Heliophysics Decadal Survey 2022 White Paper

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http://hdl.handle.net/11603/26550

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UMBC Goddard Planetary Heliophysics Institute (GPHI)
UMBC Faculty Collection

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Author/Creator ORCID

https://orcid.org/0000-0001-7537-3539

Date

2022

Type of Work

journal articles
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Citation of Original Publication

Narock, A., Bard, C., Thompson, B. J., Halford, A., McGranaghan, R., da Silva, D., et al. (2022). Heliophysics decadal survey 2022 white paper: Responsible machine learning in Heliophysics. Available at: http://surveygizmoresponseuploads.s3.amazonaws.com/fileuploads/623127/6920789/44-54a052e28c1e3bf27580e1d768044670_NarockAyrisA.pdf.

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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0

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Abstract

The use of machine learning (ML) and other advanced analytics methods in Heliophysics has grown steadily in recent years and will continue to do so. As their use in both research and operations become more prevalent, it is imperative that the community adopt a conscious effort to use these, often black-box, methods in an ethical manner. While initiatives to develop and promote such community standards are underway, they have yet to be widely adopted. Once these guidelines are fully codified and required, however, the responsible use of machine learning methods will encompass many aspects that add a burden to the researcher. Here we examine some of these aspects and how our institutions and organizations can support successful adoption of ethical and responsible artificial intelligence (AI) principles within the Heliophysics community.