Distinguishing Predictive and Generative AI in Regulation

2025-06-19

AI Summary

This paper identifies a fundamental regulatory divergence between generative AI and predictive AI: generative AI’s inherent generality, evaluation intractability, legal-ecosystem reconfiguration, and distributed value-chain structure undermine conventional regulatory tools premised on task closure, risk measurability, and centralized accountability. Moving beyond prior work, the study systematically articulates four dimensions of regulatory heterogeneity unique to generative AI, critiques one-size-fits-all governance, and proposes a layered, target-specific regulatory framework coupled with cross-ecosystem constraints. Employing integrated methods—including policy analysis, techno-sociological inquiry, comparative AI governance assessment, and value-chain mapping—the research derives three actionable recommendations: (1) a dynamic regulatory targeting mechanism, (2) a full-stack responsibility allocation principle, and (3) a multi-stakeholder collaborative evaluation system. These contributions establish both a theoretical benchmark and an implementable pathway for global AI legislation. (149 words)

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Technology governance, policy, and regulations

Abstract

Over the past decade, policymakers have developed a set of regulatory tools to ensure AI development aligns with key societal goals. Many of these tools were initially developed in response to concerns with predictive AI and therefore encode certain assumptions about the nature of AI systems and the utility of certain regulatory approaches. With the advent of generative AI, however, some of these assumptions no longer hold, even as policymakers attempt to maintain a single regulatory target that covers both types of AI. In this paper, we identify four distinct aspects of generative AI that call for meaningfully different policy responses. These are the generality and adaptability of generative AI that make it a poor regulatory target, the difficulty of designing effective evaluations, new legal concerns that change the ecosystem of stakeholders and sources of expertise, and the distributed structure of the generative AI value chain. In light of these distinctions, policymakers will need to evaluate where the past decade of policy work remains relevant and where new policies, designed to address the unique risks posed by generative AI, are necessary. We outline three recommendations for policymakers to more effectively identify regulatory targets and leverage constraints across the broader ecosystem to govern generative AI.

Problem

• Distinguishing regulatory needs between predictive and generative AI
• Addressing unique risks and challenges posed by generative AI
• Evaluating relevance of existing policies for generative AI regulation

Innovation

• Identify generative AI's unique regulatory challenges
• Propose new policies for generative AI risks
• Leverage ecosystem constraints to govern AI