  Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to
their black box nature, however, it may sometimes be hard to understand and
trust the results they provide. This has increased the demand for reliable
visualization tools related to enhancing trust in ML models, which has become a
prominent topic of research in the visualization community over the past
decades. To provide an overview and present the frontiers of current research
on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in
ML models with the use of interactive visualization. We define and describe the
background of the topic, introduce a categorization for visualization
techniques that aim to accomplish this goal, and discuss insights and
opportunities for future research directions. Among our contributions is a
categorization of trust against different facets of interactive ML, expanded
and improved from previous research. Our results are investigated from
different analytical perspectives: (a) providing a statistical overview, (b)
summarizing key findings, (c) performing topic analyses, and (d) exploring the
data sets used in the individual papers, all with the support of an interactive
web-based survey browser. We intend this survey to be beneficial for
visualization researchers whose interests involve making ML models more
trustworthy, as well as researchers and practitioners from other disciplines in
their search for effective visualization techniques suitable for solving their
tasks with confidence and conveying meaning to their data.
