  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
browser. In this survey article, we present the updated findings of new
analyses of this dataset as of fall 2023 and discuss trends, insights, and
eight open challenges for using visualizations in machine learning. Our results
corroborate the rapidly growing trend of visualization techniques for
increasing trust in machine learning models in the past three years, with
visualization found to help improve popular model explainability methods and
check new deep learning architectures, for instance.
