New research proposes a framework for evaluating general-purpose models against novel threats. The framework, co-authored by researchers from various prestigious institutions, aims to identify new capabilities and risks in AI systems at an early stage. As AI systems become increasingly powerful, it’s important to expand the evaluation process to include extreme risks from models that possess dangerous capabilities like manipulation, deception, and cyber-offense.
The framework emphasizes the evaluation of dangerous capabilities and alignment in general-purpose AI systems. By identifying these risks early on, developers can be more responsible when training and deploying AI systems. Evaluating for extreme risks involves assessing the model’s potential to threaten security, apply capabilities to cause harm, and detect alignment failures. These evaluations help developers understand if the ingredients for extreme risk are present in the model.
To ensure responsible development and deployment of AI, model evaluation is crucial. It helps companies and regulators make informed decisions about training and deploying potentially risky models. Additionally, model evaluation contributes to transparency and security measures. The research outlines a blueprint for embedding model evaluations into the decision-making processes of model training and deployment.
While model evaluation is an important part of ensuring AI safety, it’s not a solution for all risks, especially those influenced by external factors such as societal forces. Therefore, it should be combined with other risk assessment tools and a commitment to safety across various sectors.
The research highlights the need for technical and institutional progress in building a comprehensive evaluation process. Collaboration among AI researchers and stakeholders is crucial in developing approaches and standards for responsible AI development and deployment.
Overall, the proposed framework for evaluating extreme risks in AI models is an important step towards responsible AI development. It emphasizes the need to identify and address potential risks early on, ensuring that the benefits of AI technology can be realized safely.