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Every vote counts for this math student

Graduate student Ashwin Narayan takes off the fall semester to work on an election information database.
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Photo of Ashwin Narayan standing near some trees wearing an "I voted" knit hat
With just days before the election, math doctoral candidate Ashwin Narayan described his virtual workplace with a single word that few others working from home can use: Exciting.
Photo courtesy of Ashwin Narayan.
Photo of Ashwin Narayan standing before a landscape
“[T]his election, I really felt that I wanted to do something more, to be more active and work towards some immediate impact,” says graduate student Ashwin Narayan.
Photo courtesy of Ashwin Narayan.

Record voter turnouts are predicted in the U.S. elections this year, but will they arrive at the polls, or the early-voting ballot box, with informed opinions? And are more-informed voters more likely to vote? That’s a problem that math doctoral candidate Ashwin Narayan decided to work on this semester.

Narayan had moved home to New Jersey following MIT’s shutdown in the spring, and over the summer he started to look for work in progressive data science. “Because of all the Covid-related upheaval at MIT and in the world, I felt I would struggle with focusing on my thesis,” he recalls. Shifting the completion of his PhD to September 2021, he signed on at the nonpartisan national voter education organization BallotReady to work on its CivicEngine platform.

With just days before the election, Narayan described his virtual workplace with a single word that few others working from home can use: exciting.

“The adrenaline is pumping, the caffeine is flowing, the nerves are wracked, and the tension is high,” he says. “I’ve been interested in politics for quite a while, but it was definitely a passive interest, mostly just reading a lot of news. But this election, I really felt that I wanted to do something more, to be more active and work towards some immediate impact.”

Founded five years ago out of the University of Chicago, BallotReady works with customers, from state parties to companies like Snapchat and the Miami Heat, to help provide unbiased information about candidates and ballot initiatives in order to encourage and educate voters. In an internal analysis that BallotReady commissioned a few years ago from a team of researchers at MIT Department of Economics, they found that BallotReady users are 20 percentage points more likely to vote than nonusers, based on the turnout in Kentucky’s general election in 2015. The authors of that study, MIT postdoc Cory Smith and Enrico Cantoni and Donghee Jo, are working on more recent data to figure out how the site's tools affect turnout, says Narayan. 

Narayan’s role as an electoral fellow is to manage the interface between the campaigns and organizations and one of the country’s largest elections databases. Specifically, his focus is on the “Make a Plan to Vote” tool, which informs voters on how to vote by mail, drop box, or in person, whether by early vote or on Election Day.

“Questions about mail-in ballots and early voting have been highest on people’s minds,” says Narayan. “The database we have compiled now has data about mail-in voting regulations — how to get a ballot and when to return it by, locations for ballot drop boxes, and early voting polling locations for nearly every address in the country.”

Users can also access information about candidates and ballot questions using a customizable, mobile-friendly voter guide. To avoid bias, information is linked to a source, information is aggregated instead of interpreted, and candidates are listed in alphabetical order. The site also collects endorsements, a candidate’s experience, and stances on issues, based on what they’ve said in debates as covered in the news, or from the candidates’ websites. While BallotReady doesn’t monetize its voter facing site, the CivicEngine platform that Narayan is working on does sell products to drive turnout for its customers. 

“For me personally, I find the chance to work with such comprehensive data about elections in the U.S. a fascinating opportunity to shed light on how to make voting easier. We look at the presence of drop-box locations, the restrictiveness of mail-in policies, the number of candidates on a ballot, how many races are uncontested, and so on and so forth, and, based on post-election statistics on turnout, can draw connections between the number of voters and the policies.”

“The mission of BallotReady appealed to me; informing every voter about every aspect of their ballot is such a fundamental tenet of democracy that it should be entirely nonpartisan,” he adds. “It was important to me to work not only with a group of data scientists, but rather with a group that has deep knowledge about political campaigns, with policy, and organizing, who can motivate the key questions to ask with their deep domain knowledge.”

His work with BallotReady will extend through December. Because BallotReady launched in 2016 on a regional basis, and their 2018 expansion nationally was not in an election year, Narayan will be helping the company to debrief what information they collected, and to prepare for future elections. “We are hoping to analyze where our users come from, how popular our various tools are, and go through feedback from customers to figure out exactly what they liked about their data and hope for in future elections,” he says.

While he may have taken a semester off officially, his work is aligned with his studies, which focus on how policy and society interact with data science. He has taken law school courses addressing regulation around data and privacy, recently contributed an article on the impact of AI on existing health-care privacy protections for MIT Science Policy Review, and works with his advisor, Professor Bonnie Berger, to develop statistically motivated algorithms to analyze large biological datasets.

“I do think I was well-prepared for the work I’m doing now because of my research,” he says. “I’ve spent the past four years working with biologists to figure out the right questions to ask to better understand massive, noisy datasets, and the political world is extremely analogous: It’s not only that the data are noisy and hard to compile, but also it’s crucial to work with the experts to figure out the right questions.”

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