Heliophysics Decadal Survey 2022 White Paper
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Author/Creator ORCID
Date
2022
Type of Work
Department
Program
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.
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
Public Domain Mark 1.0
Subjects
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.