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The coordination gap in frontier AI safety policies

Isaak Mengesha

TL;DR

Drawing on risk regimes in nuclear safety, pandemic preparedness, and critical infrastructure, it is proposed that similar mechanisms -- precommitment, shared protocols, and standing coordination venues -- could be adapted to frontier AI governance.

Abstract

Frontier AI Safety Policies concentrate on prevention -- capability evaluations, deployment gates, and usage constraints -- while neglecting institutional capacity to coordinate responses when prevention fails. We argue that this coordination gap is structural: investments in ecosystem robustness yield diffuse benefits but concentrated costs, generating systematic underinvestment. Drawing on risk regimes in nuclear safety, pandemic preparedness, and critical infrastructure, we propose that similar mechanisms -- precommitment, shared protocols, and standing coordination venues -- could be adapted to frontier AI governance. Without such architecture, institutions cannot learn from failures at the pace of relevance.

The coordination gap in frontier AI safety policies

TL;DR

Drawing on risk regimes in nuclear safety, pandemic preparedness, and critical infrastructure, it is proposed that similar mechanisms -- precommitment, shared protocols, and standing coordination venues -- could be adapted to frontier AI governance.

Abstract

Frontier AI Safety Policies concentrate on prevention -- capability evaluations, deployment gates, and usage constraints -- while neglecting institutional capacity to coordinate responses when prevention fails. We argue that this coordination gap is structural: investments in ecosystem robustness yield diffuse benefits but concentrated costs, generating systematic underinvestment. Drawing on risk regimes in nuclear safety, pandemic preparedness, and critical infrastructure, we propose that similar mechanisms -- precommitment, shared protocols, and standing coordination venues -- could be adapted to frontier AI governance. Without such architecture, institutions cannot learn from failures at the pace of relevance.
Paper Structure (11 sections, 2 figures)

This paper contains 11 sections, 2 figures.

Table of Contents

  1. Introduction
  2. The 'robustness gap' in the broader Ecosystem
  3. Consequences of ignoring robustness
  4. Tentative steps towards robust AI governance
  5. Introducing robustness thinking to FASP and other stakeholders
  6. Learning from other domains
  7. A tentative policy proposal
  8. Challenges and conditions for integration
  9. Why robustness is undersupplied
  10. Towards improving coordination
  11. Conclusion

Figures (2)

  • Figure 1: The robustness gap in frontier AI governance. Risk of harm accumulates downstream (top row). Opportunities for prevention concentrate upstream. Broader mitigation capacity remains underdeveloped. However, this determines society's capacity to adopt and learn.
  • Figure 2: The Scenario Response Registry (SRR) as a coordination mechanism. Scenarios are curated by an independent panel (1). Relevant actors file standardized if--then response plans (2). The SRR harmonizes filings to identify gaps and coordination opportunities (3). Together with complementary efforts—incident monitoring and preparedness frameworks—the SRR contributes to improved crisis readiness. Feedback from drills and real incidents updates the scenario library.