Heliophysics Decadal Survey 2022 White Paper

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dc.contributor.authorNarock, Ayris
dc.contributor.authorBard, Christopher
dc.contributor.authorThompson, Barbara J.
dc.contributor.authorHalford, Alexa
dc.contributor.authorMcGranaghan, Ryan
dc.contributor.authorSilva, Daniel da
dc.contributor.authorKosar, Burcu
dc.contributor.authorShumko, Mykhaylo
dc.date.accessioned2023-01-04T23:18:16Z
dc.date.available2023-01-04T23:18:16Z
dc.date.issued2022
dc.description.abstractThe 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.en_US
dc.description.sponsorshipThis LATEX position paper template was originally created by Alexa Halford and generalized for the Heliophysics Decadal Survey by Ryan McGranaghan.en_US
dc.description.urihttp://surveygizmoresponseuploads.s3.amazonaws.com/fileuploads/623127/6920789/44-54a052e28c1e3bf27580e1d768044670_NarockAyrisA.pdfen_US
dc.format.extent8 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2s8e9-zxmg
dc.identifier.citationNarock, 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.en_US
dc.identifier.urihttp://hdl.handle.net/11603/26550
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Goddard Planetary Heliophysics Institute (GPHI)
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis 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.en_US
dc.rightsPublic Domain Mark 1.0*
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.titleHeliophysics Decadal Survey 2022 White Paperen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0001-7537-3539en_US

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