19 November 2019
by: Michelle Donahue
At a crossroads in her career as a professor of computer science at Bowie State University, Quincy Brown recalls watching senators asking questions about coding and programming during a hearing with a Cabinet secretary. Though fascinated by the computing-related line of inquiry, the questions seemed to be lacking.
“I could tell they didn’t know anything about what they were asking about,” Brown said. “And I thought, wouldn’t it be awesome if they had people working for them who knew something about this, so they could ask more intelligent questions.”
Yet this was in 2014—well into the era of high-powered computing. Soon after, Brown applied to the AAAS Science & Technology Policy Fellowship and spent two years (2014-16) as a fellow at the National Science Foundation (NSF). With a deep personal interest in expanding access to technology for minority and underrepresented groups, Brown had worked in NSF-funded programs at Bowie State and relished the opportunity to contribute from the other side of the fence.
“It was interesting to be able to bring my perspective of being on the awardee side to the policymaking side,” Brown said. “Even though it wasn’t a decision-making role, it’s important to bring that perspective of the community to the work, as most people who come into the office don’t have that background and aren’t from that community.”
For Ishmael Amarreh, who holds degrees in computational neurology as well as public policy from University of Wisconsin-Madison, the draw of public service was always strong.
The odd combination of his interests frequently confounded colleagues, he said, but he’d explain it by contrasting the two disciplines. Neurology, he would explain, is the study of how one brain works; public policy, the study of how many brains work together.
And despite being on track for a life in academia, Amarreh says he frequently pondered how he could parlay his computing expertise into a benefit for the public. The STPF program seemed to be a good place to start joining them together.
“As a citizen, I feel you have to be well-informed, and contribute to a well-informed public policy,” Amarreh said. “We all have shades of how we think about the world, our own domains of existence. But when we all come together—that’s what policy is.”
During his 2014-15 fellowship at the National Institute on Drug Abuse at the National Institutes of Health (NIH), Amarreh worked on a long-term longitudinal study of adolescent brain development. But the placement also gave him an opportunity to have discussions with leadership on how data science could be a powerful tool for improving equity in funding.
He cited a 2011 NIH study published in the journal Science, which found that African American applicants for NIH funding between 2000 and 2006 were 10 percentage points less likely than white applicants to be awarded research grants.
Machine learning and artificial intelligence algorithms are also potentially powerful tools to help reduce gaps in funding—as long as the algorithms themselves are developed carefully, with a multitude of worldviews as inputs.
“I’m interested in how policymakers can use data science to see which points are where we should pay attention to, and develop intervention for those points in the pipeline,” Amarreh said. “But algorithms are written from an experiential point of view, and we have to be sure that whatever we’re doing does not increase the disparity that already exists.”
Karna Desai, who trained as an astrophysicist at Indiana University Bloomington using big data to study planetary formation and movement, desired to leave academia, yet didn’t like the idea of going into the computing industry—he liked the idea of finding satisfaction through broad impact and a healthier work-life balance.
His placement as a 2018-19 executive branch fellow at NSF led him to apply for a second year. This year, he is a 2019-20 executive branch fellow at the State Department, where he hopes to continue having conversations with his colleagues about data science literacy, especially in the area of machine learning.
“Data science literacy needs to be part and parcel of your job, what data and artificial intelligence can and cannot do,” Desai said. “It’s similar to communication and writing – is that just one discipline? No. It’s omnipresent. Everyone uses phones, and email, and even though AI is still new, everyone is going to need to understand it. It will make everyone more informed decision-makers.”
Desai added that the broad and deep importance of data in the modern age means that policies related to its use and development have national and even international impacts – and described contributing to the conversation around data science as a noble cause.
“Though the government might be lower on salary, working for the public feels good after a month,” Desai said. “In short, even if you’re getting paid less than you might in industry, at the end of the day, you’re making a difference for all citizens. That’s extremely vital.”
Brown echoed Desai’s sentiment.
“I don’t know that anyone knows how to compete with the lucrative piece, but the potential to have impact in areas that computer scientists typically aren’t exposed to, or talk about in our career paths, is huge,” she said. “Industry and all its opportunities will still be there. This fellowship was an opportunity to spend a few years in a different space, and bring expertise to an agency or office that desperately needs it.”