Responsible Scaling Policy
The Responsible Scaling Policy (RSP) is an AI safety governance framework developed by Anthropic to regulate the development and deployment of high-capability AI models. The framework classifies the potential risk capabilities of AI systems into multiple safety levels ranging from ASL-1 to ASL-4+, specifying corresponding evaluation requirements, safety standards, and deployment conditions for each level. Its purpose is to ensure that safety measures keep pace with advancing AI capabilities, preventing catastrophic or irreversible risks.
Core Facts
Anthropic's Responsible Scaling Policy (RSP) stipulates that once a model reaches a specific dangerous capability threshold, it must meet corresponding safety standards before training or deployment can continue.
Anthropic has implemented a Responsible Scaling Policy (RSP) that sets corresponding safety protection requirements based on model capability levels.
The Responsible Scaling Policy (RSP) was formally proposed by Anthropic in 2023, with its design inspired by the Biosafety Level (BSL-1 to BSL-4) grading system.
Timeline (last 90 days)
The Cyber Verification Program is linked to Anthropic's Usage Policy and Responsible Scaling Policy.
Major frontier AI labs have in recent years successively released Responsible Scaling Policies or similar safety frameworks, committing to take corresponding safety measures when model capabilities reach specific thresholds.
In September 2023, Anthropic released the first version of its 'Responsible Scaling Policy,' which assigns levels to models' potential risks and was the industry's first systematic dynamic safety mechanism.
Enforcement oversight of policies such as the RSP remains internal to the company, and external verification mechanisms are still underdeveloped.
Within the "Responsible Scaling Policy" framework, the authority to define thresholds, choose evaluation methods, and decide on result disclosure all remain inside the company, which critics argue is equivalent to letting athletes serve as their own referees.
The core logic of the "Responsible Scaling Policy" framework is that developers themselves set capability thresholds, which trigger corresponding safety evaluation obligations when a model reaches a specific risk level.
OpenAI's Responsible Scaling Policy specifies a series of evaluation trigger points, requiring safety evaluations to be completed before training or deployment can continue when a model reaches certain capability thresholds.
The core logic of the RSP is: before a model's capabilities reach a preset dangerous threshold, it must first be demonstrated that corresponding safety measures are in place; otherwise, further scaling of training must be paused.
Critics point out that the RSP is essentially corporate self-regulation, lacking external binding force, with Anthropic being both the rule-maker and the party being regulated.
The RSP categorizes model danger levels into AI Safety Level (ASL) 1 through 4, and specifies concrete evaluation requirements and deployment restrictions for each level.
16 more timeline events
All Facts (20)
Anthropic's Responsible Scaling Policy (RSP) stipulates that once a model reaches a specific dangerous capability threshold, it must meet corresponding safety standards before training or deployment can continue.
75%VerifiedAnthropic has implemented a Responsible Scaling Policy (RSP) that sets corresponding safety protection requirements based on model capability levels.
65%VerifiedThe Responsible Scaling Policy (RSP) was formally proposed by Anthropic in 2023, with its design inspired by the Biosafety Level (BSL-1 to BSL-4) grading system.
65%UnverifiedAnthropic released its Responsible Scaling Policy (RSP) in 2023, whose design logic is highly similar to OpenAI's Preparedness Framework.
70%UnverifiedSeveral leading laboratories have published their respective Responsible Scaling Policies or Preparedness Frameworks, setting risk-level thresholds for model capabilities.
70%UnverifiedThe Responsible Scaling Policy was first formally proposed by Anthropic in 2023.
70%UnverifiedAnthropic adopts 'Responsible Scaling' as its core philosophy
70%UnverifiedThe Cyber Verification Program is linked to Anthropic's Usage Policy and Responsible Scaling Policy.
50%UnverifiedMajor frontier AI labs have in recent years successively released Responsible Scaling Policies or similar safety frameworks, committing to take corresponding safety measures when model capabilities reach specific thresholds.
50%UnverifiedEnforcement oversight of policies such as the RSP remains internal to the company, and external verification mechanisms are still underdeveloped.
50%UnverifiedWithin the "Responsible Scaling Policy" framework, the authority to define thresholds, choose evaluation methods, and decide on result disclosure all remain inside the company, which critics argue is equivalent to letting athletes serve as their own referees.
50%UnverifiedThe core logic of the "Responsible Scaling Policy" framework is that developers themselves set capability thresholds, which trigger corresponding safety evaluation obligations when a model reaches a specific risk level.
50%UnverifiedOpenAI's Responsible Scaling Policy specifies a series of evaluation trigger points, requiring safety evaluations to be completed before training or deployment can continue when a model reaches certain capability thresholds.
50%UnverifiedThe core logic of the RSP is: before a model's capabilities reach a preset dangerous threshold, it must first be demonstrated that corresponding safety measures are in place; otherwise, further scaling of training must be paused.
50%UnverifiedThe RSP categorizes model danger levels into AI Safety Level (ASL) 1 through 4, and specifies concrete evaluation requirements and deployment restrictions for each level.
50%UnverifiedCritics point out that the RSP is essentially corporate self-regulation, lacking external binding force, with Anthropic being both the rule-maker and the party being regulated.
50%UnverifiedThese responsible scaling policies and safety frameworks are all voluntarily developed by companies, with inconsistent standards, and their implementation and verification rely on the companies themselves.
50%UnverifiedCompanies such as Anthropic and OpenAI have each published responsible scaling policies or safety frameworks that set capability thresholds and specify the mitigation or disclosure measures to be taken upon reaching those thresholds.
50%UnverifiedAnthropic has repeatedly emphasized the importance of external evaluation in its Responsible Scaling Policy.
50%UnverifiedOrganizations such as Anthropic and Google DeepMind have already practiced similar 'Responsible Scaling Policies' internally.
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