标题： 可视分析用于可解释和值得信赖的人工智能 显示英文标题

标题： Visual Analytics for Explainable and Trustworthy Artificial Intelligence

摘要： 我们的社会越来越依赖智能系统来解决复杂问题，从推荐系统建议接下来观看的电影到人工智能模型帮助住院患者的医疗诊断。 随着诊断准确性和效率的不断改进，人工智能在减少医疗误诊方面具有巨大潜力，每年可防止大量死亡并减少约4500亿欧元的经济负担。 然而，人工智能采用的一个关键障碍在于缺乏透明度：许多自动系统像“黑箱”一样运作，提供预测但不揭示底层过程。 这种不透明性可能阻碍专家对人工智能系统的信任和依赖。 可视化分析（VA）通过将人工智能模型与交互式可视化相结合，提供了一个有说服力的解决方案。 这些专业图表和图形使用户能够结合其领域专业知识来优化和改进模型，弥合人工智能与人类理解之间的差距。 在本工作中，我们定义、分类并探讨可视化分析解决方案如何在典型的AI流程各个阶段促进信任。 我们提出了一个创新可视化的设计空间，并介绍了我们之前开发的VA仪表板概述，这些仪表板支持流程各个阶段中的关键任务，包括数据处理、特征工程、超参数调整、理解、调试、优化和比较模型。 显示英文摘要

摘要： Our society increasingly depends on intelligent systems to solve complex problems, ranging from recommender systems suggesting the next movie to watch to AI models assisting in medical diagnoses for hospitalized patients. With the iterative improvement of diagnostic accuracy and efficiency, AI holds significant potential to mitigate medical misdiagnoses by preventing numerous deaths and reducing an economic burden of approximately 450 EUR billion annually. However, a key obstacle to AI adoption lies in the lack of transparency: many automated systems function as "black boxes," providing predictions without revealing the underlying processes. This opacity can hinder experts' ability to trust and rely on AI systems. Visual analytics (VA) provides a compelling solution by combining AI models with interactive visualizations. These specialized charts and graphs empower users to incorporate their domain expertise to refine and improve the models, bridging the gap between AI and human understanding. In this work, we define, categorize, and explore how VA solutions can foster trust across the stages of a typical AI pipeline. We propose a design space for innovative visualizations and present an overview of our previously developed VA dashboards, which support critical tasks within the various pipeline stages, including data processing, feature engineering, hyperparameter tuning, understanding, debugging, refining, and comparing models.