In reaction to growing concerns about the potential harms of artificial intelligence (AI), societies have begun to demand more transparency about how AI models and systems are created and used. To address these concerns, several efforts have proposed documentation templates containing questions to be answered by model developers. These templates provide a useful starting point, but no single template can cover the needs of diverse documentation consumers. It is possible in principle, however, to create a repeatable methodology to generate truly useful documentation. Richards et al. [25] proposed such a methodology for identifying specific documentation needs and creating templates to address those needs. Although this is a promising proposal, it has not been evaluated. This paper presents the first evaluation of this user-centered methodology in practice, reporting on the experiences of a team in the domain of AI for healthcare that adopted it to increase transparency for several AI models. The methodology was found to be usable by developers not trained in user-centered techniques, guiding them to creating a documentation template that addressed the specific needs of their consumers while still being reusable across different models and use cases. Analysis of the benefits and costs of this methodology are reviewed and suggestions for further improvement in both the methodology and supporting tools are summarized.

翻译：针对人工智能潜在危害的日益担忧，社会各界已开始要求提高AI模型及系统创建与使用过程的透明度。为应对这些关切，多项研究提出了包含模型开发者需回答问题的文档化模板。这些模板提供了有益的起点，但单一模板无法满足不同文档使用者的需求。然而，理论上可以建立一种可重复的方法论来生成真正有价值的文档。Richards等人[25]提出了这样一种方法论，用于识别特定文档需求并创建满足这些需求的模板。尽管这一方案颇具前景，但尚未经过评估。本文首次评估了这一以用户为中心的方法论在实践中的效果，报告了一个医疗AI领域团队采用该方法论提升多个AI模型透明度的经验。结果表明，未受过用户中心技术培训的开发者也能有效运用该方法论，指导其创建既满足使用者特定需求、又可在不同模型与用例间复用的文档模板。本文分析了该方法论的收益与成本，并总结了方法论及配套工具的改进建议。