Responsible Machine Learning in Heliophysics

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44-54a052e28c1e3bf27580e1d768044670_NarockAyrisA.pdf (233.51 KB)

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https://baas.aas.org/pub/2023n3i288/release/1

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

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UMBC Goddard Planetary Heliophysics Institute (GPHI)
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

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

Date

2023-07-31

Type of Work

journal articles
Text

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Citation of Original Publication

Narock, Ayris, Chris Bard, Barbara Thompson, Alexa Halford, Ryan McGranaghan, Daniel da Silva, Burcu Kosar, and Mykhaylo Shumko. “Responsible Machine Learning in Heliophysics.” Bulletin of the AAS 55, no. 3 (July 31, 2023). https://doi.org/10.3847/25c2cfeb.ce7c61a6.

Rights

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

Subjects

Abstract

Machine learning has become embedded in the field of Heliophysics. Ethical and responsible use of these methods encompasses many aspects that create necessary additional burden to the individual researcher and the research community as a whole. Sustained financial and infrastructural support from affiliated agencies and institutions is needed in addition to community-based governance strategies.