Using Artificial Intelligence to Augment Science Prioritization for Astro2020

TL;DR

The paper proposes augmenting the traditional science prioritization process for Astro2020 with AI techniques to improve long-term planning and decision-making, while emphasizing human oversight.

Contribution

It introduces a novel approach to incorporate AI predictions into the decadal survey process for science prioritization.

Findings

AI can assist in reviewing large volumes of scientific papers.

AI predictions can inform future science priorities.

Proposed small-scale trials to evaluate AI effectiveness.

Abstract

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.