Leveraging machine learning to reduce cost & burden of reviewing research proposals at S&T agencies

Author: Ilana Harrus


With about $130 billion USD, the United States leads the world in federal research and development spending. Most of this spending is distributed by science and technology (S&T) agencies that use internal reviews to identify the best proposals submitted in response to competitive funding opportunities. As stewards of quality scientific research, part of each funding agency’s mission is to ensure fairness, transparency, and integrity in the proposal-review process. Manual proposal review is time-consuming and expensive, costing an estimated $300 million annually at the National Science Foundation alone. Yet at current proposal-success rates (between 5% and 20% for most funding opportunities), a substantial fraction of proposals reviewed are simply not competitive.

The next administration should initiate and execute a plan to advance machine learning to triage scientific proposals. This proposal presents a set of actions and a vision to diffuse machine-learning across science and technology agencies to ultimately become a standard component of proposal review, while improving the efficiency of the funding process without compromising the quality of funded research

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About the Author

Ilana Harrus is the Senior Program Associate of the Artificial Intelligence: Applications/Implications Initiative hosted by the American Association for the Advancement of Science (AAAS) Scientific Responsibility, Human Rights and Law Program. An astrophysicist by training, Ilana spent more than 15 years working at NASA and NSF including serving as Program Scientist for three space NASA missions and the lead for the R&D program of NASA Astrophysics. She is a PMI-certified Project Manager (PMP), has a PhD in Physics from Columbia University and a Master in Information Systems from University of Maryland, Baltimore County.