  Science funding agencies (NASA, DOE, and NSF), the science community, and the
US taxpayer have all benefited enormously from the several-decade series of
National Academies Decadal Surveys. These Surveys are one of the primary means
whereby these agencies may align multi-year strategic priorities and funding to
guide the scientific community. They comprise highly regarded subject matter
experts whose goal is to develop a set of science and program priorities that
are recommended for major investments in the subsequent 10+ years. They do this
using both their own professional knowledge and by synthesizing details from
many thousands of existing and solicited documents.
  Congress, the relevant funding agencies, and the scientific community have
placed great respect and value on these recommendations. Consequently, any
significant changes to the process of determining these recommendations should
be scrutinized carefully. That said, we believe that there is currently
sufficient justification for the National Academies to consider some changes.
We advocate that they supplement the established survey process with
predictions of promising science priorities identified by application of
current Artificial Intelligence (AI) techniques These techniques are being
applied elsewhere in long-range planning and prioritization.
  We present a proposal to apply AI to aid the Decadal Survey panel in
prioritizing science objectives. We emphasize that while AI can assist a mass
review of papers, the decision-making remains with humans. In our paper below
we summarize the case for using AI in this manner and suggest small inexpensive
demonstration trials, including an AI/ML assessment of the white papers
submitted to Astro2020 and backcasting to evaluate AI in making predictions for
the 2010 Decadal Survey.
