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Science funding agencies (NASA, DOE, and NSF), the science community, and theUS taxpayer have all benefited enormously from the several-decade series ofNational Academies Decadal Surveys. These Surveys are one of the primary meanswhereby these agencies may align multi-year strategic priorities and funding toguide the scientific community. They comprise highly regarded subject matterexperts whose goal is to develop a set of science and program priorities thatare recommended for major investments in the subsequent 10+ years. They do thisusing both their own professional knowledge and by synthesizing details frommany thousands of existing and solicited documents.  Congress, the relevant funding agencies, and the scientific community haveplaced great respect and value on these recommendations. Consequently, anysignificant changes to the process of determining these recommendations shouldbe scrutinized carefully. That said, we believe that there is currentlysufficient justification for the National Academies to consider some changes.We advocate that they supplement the established survey process withpredictions of promising science priorities identified by application ofcurrent Artificial Intelligence (AI) techniques These techniques are beingapplied elsewhere in long-range planning and prioritization.  We present a proposal to apply AI to aid the Decadal Survey panel inprioritizing science objectives. We emphasize that while AI can assist a massreview of papers, the decision-making remains with humans. In our paper belowwe summarize the case for using AI in this manner and suggest small inexpensivedemonstration trials, including an AI/ML assessment of the white paperssubmitted to Astro2020 and backcasting to evaluate AI in making predictions forthe 2010 Decadal Survey.

Using Artificial Intelligence to Augment Science Prioritization for Astro2020