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Chapter 4 · 2025

How “AI Safety” is Leveraged Against Regulatory Oversight

Timnit Gebru, Émile P. Torres, Dylan Baker

Abstract

This paper critically examines how the discourse around 'AI safety' is sometimes used to resist or undermine effective regulatory oversight of AI systems. It argues that an overemphasis on speculative, long-term risks can distract from immediate, tangible harms and delay the implementation of necessary regulations. The authors advocate for a more robust and proactive regulatory approach that prioritizes public accountability and democratic control over AI development and deployment.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper directly addresses one of the core structural challenges in Zone III deployments. The research on AI Safety, Regulatory Oversight, AI Governance provides evidence-based foundations that enterprise architects cannot ignore when designing long-horizon autonomous workflows. The findings challenge the assumption that a base language model — however capable — can handle the complexity of durable, governed, multi-step execution without explicit architectural intervention. For Zone III practitioners, this paper belongs in the required reading list.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

AI SafetyRegulatory OversightAI GovernanceAI EthicsPublic Policy