梳理59篇文档工具论文，发现阻碍数据集文档普及的四大根源。
From Reflection to Repair: A Scoping Review of Dataset Documentation Tools
- 通过混合方法分析59篇论文，揭示工具设计背后的动机与障碍。
- 发现文档价值模糊、设计脱离实际、忽视劳动成本等四类共性问题。
- 呼吁从个人工具转向制度化解决方案，适合关注负责任AI的研究者。
数据集文档被广泛认为是负责任自动化系统开发的关键。尽管已有多种文献支持文档工作，但对其工具设计动因及采纳障碍仍知之甚少。本文基于对59篇数据集文档相关研究的系统性回顾，结合混合方法分析，探讨了工具构建的动机、作者如何理解文档实践，以及这些工具与现有系统、法规和文化规范的关系。分析揭示出四个持续存在的文档概念化模式，可能阻碍采纳与标准化：文档价值的模糊操作化、脱离上下文的设计、未解决的劳动需求，以及将集成视为未来工作的倾向。基于此，本文主张在负责任AI工具设计中转向制度性而非个体性解决方案，并提出人机交互领域可采取的具体行动以推动可持续的文档实践。
原文摘要 · Abstract (English)
Dataset documentation is widely recognized as essential for the responsible development of automated systems. Despite growing efforts to support documentation through different kinds of artifacts, little is known about the motivations shaping documentation tool design or the factors hindering their adoption. We present a systematic review supported by mixed-methods analysis of 59 dataset documentation publications to examine the motivations behind building documentation tools, how authors conceptualize documentation practices, and how these tools connect to existing systems, regulations, and cultural norms. Our analysis shows four persistent patterns in dataset documentation conceptualization that potentially impede adoption and standardization: unclear operationalizations of documentation's value, decontextualized designs, unaddressed labor demands, and a tendency to treat integration as future work. Building on these findings, we propose a shift in Responsible AI tool design toward institutional rather than individual solutions, and outline actions the HCI community can take to enable sustainable documentation practices.
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