Frontier Safety Framework

An AI safety framework introduced by DeepMind designed to identify and mitigate potential future risks posed by advanced AI models.

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Frontier Safety Framework is a set of protocols proposed by Google DeepMind to proactively identify potential situations where future AI capabilities could lead to severe harm and establish mechanisms to detect and mitigate these risks. The framework focuses on model-level powerful capabilities, such as exceptional agency or sophisticated networking capabilities. It is intended to complement our alignment research, which aims to train models to act according to human values and societal goals, as well as Google's existing AI responsibility and safety practices.

Determine the potential for a model to cause serious harm.
Periodically evaluate cutting-edge models for reaching key capability thresholds.
Apply mitigation plans when models pass the warning assessment.
Based on four initial key capability levels: autonomy
biosecurity
cybersecurity
and machine learning research and development.
Tailor the intensity of mitigation measures to each key capability level.
Invest in frontier risk assessment science and continuously refine the framework.
Adhere to Google's AI Principles

This framework is aimed at AI researchers
developers
and organizations and policymakers concerned about AI safety and ethics. It offers a methodology for assessing and mitigating AI risks
helping them build safer and more human-aligned AI systems.

Used to evaluate the potential risks of AI models in the field of autonomous driving.
In drug discovery
ensure that AI model recommendations do not lead to unforeseen side effects.
In economic productivity enhancement
prevent AI model decisions from leading to unfair or unethical outcomes.

Step 1: Determine the potential for a model to cause serious harm.
Step 2: Periodically evaluate cutting-edge models for reaching key capability thresholds.
Step 3: Develop a warning assessment toolkit to alert when models approach key capability thresholds.
Step 4: Apply mitigation plans based on overall benefit-risk balance and expected deployment environment when models pass the warning assessment.
Step 5: Tailor the intensity of mitigation measures to each key capability level.

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