The rapid advancement of artificial intelligence presents significant risks, including the potential for large-scale harm from increasingly capable systems. While Responsible Scaling Policies (RSPs) have emerged as a voluntary framework to manage these risks through development thresholds and safety requirements, there's currently no systematic way to evaluate how effective these policies actually are at preventing harm, coordinating industry efforts, or serving as regulatory templates.

Evaluating Responsible Scaling Policies

One approach could involve creating a comprehensive evaluation framework that examines RSPs across three key dimensions. First, it could assess how well these policies actually prevent catastrophic risks by analyzing their methodology for identifying and mitigating potential harms. Second, it could evaluate their ability to coordinate safety standards across competing AI organizations. Third, it could analyze whether RSPs might form a practical basis for future AI governance regulations.

The evaluation might combine:

• Theoretical analysis of RSP structures and their underlying assumptions
• Empirical data from organizations that have implemented RSPs
• Comparative studies against alternative governance approaches

Implementation and Stakeholders

For organizations considering this evaluation, a phased approach might work well. An initial literature review could establish baselines, followed by interviews with RSP implementers to gather real-world insights. The development of specific evaluation criteria could then lead to comparative analyses and practical recommendations.

Key stakeholders who might benefit from such an evaluation include:

• AI safety researchers needing to understand RSP effectiveness
• Companies deciding whether to adopt RSPs
• Policy makers considering regulatory frameworks
• Investors assessing AI-related risks

While primarily a research initiative, funding could potentially come from AI safety grants, consulting services for organizations developing RSPs, or specialized reports for institutional investors. The unique value would come from combining theoretical rigor with practical insights from early adopters, while maintaining appropriate confidentiality for proprietary information.