More than six years after residents of Flint, Michigan, suffered widespread lead poisoning from their drinking water, hundreds of millions of dollars have been spent to improve water quality and bolster the city’s economy. But residents still report a type of community PTSD, waiting in long grocery store lines to stock up on bottled water and filters. Media reports Wednesday said former governor Rick Snyder has been charged with neglect of duty for his role in the crisis.
Snyder maintains his innocence, but he told Congress in 2016, “Local, state and federal officials—we all failed the families of Flint.”
One tool that emerged from the crisis is a form of artificial intelligence that could prevent similar problems in other cities where lead poisoning is a serious concern. BlueConduit, an analytics startup that says it uses predictive modeling to find lead pipes, offered promising results in Flint, but the city’s complex politics ended its use prematurely.
Now, four years later and 100 miles away, officials in Toledo, Ohio, facing concerns about lead pipes, want to use the technology. They hope to avoid the problems that surfaced in Flint by expanding community outreach and involvement. The Ohio Department of Health estimates that as many 19,000 children in the state have elevated levels of lead; children in Toledo tested positive for lead poisoning at nearly double the statewide rate, according to a 2016 report from the Toledo Lead Poisoning Prevention Coalition.
Lead is a crippling neurotoxin that can cause lifelong developmental problems in children and is toxic to adults even at low exposure levels. Last year, Toledo committed to a 30-year project to find and replace the estimated 30,000 lead pipes in the city. In October, a coalition including the city, local activists, and a nonprofit group received a $200,000 grant from the Environmental Protection Agency to use BlueConduit’s technology to find lead pipes.
Started in 2019 by Jacob Abernethy and Eric Schwartz, BlueConduit grew out of a University of Michigan project to identify lead pipes in Flint. Abernethy says the startup has contracts with organizations governing 50 cities to help replace lead pipes.
BlueConduit uses statistical techniques to predict which neighborhoods and households are most likely to have lead pipes, based on dozens of factors: the age of the home, the neighborhood, proximity of other homes where lead has been found, utility records, and more. Given a list of addresses, BlueConduit offers a ranking based on the likelihood of a lead service line. Cities can use the ranking to prioritize homes for excavations to examine the pipes.
“You can think of this not so much as ‘These homes have lead, these homes don’t,’” Schwartz says. “What we’re saying is, here’s the rank ordering of probabilities. And if your goal is reducing the amount of time people in the community are living with lead pipes, this is the way you should start going down the list.”
Alexis Smith, community program and technical associate at Freshwater Future, a nonprofit working with Toledo, says one appeal of Toledo’s approach is the input from residents, as well as the algorithms.
“It’s going to take the knowledge of homeowners and information not just from the city, but from the residents,” she says. “It really put our mind at ease that this isn’t just something that’s going to happen to us. We’re going to be working as a part of this program.”
Balancing tech and community perspectives is essential so residents don’t feel as though their concerns are secondary to algorithms. During the Flint project, BlueConduit’s model offered promising results, but it was met with a divided community and deep mistrust in leadership.
In 2017, Schwartz and Abernethy, professors of marketing and computer science, respectively, worked with Flint officials, who were initially impressed by the team’s predictive model. That year roughly 70 percent of the homes identified by the model turned out to have lead pipes. The city later signed a deal with AECOM, a Los Angeles-based engineering firm, that declined to use the pair’s predictive modeling. The following year, without the model, accuracy dropped to roughly 15 percent.
In a court declaration, Schwartz said he sent numerous emails to AECOM staff, including Alan Wong, the project lead. The emails went unanswered. Asked why AECOM didn’t use the model, Wong told city council members that his team was only offered a “heat map” of potential lead pipe locations and that he couldn’t comment on the accuracy of either AECOM’s work or the predictive model. (A spokesperson for AECOM told WIRED the company does not currently hold a contract for pipe replacement and referred questions to the city of Flint.)
While some community members wanted a return to the model, former mayor Karen Weaver told The Atlantic in 2019 that residents voiced frustration when the model directed the city to inspect neighbors’ homes and not their own. Council members also voiced concerns when workers tested more pipes in certain wards of the city than others; some people felt they were being skipped.
Schwartz says the prioritization of homes for pipe replacement in Flint included factors such as “the number of children under 6, the presence of pregnant mothers, military veterans or senior citizens.”
In an interview, Weaver says the community reaction was less about a lack of trust in the model and more about a lack of trust in the government. “We didn’t want to use this method because we didn’t want to miss anyone,” she says.
When Flint began using the predictive model in 2018, trust in city officials had eroded after four years of neglect and obfuscation. The EPA knew about unsafe lead levels months before the crisis garnered widespread media attention, as did several city employees and at least one consulting firm contracted with Flint. Governor Snyder, who appointed the emergency managers that made the decision to change the source of Flint’s water, claimed repeatedly he’d been misled about the safety of the water.
“When you’ve been screaming for 18 months that something’s wrong with the water and you have to wait for someone else to come in and validate your reality, you don’t trust,” Weaver says.
Flint has since resumed using BlueConduit’s model. At the end of 2020, nearly 10,000 lead pipes in Flint had been replaced. But that required excavations at 26,000 homes. Many cities would balk at the costly notion of house-by-house excavations, preferring to prioritize areas with lead pipes, making BlueConduit’s system of ranking and prioritization more sensible. Excavating and replacing pipes can cost $2,000 to $10,000, typically paid for by homeowners, either directly (if they pay out of pocket) or indirectly (if the utility company pays the upfront cost then raises water rates).
Abernethy says BlueConduit’s model tries to account for the fact that older homes, whose residents tend to be poorer, often have incomplete records. He says BlueConduit asks cities it is working with to collect a random sample of households, not just offer data on the homes for which the city already has records.
“When you have racial segregation or other kinds of distributional issues in a city, it’s very important they collect a random sample so they can get unbiased estimates of where lead is,” he says. “If they only go to places they have records for, it’s going to reinforce those existing biases.”
Projects are never as simple as following an AI’s judgments. Community residents must trust their leaders not to rely too much on technology. The communities most impacted by environmental concerns like lead have faced decades of income inequality and segregation. Mistrust in city leaders goes back much further than a few years.
Mark Riley, the administrator for Toledo’s water distribution division, says the key to balancing community perspectives and predictive tech is “continual engagement“ with the people of Toledo. “Our plan is to have meetings to share results with the community, and develop essentially a public-facing map showing our results,” he says.
Over the next year, Freshwater Future plans a three-step approach based on education. Homeowners have to consent to having their pipes excavated, so they plan to use the model to guide education efforts and reach out to residents where the model points to lead pipes. From there, they can discuss filtering, health, and of course, granting access for excavation.
“There’s no absolute reliance on machine learning, but we do think it can help target funding to the biggest needs,” Freshwater Future executive director Jill Ryan says. “Of course, if residents don’t like it, they can go to the city, and it’s our work to be there and help them. This isn’t intended to be one and done.”
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