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.

翻译：机器学习（ML）模型如今被广泛应用于医学、生物信息学及其他科学领域的复杂应用中。然而由于其黑箱特性，有时难以理解和信任模型提供的结果。这促使对提升ML模型信任度的可靠可视化工具的需求日益增长，过去数十年间已成为可视化领域的重要研究课题。为系统梳理该领域的研究现状并呈现前沿进展，我们撰写了关于利用交互式可视化增强ML模型信任度的最新技术综述（STAR）。我们定义并阐述了该主题的背景知识，提出了旨在实现这一目标的可视化技术分类体系，并探讨了未来研究方向的见解与机遇。其中一项贡献是扩展并改进了此前研究中对交互式ML不同维度信任度的分类方法。我们从不同分析视角对研究结果进行探讨：(a) 提供统计概览，(b) 总结关键发现，(c) 进行主题分析，以及(d) 探索各篇论文使用的数据集，所有分析均依托交互式网络调查浏览器完成。我们希望本综述能为致力于使ML模型更可信的可视化研究人员提供助力，同时也为其他学科的研究者和从业者寻找合适的可视化技术提供参考，帮助他们自信地解决任务并赋予数据以意义。