提出七条原则，让AI成本与算力核算更可信、难钻空子。
Practical Principles for AI Cost and Compute Accounting
- 设计核算标准需防企业刻意规避监管
- 避免惩罚负责任的模型风险控制行为
- 确保跨公司跨地区执行一致
政策制定者越来越将开发成本和算力消耗作为人工智能能力与风险的代理指标。近期立法已引入针对特定阈值的模型或开发者监管要求。然而，当前在如何实施核算方面存在技术模糊性，导致监管漏洞，削弱了政策效力。本文提出了七条设计AI成本与算力核算标准的原则，旨在（1）减少策略性规避空间，（2）避免对负责任的风险缓解行为产生抑制，（3）实现企业间与司法管辖区间的统一实施。
原文摘要 · Abstract (English)
Policymakers increasingly use development cost and compute as proxies for AI capabilities and risks. Recent laws have introduced regulatory requirements for models or developers that are contingent on specific thresholds. However, technical ambiguities in how to perform this accounting create loopholes that can undermine regulatory effectiveness. We propose seven principles for designing AI cost and compute accounting standards that (1) reduce opportunities for strategic gaming, (2) avoid disincentivizing responsible risk mitigation, and (3) enable consistent implementation across companies and jurisdictions.
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