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.

翻译：人工智能文档是一个快速发展的通道，用于协调人工智能技术的设计与透明度及可及性政策。如今，要求标准化并制定算法危害与影响的文档已成为普遍现象。然而，人工智能的文档标准仍处于萌芽阶段，未能匹配大规模语言模型等日益具有影响力的架构的能力和社会效应。本文揭示了当前文档协议的局限性，并论证了动态文档作为理解和评估人工智能系统新范式的必要性。我们首先回顾人工智能语境之外系统文档的经典方法，重点关注环境影响声明的复杂历史。接下来，我们将环境影响声明框架的关键要素与当前算法文档面临的挑战进行比较——后者继承了环境影响声明的局限性，却未吸纳其优势。这些挑战通过日益流行的模型卡片以及中国和加拿大两个算法影响评估案例研究得到具体说明。最后，我们评估了包括奖励报告在内的较新提议，将其视为完全动态人工智能文档协议的潜在组成部分。