The new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism for the content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool’s design, and summarize a number of points where further input from policymakers and researchers would be required.

新一波的“基础模型”——用于生成文本（例如，ChatGPT）或图像（例如，MidJourney）的通用生成人工智能模型——代表了人工智能技术水平的巨大进步。但它们的使用也带来了一系列新的风险，这引发了关于可能的监管机制的持续讨论。在这里，我们提出了一项应纳入立法的具体原则：任何开发供公众使用的基础模型的组织都必须证明其生成的内容具有可靠的检测机制，作为其公开发布的条件。检测机制应在工具中公开，允许用户查询任意内容项，该项是否是由模型（全部或部分）生成的。在本文中，我们认为这一要求在技术上是可行的，并将在减少许多领域新人工智能模型的某些风险方面发挥重要作用。我们还概述了该工具设计的一些选项，并总结了需要政策制定者和研究人员进一步投入的一些要点。