Supporting responsible machine learning in heliophysics - Narock, Ayris; Bard, Christopher; Thompson, Barbara J.; Halford, Alexa J.; McGranaghan, Ryan M.; da Silva, Daniel; Kosar, Burcu; Shumko, Mykhaylo (Frontiers in Astronomy and Space Sciences, 2022)
Over the last decade, Heliophysics researchers have increasingly adopted a variety of machine learning methods such as artificial neural networks, decision trees, and clustering algorithms into their workflow. Adoption of these advanced data science methods had quickly outpaced institutional response, but many professional organizations such as the European Commission, the National Aeronautics and Space Administration (NASA), and the American Geophysical Union have now issued (or will soon issue) standards for artificial intelligence and machine learning that will impact scientific research. These standards add further (necessary) burdens on the individual researcher who must now prepare the public release of data and code in addition to traditional paper writing. Support for these is not reflected in the current state of institutional support, community practices, or governance systems. We examine here some of these principles and how our institutions and community can promote their successful adoption within the Heliophysics discipline.