Bridging the Global Divide in AI Regulation: A Proposal for a Contextual, Coherent, and Commensurable Framework

• URL: http://arxiv.org/abs/2303.11196v5
• Date: Mon, 15 Jul 2024 23:43:22 GMT
• Title: Bridging the Global Divide in AI Regulation: A Proposal for a Contextual, Coherent, and Commensurable Framework
• Authors: Sangchul Park,
• Abstract summary: This paper proposes an alternative contextual, coherent, and commensurable (3C) framework for regulating artificial intelligence (AI) To ensure contextuality, the framework bifurcates the AI life cycle into two phases: learning and deployment for specific tasks, instead of defining foundation or general-purpose models. To ensure commensurability, the framework promotes the adoption of international standards for measuring and mitigating risks.
• Score: 0.9622882291833615
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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