And I think in some ways that's okay. I think the key is whether or not I use that to justify discriminating or mistreating people. It's the discrimination, the prejudice and the mistreatment that should be the targets of our ire.
Raghav Pant: Right. That makes complete sense and I think hopefully I speak for all undergraduates, whether they're in STEM or elsewhere, in hoping that we can do our part moving forward in the next 5 to 10 years to contribute to keep this moving forward and creating kind of a symbiosis between social concerns like that and just human progress as a whole. So I want to thank you, Dr. Ogunbu for your time today and for your thoughts and your insights, both in the panel and with me. And I really do appreciate it, again, and hopefully we can continue dialogues moving forward.
Professor Brandon Ogbunu: Well, I want to say to you thank you. You really are a demonstration of what the next generation can do in this space. You're extremely bright with your mathematical work and I think you obviously understand the way society works, and so I'm excited to see what you become and your generation becomes and how they take these issues moving forward.
Student Voices is a feature of the Race & in America digital publication series developed by the Brown University Library. Our theme music is “see the unseen” by Butter. Explore the series at DigitalPublications.Brown.edu