Anthropic ensures AI safety and reliability through several measures, including stress testing, safety protocols, and a responsible scaling policy[1]. They utilize AI safety levels (ASL), modeled after biosafety levels, to determine safety measures based on potential risks[2][3].
Key components of Anthropic's safety approach:
* Responsible Scaling Policy (RSP) Anthropic employs a system of AI Safety Levels (ASL)[3]. If an AI system demonstrates certain dangerous capabilities, Anthropic commits to not deploying it or training more powerful models until specific safeguards are implemented[3].
* Frequent Testing Anthropic tests frequently for dangerous capabilities at regular intervals to ensure that dangerous capabilities are not created unknowingly[3].
* Model Evaluations Designed to detect dangerous capabilities, these evaluations act as conservative "warning signs" to prevent accidentally exceeding critical safety thresholds[2]. Evaluations may consist of multiple difficulty stages, where later stages are run only if earlier evaluations show warning signs[2].
* Procedural Commitments The ASLs specify what must be true of Anthropic's models and security to allow safe training and deployment[2].
* Monitoring and Logging: For internal usage, generated outputs and corresponding inputs are logged and retained for at least 30 days. These logs are monitored for abnormal activity, and alarms are taken seriously and responded to promptly[2].
* Tiered Access: In limited cases, models with capabilities relevant to catastrophic harm may be available to a select group of vetted users with a legitimate and beneficial use-case that cannot be separated from dangerous capabilities, provided that access can be granted safely and with sufficient oversight[2].
* Vulnerability and Incident Disclosure: Anthropic engages in a vulnerability and incident disclosure process with other labs (subject to security or legal constraints) that covers red-teaming results, national security threats, and autonomous replication threats[2].
* Rapid Response to Model Vulnerabilities: When informed of a newly discovered model vulnerability enabling catastrophic harm, Anthropic commits to mitigate or patch it promptly[2].
* Two-Party Control: Applied to all systems involved in the development, training, hosting, and deployment of frontier AI models, this involves a system design where no single person has persistent access to production-critical environments; instead, they must request time-limited access from a coworker with a business justification[8].
* User Safety Features These include detection models to flag potentially harmful content, safety filters on prompts, and enhanced safety filters for users who repeatedly violate policies[7].
Anthropic also uses detection models to flag potentially harmful content and safety filters on prompts[7]. They are actively investing in and experimenting with additional safety features and providing tools to mitigate harm, and they encourage users to provide feedback on these measures[7].
Citations:
[1] https://myscale.com/blog/transformative-influence-anthropic-ai-safety-measures/
[2] https://www-cdn.anthropic.com/1adf000c8f675958c2ee23805d91aaade1cd4613/responsible-scaling-policy.pdf
[3] https://www.anthropic.com/news/uk-ai-safety-summit
[4] https://accelerationeconomy.com/ai/anthropic-leads-the-charge-in-ai-safety-and-performance/
[5] https://www.anthropic.com/news/core-views-on-ai-safety
[6] https://engineeringideas.substack.com/p/comments-on-anthropics-ai-safety
[7] https://support.anthropic.com/en/articles/8106465-our-approach-to-user-safety
[8] https://www.anthropic.com/news/frontier-model-security