Despite widespread calls for transparent artificial intelligence systems, the term is too overburdened with disparate meanings to express precise policy aims or to orient concrete lines of research. Consequently, stakeholders often talk past each other, with policymakers expressing vague demands and practitioners devising solutions that may not address the underlying concerns. Part of why this happens is that a clear ideal of AI transparency goes unsaid in this body of work. We explicitly name such a north star -- transparency that is user-centered, user-appropriate, and honest. We conduct a broad literature survey, identifying many clusters of similar conceptions of transparency, tying each back to our north star with analysis of how it furthers or hinders our ideal AI transparency goals. We conclude with a discussion on common threads across all the clusters, to provide clearer common language whereby policymakers, stakeholders, and practitioners can communicate concrete demands and deliver appropriate solutions. We hope for future work on AI transparency that further advances confident, user-beneficial goals and provides clarity to regulators and developers alike.

翻译：尽管社会各界广泛呼吁构建透明的人工智能系统，但该术语承载了过多含义各异的解读，难以表达精确的政策目标或指导具体的研究方向。由此导致利益相关方往往各说各话：政策制定者提出模糊诉求，而实践者设计的解决方案可能并未触及根本关切。这种现象的部分成因在于，当前相关研究中缺乏对AI透明度理想状态的清晰界定。我们明确提出了这样的北极星导向——以用户为中心、适应用户需求且诚实透明的透明度标准。通过开展广泛的文献调研，我们识别出多个具有相似透明度概念的簇群，并将每个簇群与我们的北极星导向相联系，分析其如何促进或阻碍理想的AI透明度目标。最后，我们探讨所有簇群的共通线索，旨在提供更清晰统一的语言框架，使政策制定者、利益相关方和实践者能够就具体需求进行沟通，并提供相适配的解决方案。我们期待未来关于AI透明度的研究能进一步推进以用户利益为核心的目标，为监管者与开发人员提供明晰指引。