Biased data is clouding the truth in minority communities

Q&A with Dr. Amy Quarkume of Howard University on how her research is clearing up misrepresented environmental data in Black and Brown neighborhoods.

Q: Welcome to today's program. My name is Eva Chillura and I'm here with Dr. Amy Quarkume of Howard University. She is an associate professor of Africana Studies and Data Science and is affiliated with the Environmental Studies department at Howard. 

She is here with me today to discuss her ongoing research on environmental data bias and underrepresented communities, some of which include Manchester, TX, Little Haiti, FL, Mossville, LA, Baltimore, MD, and of course [Washington] D.C., specifically in the Howard and Brentwood neighborhood. This is the third year of her research, and most of these communities are majority African American, Hispanic and Native American.

Thank you so much for joining us.

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A: Thank you for the interest in this project. My name is Dr. Amy Yeboah Quarkume. Most people call me Dr. A. 

Q: We're very excited to be talking about the environmental data bias research that you've been working on. And so that's what we have a couple questions about today. So to start, will you tell me a little bit about you and your role in this research?

A: My background is in traditionally African American studies, so my work looks at gender issues and education. But most recently, particularly around COVID, I pivoted and began to dive deep more into data science and environmental studies.

Q: And I actually had a question about what you were mentioning earlier about your past positions in research. It seems very rooted in the humanities, from publications on civil rights, women's rights, collegiate life – now, to you’re covering environmental data bias seems like a very stark deviation. How did you get here and why?

A: I believe that where the hard sciences – where the data science and tech is – it cannot move forward without the humanities. So I'm still a humanist and I'm trying to bring more of the humanist – human loop – into the hard sciences. The pivot comes with COVID. It was very disheartening to see that AI had under-reported, exponentially mis-detected, allowed vaccines to not be in certain neighborhoods, predicted incorrectly many African Americans’ experiences, so tech and COVID really had me hard pivot.

Q: When you talk about this environmental data bias and this AI work that's happening, what does that look like for the average citizen? For the average community member? You know, how does that affect them?

A: It comes from the [Environmental Protection Agency] and many government agencies. A lot of the data that's collected on weather, heat is very generalizable and not generalizable to all communities in a good way. There are many ways that many communities are facing distinctly specific realities that is not captured.

And they're getting information as if it's their localized reflected experience. But it's not. People are being told that it's 80 [degrees Fahrenheit], but it's really 95. People are told that their air quality is good, but it's really not good because the EPA doesn't test for certain toxins. So that's the definite center of our project. 

Q: And you mentioned one of your communities is looking into D.C., you work at Howard University, which is in Ward 1, and this ward, as you know, has a high population of Black and Hispanic people, 46% of the population are actually Black or Hispanic. How do you see the effects of environmental data bias on these minority communities specifically?

A: It’s interesting. So we've looked at that data – the air quality around Howard and the Shaw community and compared to a couple of pockets in D.C., interestingly, Howard, you know, Blacks is very high in having high and poor air quality. And a lot of that has to look at Waze data, traffic data, a lot of that has to do with Georgia Avenue.  

And the amount of pollution that is emitted as people go from Maryland to D.C., so we're hoping that this project would open people's eyes to really question localized data. You know, there are ways in which we should specifically cater to and address issues with these communities that have higher rates of air quality issues and just not assume that because D.C. generally has good air quality compared to other areas that certain areas need specialized attention when it comes to air quality issues.

Q: When we're talking about this environmental data bias and where it's coming from, you mentioned like the EPA, government agencies, is this something that is intentional or just negligent?

A: So when it comes to infrastructure, some of it’s structurally intentional like things were built certain ways. Pipelines were built in certain ways. Landfills are next to Black communities because historically people felt that these are where they should be put. So that somewhat is structural, historical, and capitalistic in a way. If we had to get rid of waste, we'd put it next to people we don't value unfortunately. It is structural. It is historical, and it's not a secret.

“It is structural. It is historical, and it's not a secret.”

-Dr. Amy Quarkume

Q: In the past two years, what challenges have you faced?

A: One, thank you for the question. One challenge is building trust takes time. Being able to get localized data, a lot of the project deals with people's real lived experience with environmental issues. So what is the water quality from the water coming from your tap? What is the heat that you feel around your home? What is the air quality around your home? In many cases, some of the institutional EPA monitoring sites are away from these communities, so they're getting readings at, for example Little Haiti, the nearest monitors are at the airport. So for us to be able to get their unique specific air quality heat temperature, I would have to get permission from someone to allow me to put a monitor on their house. So that was a challenge just kind of getting individuals to kind of agree to be monitored in a way that they didn't feel surveillanced.  

Q: Is there a solution that you've theorized or thought about in the process of this research? 

A: I think right now the solution we're going for is community localized data. People just need to know what they're dealing with if we can start with that. You know, if we know how bad the air quality is at 8:55 a.m. when kids start going to school on Georgia Avenue, maybe we can divert traffic. So kids are not, you know, walking through the [HI!] –  so having people kind of know from a community level what that data is and then they can create their own solutions. So I don't think I have the solutions [HI!]. I think part of the solution is giving people more information.

“Our children need to have a place to live that is clean, and as much as we think it's important to pass on wealth, it's important to pass on, you know, I went to a college so my child could have a legacy of going to that same college. It's also important that we pass on clean air.” 

-Dr. Amy Quarkume

Q: You mentioned wanting to protect your daughter and your life from these environmental risk factors into the future. Tell me a little bit about her. I can hear her voice now.

A: Can you say hi, Anna? Anna, say hi. Okay, now she’s shy because she’s on camera [laughter].

Anna is – mhm k –  Anna is 14 months. She was born in Howard County, MD, close to Baltimore, one of the biggest issues is air quality and asthma. Our children need to have a place to live that is clean, and as much as we think it's important to pass on wealth, it's important to pass on, you know, I went to a college so my child could have a legacy of going to that same college. It's also important that we pass on clean air. 

The questions we have in the Academy are being raised by young people because they know it is a pressing issue. You need to continue to allow us to stay focused on that pressing issue because it is your future. 

Society can get bogged down with war, crime, economics but equally, young people need to kind of keep us abreast with saying that we want a future where we can have and maybe deal with these other issues.

Q: Well, thank you so much for joining me today. It's been such an insightful conversation. 

Just thank you for your time and your research. I hope to see more of those results in the coming years.

A: Same here. Thank you. Enjoy the rest of your day. Thanks for having me. OK bye. 

Say bi.

Q: Bye Anna!