This paper examines the use of training compute thresholds as a tool for governing artificial intelligence (AI) systems. We argue that compute thresholds serve as a valuable trigger for further evaluation of AI models, rather than being the sole determinant of the regulation. Key advantages of compute thresholds include their correlation with model capabilities and risks, quantifiability, ease of measurement, robustness to circumvention, knowability before model development and deployment, potential for external verification, and targeted scope. Compute thresholds provide a practical starting point for identifying potentially high-risk models and can be used as an initial filter in AI governance frameworks alongside other sector-specific regulations and broader governance measures.

翻译：本文探讨了将训练计算阈值作为人工智能系统治理工具的应用。我们认为，计算阈值应作为进一步评估AI模型的有价值触发条件，而非监管的唯一决定因素。计算阈值的关键优势包括：与模型能力和风险的相关性、可量化性、易于测量、抗规避性、在模型开发和部署前的可知性、可外部验证性以及针对性范围。计算阈值可作为识别潜在高风险模型的实用起点，并能够与其它特定领域监管措施及更广泛的治理方案相结合，在AI治理框架中发挥初始过滤作用。