The prevailing methodologies for visualizing AI risks have focused on technical issues such as data biases and model inaccuracies, often overlooking broader societal risks like job loss and surveillance. Moreover, these visualizations are typically designed for tech-savvy individuals, neglecting those with limited technical skills. To address these challenges, we propose the Atlas of AI Risks-a narrative-style tool designed to map the broad risks associated with various AI technologies in a way that is understandable to non-technical individuals as well. To both develop and evaluate this tool, we conducted two crowdsourcing studies. The first, involving 40 participants, identified the design requirements for visualizing AI risks for decision-making and guided the development of the Atlas. The second study, with 140 participants reflecting the US population in terms of age, sex, and ethnicity, assessed the usability and aesthetics of the Atlas to ensure it met those requirements. Using facial recognition technology as a case study, we found that the Atlas is more user-friendly than a baseline visualization, with a more classic and expressive aesthetic, and is more effective in presenting a balanced assessment of the risks and benefits of facial recognition. Finally, we discuss how our design choices make the Atlas adaptable for broader use, allowing it to generalize across the diverse range of technology applications represented in a database that reports various AI incidents.

翻译：当前可视化AI风险的主流方法主要关注数据偏见和模型误差等技术问题，往往忽视了更广泛的社会风险，如失业和监控。此外，这些可视化工具通常为技术熟练者设计，忽略了技术能力有限的群体。为应对这些挑战，我们提出了AI风险图谱——一种叙事式工具，旨在以非技术人员也能理解的方式，系统呈现各类AI技术相关的广泛风险。为开发和评估该工具，我们进行了两项众包研究。第一项研究包含40名参与者，明确了面向决策支持的AI风险可视化设计需求，并指导了图谱的开发。第二项研究包含140名在年龄、性别和族裔分布上反映美国人口特征的参与者，评估了图谱的可用性与美学表现，以确保其满足既定需求。以人脸识别技术为案例研究发现，相较于基线可视化方案，本图谱具有更优的用户友好性、更经典且富有表现力的美学设计，并能更有效地呈现对人脸识别技术风险与收益的平衡评估。最后，我们探讨了如何通过设计选择使图谱具备更广泛的适用性，使其能够适配报告各类AI事件的数据库中涵盖的多样化技术应用场景。