Transforming Science Prioritization Processes Using Artificial Intelligence

Artificial Intelligence (AI) and Machine Learning (ML) have potential to augment significantly the
current labor-intensive processes of science prioritization, specifically by the National Academies’
Decadal Survey on behalf of NASA and NSF. Here we summarize what we believe to be the first
exploratory demonstration-of-concept results from an application of AI/ML to Survey science
prioritization. Specifically, we applied Latent Dirichlet Allocation (LDA) and Natural Language
Processing (NLP) to reveal trends in published astrophysics research that may indicate science
priorities and which could be applied to strategic planning. For the purpose of the work that we
summarize here, AI/ML is able to analyze – that is, to “understand,” in a manner of speaking – a vast
amount of text to reveal complex relationships among research topics, including the growth or decline
of science community activities in those topics over time. We trained ourselves and AI/ML algorithms
by using ~400,000 abstracts in the period 1998 to 2010 to “forecast” the Academies’ Astro2010
recommendations and compare with the solicited white papers. Comparing our results with actual
Astro2010 recommendations allowed us to identify candidate metrics that better predicted the actual
results of the Survey. We found, for example, that Compound Annual Growth Rate (CAGR) of
papers published in a topic area is a good proxy measure for importance of this topic area of research.
With this training complete, we identified candidate astrophysics astrophysics science priorities for
the 2021+ period using the research during 2007 - 2019 . We conclude that appropriate application
of AI can potentially significantly reduce the current workload of the Decadal Survey processes and
reveal otherwise unrecognized characteristics in the body of astronomical research.

We emphasize throughout the exploratory nature of our work, encouraging colleagues to pursue
promising results further. Our most critical governing assumption was that increased (or decreased)
research activity can be used to identify scientific or technology topic areas worthy of increased (or
decreased) future emphasis. We discuss advantages, limitations, and recognize the “black box”
nature of our technique. We note ethics issues associated, for example, with using AI/ML to reveal
“hidden” meanings and biases in published work. Furthermore, inevitable improvements in AI may
soon enable widespread and welcome identification of and advocacy for science and technology
priorities by disparate and diverse groups and organizations. Consequently, we continue to urge a
near-term, in-depth evaluation of appropriate applications of AI, including implications and
consequences, as well as support for multiple follow-on assessments, of which ours is only a beginning.