Reinsuring AI: Energy, Agriculture, Finance & Medicine as Precedents for Scalable Governance of Frontier Artificial Intelligence

• URL: http://arxiv.org/abs/2504.02127v1
• Date: Wed, 02 Apr 2025 21:02:19 GMT
• Title: Reinsuring AI: Energy, Agriculture, Finance & Medicine as Precedents for Scalable Governance of Frontier Artificial Intelligence
• Authors: Nicholas Stetler,
• Abstract summary: This paper proposes a novel framework for governing such high-stakes models through a three-tiered insurance architecture.<n>It shows how the federal government can stabilize private AI insurance markets without resorting to brittle regulation or predictive licensing regimes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The governance of frontier artificial intelligence (AI) systems--particularly those capable of catastrophic misuse or systemic failure--requires institutional structures that are robust, adaptive, and innovation-preserving. This paper proposes a novel framework for governing such high-stakes models through a three-tiered insurance architecture: (1) mandatory private liability insurance for frontier model developers; (2) an industry-administered risk pool to absorb recurring, non-catastrophic losses; and (3) federally backed reinsurance for tail-risk events. Drawing from historical precedents in nuclear energy (Price-Anderson), terrorism risk (TRIA), agricultural crop insurance, flood reinsurance, and medical malpractice, the proposal shows how the federal government can stabilize private AI insurance markets without resorting to brittle regulation or predictive licensing regimes. The structure aligns incentives between AI developers and downstream stakeholders, transforms safety practices into insurable standards, and enables modular oversight through adaptive eligibility criteria. By focusing on risk-transfer mechanisms rather than prescriptive rules, this framework seeks to render AI safety a structural feature of the innovation ecosystem itself--integrated into capital markets, not external to them. The paper concludes with a legal and administrative feasibility analysis, proposing avenues for statutory authorization and agency placement within existing federal structures.

Related papers

• A Framework for the Private Governance of Frontier Artificial Intelligence [0.0]
  The paper presents a proposal for the governance of frontier AI systems through a hybrid public-private system. Private bodies, authorized and overseen by government, provide certifications to developers of frontier AI systems on an opt-in basis. In exchange for opting in, frontier AI firms receive protections from tort liability for customer misuse of their models.
  arXiv  Detail & Related papers  (2025-04-15T02:56:26Z)
• A proposal for an incident regime that tracks and counters threats to national security posed by AI systems [55.2480439325792]
  We propose a legally mandated post-deployment AI incident regie that aims to counter potential national security threats from AI systems.<n>Our proposal is timely, given ongoing policy interest in the potential national security threats posed by AI systems.
  arXiv  Detail & Related papers  (2025-03-25T17:51:50Z)
• In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI [93.33036653316591]
  We call for three interventions to advance system safety.<n>First, we propose using standardized AI flaw reports and rules of engagement for researchers.<n>Second, we propose GPAI system providers adopt broadly-scoped flaw disclosure programs.<n>Third, we advocate for the development of improved infrastructure to coordinate distribution of flaw reports.
  arXiv  Detail & Related papers  (2025-03-21T05:09:46Z)
• Liability and Insurance for Catastrophic Losses: the Nuclear Power Precedent and Lessons for AI [0.0]
  This paper argues that developers of frontier AI models should be assigned limited, strict, and exclusive third party liability for harms resulting from Critical AI Occurrences (CAIOs) Mandatory insurance for CAIO liability is recommended to overcome developers' judgment-proofness, winner's curse dynamics, and leverage insurers' quasi-regulatory abilities.
  arXiv  Detail & Related papers  (2024-09-10T17:41:31Z)
• AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies [88.32153122712478]
  We identify 314 unique risk categories organized into a four-tiered taxonomy. At the highest level, this taxonomy encompasses System & Operational Risks, Content Safety Risks, Societal Risks, and Legal & Rights Risks. We aim to advance AI safety through information sharing across sectors and the promotion of best practices in risk mitigation for generative AI models and systems.
  arXiv  Detail & Related papers  (2024-06-25T18:13:05Z)
• Taxonomy to Regulation: A (Geo)Political Taxonomy for AI Risks and Regulatory Measures in the EU AI Act [0.0]
  This work proposes a taxonomy focusing on (geo)political risks associated with AI. It identifies 12 risks in total divided into four categories: (1) Geopolitical Pressures, (2) Malicious Usage, (3) Environmental, Social, and Ethical Risks, and (4) Privacy and Trust Violations.
  arXiv  Detail & Related papers  (2024-04-17T15:32:56Z)
• A Safe Harbor for AI Evaluation and Red Teaming [124.89885800509505]
  Some researchers fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. We propose that major AI developers commit to providing a legal and technical safe harbor. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.
  arXiv  Detail & Related papers  (2024-03-07T20:55:08Z)
• The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
  The development and regulation of AI seems to have reached a critical stage. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. This paper analyses the most advanced legal proposal, the European Union's AI Act.
  arXiv  Detail & Related papers  (2023-11-03T12:51:37Z)
• Dual Governance: The intersection of centralized regulation and crowdsourced safety mechanisms for Generative AI [1.2691047660244335]
  Generative Artificial Intelligence (AI) has seen mainstream adoption lately, especially in the form of consumer-facing, open-ended, text and image generating models. The potential for generative AI to displace human creativity and livelihoods has also been under intense scrutiny. Existing and proposed centralized regulations by governments to rein in AI face criticisms such as not having sufficient clarity or uniformity. Decentralized protections via crowdsourced safety tools and mechanisms are a potential alternative.
  arXiv  Detail & Related papers  (2023-08-02T23:25:21Z)
• Frontier AI Regulation: Managing Emerging Risks to Public Safety [15.85618115026625]
  "Frontier AI" models could possess dangerous capabilities sufficient to pose severe risks to public safety. Industry self-regulation is an important first step. We propose an initial set of safety standards.
  arXiv  Detail & Related papers  (2023-07-06T17:03:25Z)
• Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety [69.59465535312815]
  Regulatory Markets for AI is a proposal designed with adaptability in mind. It involves governments setting outcome-based targets for AI companies to achieve. We warn that it is alarmingly easy to stumble on incentives which would prevent Regulatory Markets from achieving this goal.
  arXiv  Detail & Related papers  (2023-03-06T14:42:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.