Science is touted so frequently as a synonym for “progress” that I sometimes wonder when the Oxford English Dictionary will take the step of formalizing this mythological relationship. Science, so it goes, makes for not just better thinking, but for tangibly better living.
Got a problem? Solve it with science. And indeed, there is much to be proud of. Thanks to vaccines, for example, smallpox no longer regularly kills American children, and while we continue to struggle with the flu, there hasn’t been a major pandemic in a century.
In this context, it’s not surprising that “science” is also being brought to bear on what is often called “urban blight”—perpetually underserved communities made out of a nonlinear combination of racism and neoliberal economic policy. Wired reported last week that a Baltimore-based Johns Hopkins University astronomer—a member of my professional community—is working with the city of Baltimore to create maps of so-called housing vacancies. As the story, titled “The Astrophysicist Who Wants to Help Solve Baltimore’s Urban Blight,” explains, Tamás Budavári is using computer codes very similar to those he uses to understand the evolution and dynamics of galaxies. If you think that economically depressed urban communities are in need of saving, that technology can cleanse away human messes, and that the same math can describe completely disparate situations, this sounds pretty cool.
It is less exciting for those of us who are familiar with the lived experience of gentrification. Being originally from East Los Angeles has, for most of my time in academia, meant being on the receiving end of comments like, “Isn’t that the bad part of town?” Recently, as my childhood neighborhood has gentrified, I’ve started to receive, “Oh, I hear it’s getting a lot better now,” code for, “Soon that will be a nice place—for the upper-middle-class people who can afford it.” Few of the people making these comments understand the grief that it elicits.
It’s true that in East Los, as it is known, we are products of redlining, poverty wages, and the colonial violence that pushed immigrants like my mom to leave their homelands. Our lungs are full of the remnants of smog due to the freeways and factories placed right next to us. We know that look in the eyes of a parent who is afraid that we will be harassed by a gang or the police or both. We know what it’s like to be the community where the city leaves its sex offenders—with no local services to support them—after they are released from jail.
To me, growing up in East Los Angeles also meant Spanish flowing all around me, even if my mother and I only spoke English to each other; living in what was a “food desert” to outsiders but eating tons of tacos de carne and sopa de res; hating it when cops loudly chased cholos through my backyard but still loving when those same men had loud house parties that echoed vibrantly throughout the neighborhood.
Do things need to be better in East L.A. and its sister communities in cities like Baltimore? Sure. The question is whether bringing in a scientist who doesn’t have a background in urban planning will lead to the improvements that are needed. In the Wired story, this question is not even asked—the glow that comes with our tendency to overestimate the possibilities technology offers allows the reader to comfortably assume this was a step in the right direction. But who benefits from his work matters. When scientists and technologists collaborate with cities, and when those cities have a long track record of abandoning their citizens, how can scientists be sure their work is being used for ethical purposes?
If astronomers at Johns Hopkins and its Space Telescope Science Institute are committed to improving the community around them, they would be better off working with grassroots community organizations also working to do this. There are plenty of people—scientists included—working to ensure the progress we make is ethical. In November, the inaugural Data for Black Lives conference was convened at MIT by academics and activists with the stated focus to consider: “Data as protest. Data as accountability. Data as collective action.” The Baltimore Housing Roundtable, for example, is a coalition of homeowners, renters, people with the experience of homelessness, nonprofit developers, community associations, religious institutions, policy experts, and university faculty brought together around human rights values to collaborate and transform community and economic development in Baltimore. They seek to do this by prioritizing community-controlled, permanently affordable housing as part of comprehensive community development—without the displacement that comes with rising rents and the unattainably high property values that normally follow.
When I spoke to Matt Hill at BHR, he told me that just two days before the Wired article came out, a black-led multiracial group of protesters had confronted the city housing commissioner over broken funding and development promises. This is the same office that commissioned the physics-based modeling system to identify vacant buildings because, according to the Wired piece, “The faster city officials can flag them, the sooner they can issue citations, auction, and redevelop the land.”
I asked the folks at BHR whether the new data crunch was even necessary. “Our members have intimate knowledge of where vacant properties are located because they live next to them and deal with the rat infestations and numerous other problems that come with vacants,” Peter Sabonis (also of BHR) told me.
To me, that implies that the new technology is redundant at best. And ensuring that the data created will be used ethically is not a given. “Too often we see the city trying primarily to follow the private market, e.g., prioritizing investments where there is private developer interest in market-rate housing or trying to create that private developer interest in market-rate housing,” Hill said. He told me that this has two effects: First, it deprioritizes the creation of housing that is truly affordable to existing residents in those neighborhoods, and second, it runs the risk of excluding entire swaths of the city—often predominately black areas—from investment if the “market” is not interested. “Data about vacants should serve the broader vision of racially equitable, community-led development without displacement as the starting point, not an afterthought,” Hill told me.
In December, the Atlantic published a piece on policing in gentrifying neighborhoods that sums up the reason that scientists like Budavári should be skeptical that the city will use their work to improve current residents’ lives: “Areas that are changing economically often draw more police—creating conditions for more surveillance and more potential misconduct.” Given the long history of cities enacting policies specifically harmful to people in underserved communities, it seems unscientific for any scientist to trust their intentions by handing over the data and hoping for the best. The Atlantic’s reporting makes clear that data analysis like Budavári’s could enhance the police’s ability to act as an occupying force. Even more troublingly, Budavári’s program also predicts “what the likelihood is that other nearby buildings may be vulnerable” to abandonment, as Wired put it, a step beyond assessing which buildings are already vacant.
It is up to scientists to be mindful of the kind of impact their work could have when building partnerships. It is up to scientists to ask pointed questions about what safeties are in place. This may seem unnatural to us because our scientific training rarely comes with any preparation in how to make these decisions. But it is here that we can learn from grassroots community organizations—Hill suggested that scientists wanting to get involved should sit down with them: “Engage in conversations that can bring these analytical tools to meet our human rights–based priorities. We know we don’t have all the answers. We also want scientists to learn about political economy, i.e., understand that data itself may be laden with race, class, and gender structural limitations.”
When we sit down at the table with people who carry another kind of expertise, we get to do something that scientists love: learn. If I hadn’t talked to Hill and Sabonis, it might never have occurred to me that the city already had a similar data set and that it’s important to ask if there is an opportunity cost associated with focusing on reproducing it. Maybe data science can help us build better communities. Perhaps it can be a way to make progress. But that will only happen if we consciously work toward that goal, taking the lead from the people most directly impacted.