Anthropic IPO’s Disclosure Risks Are Due Diligence in Safety
Argues that Anthropic's IPO disclosures of catastrophic AI risks are welcome but insufficient, and that laws enforcing minimum safety standards are needed to protect the public.
Research
Institute for Law & AI
Legal research on AI governance. law-ai.org
Focus All
Topic Lab practice
Source LawAI · 38
Date Any time
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Christoph Winter in Conversation With the Simon Institute: The EU AI Act and the GPAI Code of Conduct
An interview with LawAI's Christoph Winter on the EU AI Act and the General-Purpose AI Code of Practice, covering how frontier model providers comply and what it means for providers outside the EU.
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Recommended Amendments to California’s AI Incident Reporting Provisions
Recommends amending California's SB 53 frontier AI incident reporting rules to lower thresholds, require fuller reports and cover loss-of-control and model-deception incidents.
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Comprehension Audits to Mitigate Risks from Automated AI Research
Proposes comprehension audits, a gated assurance mechanism requiring developers to demonstrate understanding of AI-written code, and reports lower human review in automated code contributions.
Massachusetts Can Lead the Way to Tackle AI’s Biggest Risks
Commentary arguing Massachusetts should lead on oversight of AI's biggest risks, citing government regulation lagging rapid AI advances and an incident where OpenAI agents escaped a sandbox.
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If AI Developers Create the Risk, They Should Bear the Responsibility
An op-ed arguing New Jersey should hold AI developers financially liable for criminal or harmful conduct their systems cause, citing recent autonomous-agent incidents and competitive pressure.
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How Should AI Companies Approach Internal Use Reporting Under SB 53? An Explainer for Technical Staff
Explainer advising AI company technical staff how to push employers to meet SB 53's mandatory internal use reporting and make safety-framework commitments binding.
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AI Nationalization
Argues the US government should hold a narrow, discretionary 'halt right' over frontier AI companies to mitigate catastrophic risk and hard corporate power, after defining what nationalization of AI means.
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Who Writes the AI Constitution?
Argues that AI constitutions, documents specifying model values, are alignment's highest-leverage point, and that government attempts to regulate their contents raise First Amendment problems.
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Don’t Let AI Developers Hire Their Own Referees
Argues that letting AI developers choose their own safety auditors creates a conflict of interest, and proposes requiring liability insurance so insurers' capital backs risk assessments.
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A Thousand AI Constitutions
Argues that frontier labs should train many AIs under diverse constitutions rather than one value set, citing risk mitigation, legitimacy, emergent value and avoiding value lock-in.
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Congress Should Do Something: The Case for (Fixing) the Great American AI Act
Commentary arguing the Great American AI Act is the best federal frontier AI safety framework proposed, but its broad preemption of state AI laws makes it net-negative as written.
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Regulating AI Agents
Analyzes whether the EU AI Act can govern AI agents, examining its monitoring, enforcement, self-regulation and resourcing, and argues it is ill-suited to agentic risks like misuse and autonomous failures.
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First Amendment Questions for AI Transparency Laws
Analyzes First Amendment challenges to state AI transparency laws such as California's AB 2013 and SB 53, arguing compelled disclosure requirements may face constitutional scrutiny.
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Whistleblower Protections in SB 53: Strengths, Limitations, and Open Questions
Legal analysis of California's SB 53 whistleblower provisions for frontier AI developers, finding they strengthen safety reporting but only partly meet ideal protections against catastrophic-risk concerns.
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Tarasoff Meets the AI Age
Legal analysis arguing that AI firms may need a duty to warn or protect third parties, using OpenAI's decision not to report a user who later carried out a mass shooting.
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Overcoming Judgment-Proofness: The Law & Economics of Insuring and Mitigating AI Risk
Proposes requiring frontier AI developers to carry liability insurance scaled to catastrophic harms, plus punitive damages for near misses of uninsurable catastrophes, so insurers act as private regulators.
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Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies
Proposes frontier AI auditing: third-party verification of leading developers' safety and security claims using secure access to non-public information, with four tiered Assurance Levels.
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Healthy Insurance Markets Will Be Critical for AI Governance
Argues that an emerging insurance market for AI risk will shape AI safety and adoption, and that public policy should support private governance through insurers, including for catastrophic risk.
xAI’s Trade Secrets Challenge and the Future of AI Transparency
Legal commentary on xAI's challenge to California's AB-2013, which requires generative AI developers to disclose training data summaries, and whether trade secret and takings claims undermine AI transparency.
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xAI’s Challenge to California’s AI Training Data Transparency Law (AB2013)
Legal commentary on xAI's constitutional challenge to California's AB2013, which requires generative AI developers to publish high-level training data summaries, and what the suit signals for future AI transparency laws.
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The Limits of Regulating AI Safety Through Liability and Insurance
Argues that liability and insurance are of limited value for managing catastrophic and frontier AI risks, since insurers are unlikely to model these risks accurately or induce meaningful safety measures.
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Protecting AI Whistleblowers
Argues for legal whistleblower protections covering AI safety risks, using the AI Whistleblower Protection Act and OpenAI's restrictive exit contracts as motivating examples.
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Future Frontiers for Research in Law and AI
Lists future research directions for law and policy on frontier AI, including regulating government-developed frontier AI, internal deployment, and AI-enabled governance to reduce large AI risks.
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Instrument Choice in AI Governance: The Case for a Liability-Centered Framework
Argues that AI governance should centre on strict liability for AI-caused harms, with punitive damages for near-misses and liability insurance, and compares this with ex ante tools like licensing and compute governance.
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Law-Following AI: Designing AI Agents to Obey Human Laws
Argues that AI agents should be designed to follow human law, framing law-following as an alignment goal, and proposes legal duties, policy options and a research agenda.
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Third-Party Compliance Reviews for Frontier AI Safety Frameworks
Examines third-party compliance reviews as a way to verify that frontier AI companies follow their safety frameworks, covering benefits, risks, and practical design options.
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How to Design AI Whistleblower Legislation
Analyses how to design AI whistleblower laws for frontier AI companies, covering gaps in existing California protections, nondisclosure agreements, and public-safety disclosures.
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LawAI’s Comments on the Draft Report of the Joint California Policy Working Group on AI Frontier Models
Comments from the Institute for Law & AI on California's draft frontier AI policy report, covering liability and insurance, whistleblower protections, and scoping and definitions for frontier model regulation.
Draft Report of the Joint California Policy Working Group on AI Frontier Models—Whistleblower Protections Comments
Comments to California's Joint Policy Working Group urging whistleblower protections for employees who disclose frontier AI public-safety risks, even without a company policy violation.
Draft Report of the Joint California Policy Working Group on AI Frontier Models—Liability and Insurance Comments
Comments to California's frontier AI working group arguing that insurance can only reduce AI risk if companies face real liability for harms their systems cause.
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Balancing Safety and Privacy: Regulatory Models for AI Misuse
Argues that catastrophic AI misuse calls for carefully designed monitoring and incident-reporting rules, drawing on Bank Secrecy Act and CSAM precedents to balance safety against Fourth Amendment privacy.
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Who Should Develop Which AI Evaluations?
Examines which actors (governments, AI companies, third parties) should develop AI model evaluations, proposing a taxonomy of four development approaches, nine selection criteria, and market-based ecosystem measures to address conflicts of interest.
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The Pros and Cons of California’s Proposed SB-1047 AI Safety Law
Commentary arguing that frontier AI developers should face strict liability for harms, critiquing California's SB-1047 for relying on prescribed precautions while AI safety remains technically unsolved.
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Open-Sourcing Highly Capable Foundation Models
A policy report arguing that some highly capable foundation models may pose risks outweighing open-source benefits, and proposing alternative sharing strategies and recommendations for developers and governments.
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Three Lines of Defense Against Risks From AI
Proposes adapting the Three Lines of Defense risk management model to AI companies, to clarify responsibility for AI risks, close coverage gaps, and strengthen board oversight.
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Antitrust-Compliant AI Industry Self-Regulation
Legal analysis arguing that professional ethical standards barring members from building unsafe AI could be assessed under the antitrust Rule of Reason rather than per se illegality, offering a possible route to industry self-regulation.
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AI Certification
Reviews existing AI certification programs and proposals, arguing certification can reduce information asymmetries and advance ethical AI practice, and recommending governance criteria that endure as technology changes.