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 advanced data science methods such as machine learning and neural networks. Such adoption 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 (AGU) have now issued (or will soon issue) ethical standards for artificial intelligence and machine learning in scientific research. However, 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. This is not reflected in the current state of institutional support: there must be additional sustained financial and infrastructural assistance to ease the burden of these requirements. We examine here some of these ethical principles and how our institutions can promote their successful adoption within the Heliophysics community via direct support.