【文章标题】： 23 low-regret recommendations for AI policy 关于人工智能政策的23条无悔建议
【文章正文】： Photo by
Joe Dudeck
on
Unsplash
照片由
Joe Dudeck
摄于
Unsplash
A few days ago, I published a guest post by Tim Fist and Saif Khan of the Institute for Progress, discussing the question of whether we should deliberately try to slow down the rate of AI progress:
几天前，我发表了一篇由进步研究所（Institute for Progress）的 Tim Fist 和 Saif Khan 撰写的客座文章，讨论了这样一个问题：我们是否应该刻意尝试放慢AI发展的速度？
The authors promised a raft of specific policy recommendations, and they didn’t disappoint. Here is part 2, with all of those recommendations. Did you know that 23 is my lucky number?
作者们承诺会提出一系列具体的政策建议，他们果然没有让人失望。这里是第2部分，包含所有这些建议。你知道吗，23是我的幸运数字？
In
our last post
, we evaluated the claims of a recent open letter by AI company employees calling for governments to “pace” frontier AI development. To summarize:
在
我们上一篇文章
中，我们评估了AI公司员工最近一封公开信的主张，该信呼吁政府为前沿AI发展“定速”。概括如下：
Rapid progress towards fully automated AI R&D has empirical support, but it’s less clear how much it will accelerate AI capabilities or pose severe risks.
向完全自动化的AI研发快速迈进有实证支持，但尚不清楚它会在多大程度上加速AI能力或带来严重风险。
Despite substantial uncertainty, we believe some preparatory policy action is warranted. This follows both from how serious the possible direct risks are and the risk that political backlash to AI-driven disruptions results in poorly-reasoned policy measures, such as broad bans on new data centers.
尽管存在巨大的不确定性，我们仍然认为采取一些预备性政策行动是合理的。这既是因为可能存在的直接风险有多么严重，也是因为对AI驱动的颠覆性影响的政治反弹可能导致考虑不周的政策措施，例如全面禁止新建数据中心。
If “pacing” becomes necessary, we think it should consist of two parts: first, specifying thresholds for when automated AI R&D is likely to pose severe risks; and second, if a threshold is exceeded, incentivizing AI companies to reallocate resources away from the most risky research, and towards activities that make further automation safer, or diffuse the benefits of existing AI faster.
如果“定速”确有必要，我们认为它应该包含两部分：第一，明确自动化AI研发何时可能构成严重风险的阈值；第二，如果超过阈值，激励AI公司重新分配资源，从最高风险的研究转向那些能让进一步自动化更安全的活动，或更快地传播现有AI的益处。
Without preparation now, however, our preferred pacing strategy will be impossible to implement. In this post, we’ll describe how the US can concretely prepare for the further automation of AI R&D and the risks it entails.
然而，如果现在不做好准备，我们偏好的定速策略将无法实施。在这篇文章中，我们将描述美国如何具体地为AI研发的进一步自动化及其带来的风险做好准备。
Still, we aren’t certain whether the benefits of pacing outweigh the downsides, especially given the risk that government regulation is implemented counterproductively. So to make policy preparation as targeted and low-regret as possible, we think any intervention should meet the following five criteria:
尽管如此，我们并不确定定速的好处是否大于坏处，尤其是考虑到政府监管可能适得其反的风险。因此，为了让政策准备尽可能有针对性且无悔，我们认为任何干预措施都应满足以下五个标准：
Target only AI development activities that could lead to serious and irreversible harms.
只针对可能导致严重且不可逆转伤害的AI开发活动。
Minimize any slowdown in the diffusion of existing AI capabilities, and ideally accelerate it.
尽量减少现有AI能力传播的放缓，理想情况下应加速传播。
Impose low costs, or deliver clear benefits, even if automated AI R&D and its attendant risks prove unlikely.
即使自动化AI研发及其相关风险最终被证明不太可能发生，也要施加低成本，或带来明确的收益。
Avoid systematically disadvantaging more cautious labs and countries.
避免系统性地使更谨慎的实验室和国家处于不利地位。
Avoid establishing a new regulatory apparatus that is likely to be misused (e.g., by concentrating power in a small set of companies).
避免建立一套可能被滥用的新监管体系（例如，将权力集中在少数公司手中）。
A surprisingly wide range of policy moves meet these criteria. We’ve identified 23 of them, and they span 7 areas:
令人惊讶的是，有非常广泛的政策举措符合这些标准。我们确定了23项，涵盖7个领域：
Transparency
: giving the government and public more visibility into automated AI R&D
透明度
：让政府和公众更深入地了解自动化AI研发
State capacity
: improving the government’s ability to understand and respond to automated AI R&D
国家能力
：提高政府理解和应对自动化AI研发的能力
Risk management
: developing a risk management strategy for automated AI R&D that accelerates defensive and commercial AI use
风险管理
：为自动化AI研发制定风险管理策略，加速防御性和商业性AI应用
Verification
: accelerating the development of AI verification technologies to enable agreements between mutually distrustful parties
验证
：加速AI验证技术的发展，使互不信任的各方能够达成协议
Resilience
: accelerating the development of technologies that improve society’s ability to withstand and recover from AI-driven disruptions
韧性
：加速发展能够提高社会抵御和恢复AI驱动颠覆性影响能力的技术
Competition with China
: extending the US AI lead over China to buy more time to manage risks and increase US leverage in international negotiations
与中国的竞争
：扩大美国对中国的AI领先优势，以争取更多时间管理风险，并增加美国在国际谈判中的筹码
Diplomacy
: creating option value for international cooperation to manage the risks of automated AI R&D
外交
：为管理自动化AI研发风险的国际合作创造期权价值
In the rest of this post, we’ll explain why we think policy action is justified across each area and give specific recommendations for each. For more details on this and the material from
yesterday’s
post, you can read our
full report here
.
在这篇文章的其余部分，我们将解释为什么我们认为在每个领域采取政策行动是合理的，并为每个领域提出具体建议。关于这一点以及
昨天
文章中的内容，更多细节可以阅读我们的
完整报告
。
Transparency
透明度
AI now regularly makes impressive
breakthroughs in math
and is superhuman at many aspects of software development and cybersecurity, with capabilities doubling every
7
and
5
months, respectively. But outside of frontier AI companies, how exactly the drivers of AI progress — e.g., curating more and better data, improving training algorithms, applying more reinforcement learning, simply making the model bigger — are unlocking AI capabilities is poorly understood.
AI现在经常在数学领域取得令人瞩目的
突破
，并且在软件开发和网络安全的许多方面超越人类，其能力分别每
7
个月和
5
个月翻一番。但在前沿AI公司之外，AI进步的驱动因素——例如，整理更多更好的数据、改进训练算法、应用更多强化学习、仅仅让模型更大——究竟是如何释放AI能力的，人们对此知之甚少。
The same is true for many of the crucial questions surrounding AI R&D automation. Much of the best information remains inside company walls.
围绕AI研发自动化的许多关键问题也是如此。大量最优质的信息仍停留在公司内部。
1
1
Given that automated R&D could rapidly accelerate AI progress with little warning, this dynamic could leave the government and public unprepared.
鉴于自动化研发可能在毫无预警的情况下迅速加速AI进步，这种动态可能让政府和公众措手不及。
It doesn’t have to be this way. If we want society to respond well, we’ll need much more information about what’s going on at the frontier of AI.
事情不必如此。如果我们希望社会能够良好应对，我们需要更多关于AI前沿正在发生什么的信息。
More transparency could help us understand the science behind automated AI R&D as well as what it looks like within specific companies (e.g., how much they’re automating, what policies they use to manage the risks, and any R&D-related incidents).
更高的透明度可以帮助我们理解自动化AI研发背后的科学，以及它在具体公司内部是什么样子（例如，他们在多大程度上实现了自动化，他们用什么政策来管理风险，以及任何与研发相关的事件）。
A recent NVIDIA-led letter
supported
open-weight models from an open science perspective — a valuable goal. But public disclosure practices around the science of and common practices within AI development (for both closed- and open-weight models) are minimal.
最近一封由英伟达牵头的信
支持
从开放科学的角度看待开放权重模型——这是一个有价值的目标。但围绕AI发展科学和常见实践（无论是封闭权重还是开放权重模型）的公开披露实践却少之又少。
2
2
Building on the findings of
CSET
and the
Elasticity Institute
, we suggest transparency measures for information in five categories:
基于
CSET
和
弹性研究所
的研究发现，我们建议对五类信息采取透明度措施：
The science of general AI progress.
通用AI进步的科学。
3
3
AI’s ability to automate specific AI R&D tasks.
AI自动化特定AI研发任务的能力。
4
4
Company progress toward automating AI R&D.
公司在自动化AI研发方面取得的进展。
5
5
Company AI R&D automation risk management practices and incidents.
公司AI研发自动化的风险管理实践和事件。
6
6
Company “model behavior specifications,” documents that describe the values and principles an AI model is trained to follow (e.g., OpenAI’s Model Spec for its GPT models and Anthropic’s Constitution for its Claude models).
公司的“模型行为规范”，即描述AI模型被训练遵循的价值观和原则的文件（例如，OpenAI为其GPT模型制定的Model Spec，以及Anthropic为其Claude模型制定的Constitution）。
7
7
The vast majority of this information is not subject to disclosure requirements, and so it is either disclosed voluntarily in a limited way or not at all.
这些信息绝大多数不受披露要求的约束，因此要么以有限的方式自愿披露，要么根本不披露。
8
8
Although some particularly sensitive information may only be suitable for disclosure to the US government, we generally recommend transparency measures that involve public disclosure.
尽管某些特别敏感的信息可能只适合向美国政府披露，但我们通常建议采取涉及公开披露的透明度措施。
As AI companies automate more of their R&D, this information would enhance public and policymaker understanding, improve policy responses, and enable the broader scientific community to do better work on alignment and security.
随着AI公司将其更多的研发自动化，这些信息将增强公众和政策制定者的理解，改进政策应对，并使更广泛的科学界能够在对齐和安全方面做得更好。
-
Frontier AI companies and relevant industry bodies should publicly share information relevant to trends and risks in AI R&D automation
-
前沿AI公司和相关行业机构应公开分享与AI研发自动化趋势和风险相关的信息
The categories of information outlined above would improve policymakers’ and the public’s understanding of the extent and nature of AI R&D automation at frontier AI companies, enabling outside experts to
model, project, and publish on
associated trends and impacts. In turn, this would improve policy responses and bolster the broader scientific community’s work on alignment and security.
上述信息类别将提高政策制定者和公众对前沿AI公司AI研发自动化程度和性质的理解，使外部专家能够
建模、预测并发表
相关趋势和影响。反过来，这将改进政策应对，并加强更广泛科学界在对齐和安全方面的工作。
Disclosing information about the science of AI and general AI capabilities would be a return to the historical norms of open scientific publication in the US AI industry. As recently as 2020, OpenAI
published
detailed information on the architecture, training recipe, and data of GPT-3, while in 2022, Google
published
detailed scaling laws showing how training compute, data, and model architecture correlate with AI model capabilities.
披露关于AI科学和通用AI能力的信息，将是对美国AI行业开放科学出版历史规范的回归。就在2020年，OpenAI
发布
了GPT-3架构、训练方法和数据的详细信息，而2022年，Google
发布
了详细的缩放定律，展示了训练算力、数据和模型架构与AI模型能力之间的相关性。
To reestablish this norm, employees at frontier AI companies should encourage their leadership to publicly share information in the categories outlined above, and advocate for reestablishing broader industry norms, including through industry bodies such as the
Frontier Model Forum
.
为了重建这一规范，前沿AI公司的员工应鼓励其领导层公开分享上述类别的信息，并倡导重建更广泛的行业规范，包括通过
前沿模型论坛
等行业机构。
We believe commercial and geopolitical concerns over the sensitivity of this information are manageable. Industry-level technical metrics related to the science of AI and general AI capabilities are likely well understood by most or all frontier AI companies in the US and China. They are therefore unlikely to alter the balance of AI capabilities between US AI companies and between the US and China. Additionally, specific company-level AI R&D automation activities are of extraordinary public interest, such that improving the US government’s and public’s ability to mount a policy response outweighs concerns over commercial sensitivity. Of course, some specific information on cutting-edge breakthroughs — particularly where non-US companies lack comparable knowledge — will be crucial to US strategic interests and AI leadership, and may thus be less suited to public disclosure.
我们相信，关于这些信息敏感性的商业和地缘政治担忧是可控的。与AI科学和通用AI能力相关的行业级技术指标，很可能已被美国和中国的大多数或所有前沿AI公司充分了解。因此，它们不太可能改变美国AI公司之间以及美中之间的AI能力平衡。此外，具体的公司级AI研发自动化活动具有极大的公共利益，因此提高美国政府和公众制定政策应对的能力，超过了商业敏感性的担忧。当然，一些关于前沿突破的具体信息——尤其是在非美国公司缺乏可比知识的情况下——将对美国的战略利益和AI领导地位至关重要，因此可能不太适合公开披露。
However, we do not recommend legal mandates requiring disclosure of this information. Legal mandates for public disclosure should focus on information related to risk management, as follows.
然而，我们不建议通过法律强制要求披露这些信息。公开披露的法律强制要求应侧重于与风险管理相关的信息，具体如下。
-
Congress should legislate transparency about automated AI R&D risk management, incident reporting, whistleblower protections, and model behavior specifications
-
国会应立法规定自动化AI研发风险管理、事件报告、举报人保护和模型行为规范的透明度
Several current federal bills include transparency-related requirements for internal deployment:
当前几项联邦法案包含与内部部署相关的透明度要求：
The
FRONTIER Act
, introduced in July 2026, includes the following three types of transparency and reporting obligations directly relevant to internally-deployed models (which are often used for automated AI R&D): (1) The developer’s publicly available frontier AI framework must describe how the developer reviews risk assessments in deciding to internally deploy the model; (2) The developer must periodically submit confidential reports to the Department of Commerce containing “a summary of an assessment of catastrophic risks arising from internal use of its frontier models”; and (3) The developer must report, within 72 hours, critical safety incidents involving its frontier models, including whether an incident was associated with internal use.
《
前沿法案
》（FRONTIER Act）于2026年7月提出，包含以下三类与内部部署模型（通常用于自动化AI研发）直接相关的透明度和报告义务：（1）开发者公开的前沿AI框架必须描述其在决定内部部署模型时如何审查风险评估；（2）开发者必须定期向商务部提交保密报告，其中包含“对其前沿模型内部使用所产生的灾难性风险评估摘要”；以及（3）开发者必须在72小时内报告涉及前沿模型的重大安全事件，包括事件是否与内部使用有关。
The
AI Incident Reporting Act
, introduced in June 2026, would require reports for internal-only AI models, including with respect to an internal model that “when unprompted, has demonstrated the ability to materially accelerate or automate the research, development, evaluation, engineering, or improvement of advanced artificial intelligence systems, including in ways that could significantly compress timelines for the development or deployment of more capable systems.”
《
AI事件报告法案
》（AI Incident Reporting Act）于2026年6月提出，将要求对仅限内部使用的AI模型进行报告，包括对于这样一个内部模型：“在无提示的情况下，已展现出实质性加速或自动化先进人工智能系统的研究、开发、评估、工程或改进的能力，包括可能显著压缩更强大系统开发或部署时间线的方式。”
Finally, the
AI Whistleblower Protection Act
, introduced in May 2025, would strengthen protections for whistleblowers regarding risky activities involving AI security vulnerabilities, legal violations, or dangers to public safety, public health, or national security.
最后，《
AI举报人保护法案
》（AI Whistleblower Protection Act）于2025年5月提出，将加强对举报人的保护，涉及AI安全漏洞、违法行为或对公共安全、公共卫生或国家安全构成危险的风险活动。
The bills described above would be effective in mandating public disclosure of frontier AI frameworks and government disclosure of incident reports for internally deployed models, as well as protecting whistleblowers. New legislation is necessary to mandate disclosure of model behavior specifications. Some bills, such as the FRONTIER Act, include much broader regulatory scope than public disclosure — but discussing those provisions is out of this report’s scope.
上述法案将在强制公开披露前沿AI框架、政府披露内部部署模型的事件报告以及保护举报人方面发挥有效作用。有必要通过新立法来强制披露模型行为规范。一些法案，如《前沿法案》，包含比公开披露广泛得多的监管范围——但讨论这些条款不在本报告范围之内。
State capacity
国家能力
Right now, the US government lacks the well-resourced institutions required to fully keep up with AI R&D automation, to inform politicians and frontier AI companies on how to respond, or to coordinate a pacing strategy.
目前，美国政府缺乏资源充足的机构，无法完全跟上AI研发自动化，无法为政治家和前沿AI公司提供如何应对的建议，也无法协调定速策略。
However, absent a state authority trusted by society at large, managing the pace of rapid AI capability improvement will be impossible. Only democratically accountable leaders possess the legitimacy and power to coordinate the kind of mass reallocation toward defense and diffusion that may be needed.
然而，如果缺乏一个被全社会广泛信任的国家权威机构，管理快速AI能力提升的步伐将是不可能的。只有对民主负责的领导者才拥有合法性和权力，来协调可能需要的向防御和扩散方向的大规模资源重新配置。
The government should therefore build institutions capable of responding effectively to rapid AI progress.
因此，政府应建立能够有效应对快速AI进步的机构。
9
9
-
Congress should resource the Center for AI Standards and Innovation (CAISI) with a budget of at least $84 million per year and empower it to directly advise senior government officials and frontier AI companies
-
国会应为AI标准与创新中心（CAISI）提供每年至少8400万美元的预算，并授权其直接向政府高级官员和前沿AI公司提供建议
Congress should provide CAISI with the manpower, resources, and autonomy required to generate real situational awareness of AI progress for policymakers and shape frontier AI companies