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An algorithm helps a community detect leads

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More than six years after the 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 strengthen the the city’s economy. But still residents report a type of community-based PTSD, waiting in long lines of groceries fill up on bottled water and filters. Media reports Former Governor Rick Snyder was charged with negligence on Wednesday for his role in the crisis.

Snyder maintains his innocence, but he told Congress in 2016, “Local, state and federal officials – we’ve all let Flint’s families down.”

One tool that has emerged from the crisis is a form of artificial intelligence that could prevent similar problems in others towns where lead poisoning is a serious problem. BlueConduit, an analytics start-up that says it uses predictive modeling to find lead pipes, has offered promising results to Flint, but the city’s complex politics have prematurely ended its use.

Now, four years later and 100 miles away, officials in Toledo, Ohio are concerned about lead pipes, want to use technology. They hope to avoid the problems that have arisen in Flint by expanding awareness and community involvement. The Ohio Department of Health estimates that as many as 19,000 children in the state have high levels of lead; Children in Toledo have 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 paralyzing neurotoxin that can cause lifelong developmental problems in children and is toxic to adults even at low levels of exposure. Last year, Toledo embarked on a 30-year project to find and replace the 30,000 lead pipes in the city. In October, a coalition comprising 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.

Launched in 2019 by Jacob Abernethy and Eric Schwartz, BlueConduit was born 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: age of the home, neighborhood, proximity to other homes where lead has been found. been found, utility records, etc. Given a list of addresses, BlueConduit offers a ranking based on the probability of a service line lead. Cities can use the Ranking to prioritize homes for digs to examine pipes.

“You can think of that not so long as ‘these houses have lead, these houses are not,'” says Schwartz. “What we’re saying is, here’s the order in which the probabilities are ranked. And if your goal is to reduce the length of time people in the community live with lead pipes, this is how you should start browsing the list.

Alexis Smith, community program and technical associate at Freshwater Future, a nonprofit organization working with Toledo, says one of the attractions of Toledo’s approach is the contribution of residents, as well as the algorithms.

“It’s going to take knowledge of the owners and information not only from the city, but also from the residents,” she says. “It really reassured us to know that it’s not just something that’s going to happen to us. We are going to work within the framework of this program. “

Balancing technological and community perspectives is essential so that residents do not feel their concerns are secondary to algorithms. During the Flint Project, BlueConduit’s model offered promising results, but it encountered a divided community and a deep distrust of leadership.

In 2017, Schwartz and Abernethy, professors of marketing and IT, respectively, worked with executives at Flint who were initially impressed with the team’s predictive model. This year about 70 percent houses identified by the model were found to have lead pipes. The city then signed a deal with AECOM, an engineering company based in Los Angeles, which refused to use the pair’s predictive modeling. The following year, without the model, the accuracy dropped to around 15%.


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