CAREER: HCC: Designing Visualizations to Support Critical Thinking and Calibrated Trust in Data

人们经常使用数据可视化来理解、交流和决策科学、教育、医疗保健和其他领域的重要问题。然而，数据可视化并不是中立的；关于可视化内容和可视化方式的选择会影响查看者对可视化结果的解释，特别是当他们无法访问底层数据时。这一事实提出了重要的问题，即人们如何以及是否信任他们与可视化数据的交互，以及可视化设计师如何帮助观众在可视化中建立适当的校准信任（不要过度信任不可靠的推断，也不要不信任有意义的关系）。该项目的目标是推进围绕可视化中信任校准的评估和设计的研究：在人-数据交互中创建适当的信任度量，构建可视化设计选择如何影响信任的模型，并为可视化的创建者和消费者制定设计指南，以帮助他们识别可视化数据中的信任和不信任迹象。项目团队还将创建可视化工具、教育材料和经验数据，以帮助研究人员、政策制定者和普通公众思考可信计算和可视化伦理。该项目围绕三个研究目标展开。首先，参考社会科学的理论和研究方法，项目团队将产生一系列方法来可靠地衡量用户对数据可视化的信任。其次，使用这些方法，团队将进行系统的一系列实证研究，以确定哪些可视化设计因素可以驱动批判性思维和校准信任。这些因素将包括使用与公共政策和金融相关的多个领域的真实数据集，对感知的清晰度、复杂性、准确性和数据量进行操纵。使用这些不同的数据将允许团队识别特定于领域的可视化元素，而不是一般化的可视化元素，从而促进批判性思维和校准信任。第三个目标是通过测试原型可视化和可视化系统对这些指导方针进行正式评估，从而促进校准信任。这将涉及实地研究和与研究人员、从业人员和公众成员的访谈，以检查信任指南的有效性，并确定可信赖的视觉数据通信的道德实践。该团队还将开发跨学科课程和倡议，将计算机科学、公共政策、心理学和行为经济学结合起来，以推进视觉数据通信的实践和教育。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

People often use data visualizations to understand, communicate, and make decisions about important questions in science, education, healthcare, and other domains. However, data visualization is not neutral; choices about what to visualize and how to visualize it can impact how a viewer interprets the visualization, especially when they don’t have access to the underlying data. This fact raises important questions about how and whether people trust their interactions with visualized data, and how visualization designers can help viewers develop appropriately calibrated trust (not overtrusting unreliable inferences nor undertrusting meaningful relationships) in the visualization. This project’s goal is to advance research around assessing and designing for trust calibration in visualizations: creating appropriate measures of trust in human-data interaction, building models of how visualization design choices can impact trust, and developing design guidelines for both creators and consumers of visualizations to help them identify signs of trust and mistrust in visualized data. The project team will also create visualization tools, educational materials, and empirical data to help researchers, policymakers, and members of the general public think about trustworthy computing and visualization ethics.The project is structured around three research objectives. First, referencing theories and research methodologies from social sciences, the project team will produce a collection of methods to reliably measure user trust in data visualizations. Second, using these methods, the team will conduct a systematic series of empirical studies that identify which visualization design factors can drive critical thinking and calibrated trust. These factors will include manipulations of the perceived clarity, complexity, accuracy, and amount of data, using real-world datasets across multiple domains related to public policy and finance. The use of such diverse data will allow the team to identify domain-specific versus generalizable visualization elements that promote critical thinking and calibrated trust. The third objective is a formal evaluation of these guidelines through testing prototype visualizations and visualization systems that facilitate calibrated trust. This will involve field studies and interviews with researchers, practitioners, and members of the general public to examine the effectiveness of the trust guidelines and identify ethical practices for trustworthy visual data communication. The team will also develop interdisciplinary coursework and initiatives that bring together computer science, public policy, psychology, and behavioral economics to advance practice and education around visual data communication.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.