This paper examines the current landscape of AI regulations, highlighting the divergent approaches being taken, and proposes an alternative contextual, coherent, and commensurable (3C) framework. The EU, Canada, South Korea, and Brazil follow a horizontal or lateral approach that postulates the homogeneity of AI systems, seeks to identify common causes of harm, and demands uniform human interventions. In contrast, the U.K., Israel, Switzerland, Japan, and China have pursued a context-specific or modular approach, tailoring regulations to the specific use cases of AI systems. The U.S. is reevaluating its strategy, with growing support for controlling existential risks associated with AI. Addressing such fragmentation of AI regulations is crucial to ensure the interoperability of AI. The present degree of proportionality, granularity, and foreseeability of the EU AI Act is not sufficient to garner consensus. The context-specific approach holds greater promises but requires further development in terms of details, coherency, and commensurability. To strike a balance, this paper proposes a hybrid 3C framework. To ensure contextuality, the framework categorizes AI into distinct types based on their usage and interaction with humans: autonomous, allocative, punitive, cognitive, and generative AI. To ensure coherency, each category is assigned specific regulatory objectives: safety for autonomous AI; fairness and explainability for allocative AI; accuracy and explainability for punitive AI; accuracy, robustness, and privacy for cognitive AI; and the mitigation of infringement and misuse for generative AI. To ensure commensurability, the framework promotes the adoption of international industry standards that convert principles into quantifiable metrics. In doing so, the framework is expected to foster international collaboration and standardization without imposing excessive compliance costs.

翻译：本文审视了当前人工智能监管格局，着重分析了各国采取的差异化路径，并提出了一种替代性的情境协调可通约（3C）框架。欧盟、加拿大、韩国和巴西采用横向或侧面路径，预设人工智能系统的同质性，试图识别共同危害成因，并要求实施统一的人工干预措施。而英国、以色列、瑞士、日本和中国则推行情境特定或模块化路径，根据人工智能系统具体应用场景量身定制监管规则。美国正在重新评估其战略方向，对控制人工智能存在性风险的支持日益增强。解决此类监管碎片化问题，对于确保人工智能系统的互操作性至关重要。欧盟人工智能法案当前呈现的比例性、粒度性和可预见性程度，尚不足以凝聚共识。情境特定路径虽更具潜力，但需在细节完善、协调一致性和可通约性方面进一步发展。为寻求平衡，本文提出混合型3C框架。在情境性方面，该框架根据使用模式与人类交互方式将人工智能分为自主型、分配型、惩罚型、认知型和生成型五类。在协调性方面，为每类分配特定监管目标：自主型人工智能侧重安全性；分配型人工智能需兼顾公平性与可解释性；惩罚型人工智能强调准确性与可解释性；认知型人工智能要求准确性、鲁棒性与隐私保护；生成型人工智能则需防范侵权与滥用。在可通约性方面，该框架推动采用国际行业标准，将原则转化为可量化指标。通过上述设计，该框架有望在不过度增加合规成本的前提下，促进国际合作与标准化进程。