  As debates on potential societal harm from artificial intelligence (AI)
culminate in legislation and international norms, a global divide is emerging
in both AI regulatory frameworks and international governance structures. In
terms of local regulatory frameworks, the European Union (E.U.), Canada, and
Brazil follow a horizontal or lateral approach that postulates the homogeneity
of AI, seeks to identify common causes of harm, and demands uniform human
interventions. In contrast, the United States (U.S.), the United Kingdom
(U.K.), Israel, and Switzerland (and potentially China) have pursued a
context-specific or modular approach, tailoring regulations to the specific use
cases of AI systems. This paper argues for a context-specific approach to
effectively address evolving risks in diverse mission-critical domains, while
avoiding social costs associated with one-size-fits-all approaches. However, to
enhance the systematicity and interoperability of international norms and
accelerate global harmonization, this paper proposes an alternative contextual,
coherent, and commensurable (3C) framework. To ensure contextuality, the
framework (i) bifurcates the AI life cycle into two phases: learning and
deployment for specific tasks, instead of defining foundation or
general-purpose models; and (ii) categorizes these tasks based on their
application and interaction with humans as follows: autonomous, discriminative
(allocative, punitive, and cognitive), and generative AI. To ensure coherency,
each category is assigned specific regulatory objectives replacing 2010s
vintage AI ethics. To ensure commensurability, the framework promotes the
adoption of international standards for measuring and mitigating risks.
