Why you should care
Because he wants to change how tech companies deal in social good.
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Kuang Chen was a grad student when he descended on Dar es Salaam in a programmer’s cape, ready to save the day. In Tanzania on summer break from his doctoral program at UC Berkeley and with a plan to make clunky old nonprofits less inefficient, Chen figured he’d write a few SQL queries and be a hero.
But as any idealist could have guessed, that’s not exactly how things went. Chen’s eagerness and competence encountered some of the daily realities of work in the developing world. At his health clinic job, Chen found his colleagues were collecting massive amounts of data on paper as they dispatched community health workers to quiz residents of nearby towns and villages about their health practices. What’s a coder to do? Write an app that makes the whole thing move faster, right?
Wrong. Chen went the other way — in the direction of paperwork. Mounds and mounds of handwritten reports and surveys, some more legible than others, contained useful, even invaluable, data. But inputting all that data by hand was achingly slow and expensive, and rarely done. So that’s where Chen focused, on the chasm between the chickenscratch and the computer; he built system that bridges them easily. Today, that system is a high-tech startup called Captricity, which holds $50 million in funding from players including Silicon Valley’s Social Capital; Chen himself was on the MIT Technology Review’s list of 35 innovators under 35 in 2014. Oakland-based Captricity does on a large scale what Chen began doing that summer, seven years ago, in Tanzania: Using a combination of human and machine intelligence, it parses scribbled forms and paper piles, feeding them into neat electronic systems, allowing nonprofits, nongovernmental organizations and others on the low-tech end of the spectrum to actually play the data game.
Chen is a case study in how reverse innovation— the principle of designing based on the needs of the developing world — pays off. Why not work with limitations in the developing world, the reasoning goes, instead of just trying to slap an app on top of everything. What’s more, the company suggests how the benefits of reverse innovation can rebound to rich economies. Captricity’s client roster includes corporate giants like Dell, major insurers and the government agencies like the Federal Election Commission. The revenue Captricity pulls in from its private sector clients each year — which they won’t confirm, but say has tripled each year for three years — helps subsidize the software for nonprofit clients like the Abdul Latif Jameel Poverty Action Lab and the African Leadership Academy. The eventual goal, they say, is a dollar-for-dollar model: “The payoff isn’t profit, it’s karma,” Chen says. But who says there can’t be both?
Captricity’s idea itself is simple — plug paper into the modern world — but the technology to make it happen isn’t. Organizations with thousands of paper forms upload photos of their documents, snapped with basic smartphones or cameras, to a storage room in the cloud. Captricity then takes a “digital fingerprint” of each form using the same sort of image recognition that powers self-driving cars and Snapchat. Next, so-called microworkers — the lingo refers, creepily, to actual humans who grab gig work via Amazon’s Mechanical Turk crowdsourcing platform — mine the info to create pristine data sets one field at a time— e.g., name, date, or age — to preserve privacy and make the process more efficient. As the microworkers tell the computer what they see, the technology learns to see like the humans, and it snowballs. Eventually, computers take over and people decipher only the messiest of scribbles. Captricity claims a 99.9 percent accuracy rate.
“It’s the sort of rich combination of merging technologies that really stands out as Kuang’s technical achievement,” says data systems expert Joe Hellerstein, a professor of computer science at Berkeley and co-founder of Trifacta. Chen’s “synthesis effort” was something that hadn’t been done before, Hellerstein says. The impact can be huge for cash-strapped nonprofits, since evaluation can cost as much as project investment, according to a Stanford Social Innovation Review article by Dalberg, a global development consulting firm. Data-backed success can mean more funding, and more funding means the nonprofit gets to keep doing whatever (hopefully good) work it’s already doing. Like Young 1ove, a nonprofit that fights HIV in Botswana by educating teens on the health risks of sugar daddies. Over the course of 40 days, the organization surveyed 42,000 kids across much of the largely rural country, and all with paper forms. Using smartphones would have been too expensive and slow and would have required one-on-one interviews, not an option for taboo subjects like sex. It was a “gargantuan” task, says Noam Angrist, Young 1ove’s executive director, and with Captricity, Angrist and his team were able to avoid weeks of inputting all that data by hand. “Captricity is a dream for us,” he says.
Like any startup, Captricity will need to stay relevant. Since its launch in 2011, machine learning companies have become ubiquitous. And the tech to upgrade the paper economy might itself become obsolete: There’s the chance that big, paying clients will scrap paper for good and go fully digital, which means Chen will need to find more markets, says Hellerstein.
What keeps Chen up at night? His company’s legacy as a profitable social enterprise. He is, after all, the child of Chinese immigrants who fled the Cultural Revolution to become scientists in Kansas; the story he tells of himself is about doing Mother Teresa’s work through code. Like so many in Silicon Valley, he’ll have to fight for the right to wear that title.
This editorial article was originally created by OZY Media and published on OZY.com prior to, and independently of its inclusion in this JP Morgan Chase & Co. sponsored series. OZY Media claims the full rights and responsibilities of this article.
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