2019 Government Innovation Awards
Risk reduction for grants management
For the past 65 years, the National Science Foundation has been the financial engine behind much of the nation’s technological innovations by funding the most promising research in a wide range of disciplines.
But funding requests are becoming more specialized and interdisciplinary, and NSF has struggled to identify outside experts who can evaluate such requests. Employees might have binders full of nuclear physicists on whom they can call, but they are less likely to know who could evaluate research into soil ecosystems, which requires expertise in chemical, biological and physical sciences along with data modeling, wireless communication, and cyber and physical systems.
To address that problem, a team of passionate volunteers from across the agency developed an artificial intelligence tool that uses machine learning and natural language processing algorithms to match keywords to relevant research expertise in a data lake of NSF’s research proposals and information from external publications and third parties. Semantic indexing separates search results into meaningful groups and provides users with a list of potential reviewers.
The Suggest Reviewer Dashboard was implemented in less than six months through the efforts of a cross-functional team of IT, business and human resources volunteers and has significantly reduced the administrative burden on grant administrators at no additional cost.
The data and tools required to build the dashboard would not have been available five years ago, so the convergence of technology, volunteers and passion created an opportune moment to solve a chronic problem. Approximately 40% of the NSF workforce uses the dashboard, and its popularity is growing. In addition, NSF has shared the technology and lessons learned with peer agencies.
NSF CIO Dorothy Aronson said the dashboard represented a significant win for the agency. “It let us dip our toe into the water with AI,” she added. “It was a low-risk project, but it addressed a problem that staff [was] passionate about solving and it delivered high yields.”
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