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概述
If a model crosses a set threshold, the lab commits to adding stronger safeguards, or to not deploying or not training further until those safeguards are in place. These frameworks matter because they are currently the main way the most advanced AI models are governed for catastrophic risk, though most of them are voluntary and written by the labs themselves.
深入探讨
Anthropic published the first Responsible Scaling Policy in September 2023. It introduced AI Safety Levels (ASLs), loosely modeled on biosafety levels, where higher levels require stronger security and deployment safeguards. A later version, in October 2024, restated the policy in terms of capability thresholds and required safeguards and described the role of a Responsible Scaling Officer. OpenAI released its Preparedness Framework as a beta in December 2023. It scored risk in tracked categories, originally including CBRN threats, cybersecurity, persuasion and model autonomy. A 2025 revision reorganized it around High and Critical capability thresholds and dropped persuasion as a tracked category. Google DeepMind published its Frontier Safety Framework in May 2024 and has revised it since. It defines Critical Capability Levels (CCLs) that trigger response plans. At the AI Seoul Summit in May 2024, 16 companies signed the Frontier AI Safety Commitments. They agreed to publish safety frameworks, set thresholds for intolerable risk, and not develop or deploy a model if its risks could not be kept below those thresholds. Many frameworks were published before the Paris AI Action Summit in February 2025. Critics point to several weaknesses. Labs write their own thresholds and can revise them on their own. Wording such as "meaningful uplift" leaves room for interpretation. External verification is limited, and commercial competition gives labs an incentive to read results generously. A common misconception is that these frameworks are laws. Most are voluntary, although this is changing. California's SB 53 requires large frontier developers to publish such a framework, and the EU's General-Purpose AI Code of Practice, a voluntary route to showing compliance with the AI Act, asks signatories to adopt one.
战略影响
风险与安全
灾难性和日常的人工智能危害都取决于谁了解风险以及谁能够采取行动。
更清晰的判决
公众和专业素养决定强有力的安全政策在政治上是否可行。
打破炒作
清晰的解释可以减少炒作、实验室公关和模糊道德剧场的影响。
The Future of Frontier AI Safety Frameworks and Responsible Scaling Policies
Frameworks are moving from voluntary pledges toward regulatory expectations. California now requires large frontier developers to publish them, the EU's Code of Practice asks signatories to adopt them, and more jurisdictions may follow. Open questions include who audits compliance, how thresholds get standardized across labs, and whether evaluations can keep up with rapidly improving agentic systems. Independent testing bodies, including government AI institutes, may take on a bigger role in checking lab claims. The central tension will likely remain: labs design the rules they are judged by, which makes transparency and outside scrutiny important.
现实世界的实施
Before release, a lab tests whether a new model gives meaningful uplift to someone trying to acquire biological weapons. If it crosses the threshold, the lab adds stricter misuse filters and security before deployment.
In May 2025, Anthropic said it had activated ASL-3 deployment and security protections for Claude Opus 4 as a precaution, because it could not rule out that the model had reached the relevant capability threshold.
OpenAI's Preparedness Framework directs a Safety Advisory Group to review capability reports and advise leadership on whether safeguards are adequate before a release.
A lab tracking autonomous replication or AI research acceleration runs agentic evaluations. These test whether the model can complete long, multi-step tasks without human help.
风险与防护栏
将存在风险视为科幻小说，同时能力复合。
混淆了表面产品安全与高度自治下的对准。
只给非英语和非专业观众留下低质量的资源。
实施路线图
单独的产品危害、误用和失控/失调风险。
询问哪些证据会改变您对时间表和严重性的看法。
比起营销主张，更喜欢主要来源和具体评估。
确定一条行动路径：职业、政策、资金或技能——而不仅仅是意识。
不断探索
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常见问题
What is Frontier AI Safety Frameworks and Responsible Scaling Policies?
A frontier AI safety framework, such as a responsible scaling policy, is a published commitment by an AI lab to test its models for dangerous capabilities. If a model crosses a set threshold, the lab commits to adding stronger safeguards, or to not deploying or not training further until those safeguards are in place. These frameworks matter because they are currently the main way the most advanced AI models are governed for catastrophic risk, though most of them are voluntary and written by the labs themselves.
What is the basic logic of a responsible scaling policy?
These frameworks are built on if-then commitments that tie capability thresholds to required safeguards.
Which company introduced AI Safety Levels (ASLs)?
Anthropic's September 2023 Responsible Scaling Policy introduced ASLs, loosely modeled on biosafety levels.
What does Google DeepMind call the thresholds in its Frontier Safety Framework?
DeepMind uses Critical Capability Levels (CCLs), which trigger response plans when they are reached.
What did companies agree to at the AI Seoul Summit in May 2024?
The Frontier AI Safety Commitments were voluntary pledges to publish frameworks and to not proceed if risks could not be kept below the thresholds.
Which category did OpenAI's 2025 Preparedness Framework revision drop as a tracked category?
The revision reorganized the framework around High and Critical thresholds and removed persuasion as a tracked category.
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