AI 中文总结
研究围绕数据可视化中的道德行事展开，通过对20位相关人员访谈，获取68个价值观并分九类，丰富了道德反思词汇，为可视化伦理应用提供更广泛基础。
AI 中文摘要
我们在可视化以及设计它们的人员身上重视什么？尽管关于批判性数据可视化的工作日益增多，但对进行符合道德的数据可视化工作的概念往往狭隘（例如认为仅通过避免公然说谎或操纵数据来履行道德职责），或与潜在有问题的隐含价值结构纠缠（如数据客观性和中立性的假设，以及设计师只是尽可能高效被动传达数字的角色）。然而，数据可视化中道德行事的含义广泛且多面，作为数据可视化研究人员和设计师应追求的美德值得阐明。我们对20位经验丰富的数据可视化研究人员、从业者和数据艺术家进行了访谈研究，以征求他们在可视化工作中的价值观和道德考量。我们报告了在访谈中遇到的68个价值观，分为九个美德类别。这些美德和价值共同描述了数据可视化中关心和关注事项的多样空间：从对将可视化作为说服工具的最佳使用的不安，到可视化从业者在职业责任和个人道德承诺之间常走的钢丝。这些美德本身以及受访者对道德实践的思考，为数据可视化的从业者、研究人员和教育工作者提供了更丰富的道德反思词汇，并为考虑和应用可视化伦理提供了更广泛的基础。
英文摘要
What do we value in our visualizations, and in the people who design them? Despite a growing body of work on critical data visualization, the conception of what it is to do ethical data visualization work can often be narrow (for instance, holding that our ethical duties are discharged merely by avoiding overtly lying or manipulating data), or entangled with potentially problematic implicit value structures (such as the assumption of the objectivity and neutrality of data, and so the designer's role being merely the passive conveying of numbers as efficiently as possible). Yet, what it means to act ethically in data visualization is broad and multifaceted, and the virtues to which we should aspire as data visualization researchers and designers are worth explicating. We conducted an interview study with a broad spectrum of 20 experienced data visualization researchers, practitioners, and data artists to solicit their values and ethical considerations around doing visualization work. We report on a list of 68 values, organized into nine virtue clusters, that we encountered in our interviews. These virtues and values together describe a diverse space of matters of care and concern in data visualization: from the unease around the best use of visualization as a tool for persuasion, to the tightrope that visualization practitioners often walk between their professional responsibilities and their personal moral commitments. The virtues themselves, as well as our interviewees' reflections on ethical practice, offer practitioners, researchers, and educators in data visualization a richer vocabulary for ethical reflection and provide a broader foundation for considering and applying visualization ethics.
