Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023

arXiv:2403.12005v1 Announce Type: cross
Abstract: Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and bioinformatics. After our 2020 state-of-the-art report comprising 200 techniques, we have persistently collected peer-reviewed articles describing visualization techniques, categorized them based on the previously established categorization schema consisting of 119 categories, and provided the resulting collection of 542 techniques in an online survey …

abstract analytics application art arxiv bioinformatics cs.hc cs.lg domains fields finance information machine machine learning medicine report state stat.ml trust trustworthy type visual visual analytics visualization