AI documentation is a rapidly-growing channel for coordinating the design of
AI technologies with policies for transparency and accessibility. Calls to
standardize and enact documentation of algorithmic harms and impacts are now
commonplace. However, documentation standards for AI remain inchoate, and fail
to match the capabilities and social effects of increasingly impactful
architectures such as Large Language Models (LLMs). In this paper, we show the
limits of present documentation protocols, and argue for dynamic documentation
as a new paradigm for understanding and evaluating AI systems. We first review
canonical approaches to system documentation outside the context of AI,
focusing on the complex history of Environmental Impact Statements (EISs). We
next compare critical elements of the EIS framework to present challenges with
algorithmic documentation, which have inherited the limitations of EISs without
incorporating their strengths. These challenges are specifically illustrated
through the growing popularity of Model Cards and two case studies of
algorithmic impact assessment in China and Canada. Finally, we evaluate more
recent proposals, including Reward Reports, as potential components of fully
dynamic AI documentation protocols.

本文阐述了目前 AI 文档标准的局限性，并提出动态文档作为评估 AI 系统的新范式。通过对比传统环境影响声明框架与目前算法文档标准之间的区别，以及结合模型卡和两个算法影响评估案例的分析，最后评估了动态 AI 文档规范的最新提议。