ScrapeBench: Evaluating Legal Compliance of AI Agents in Website Scraping
Introduces ScrapeBench, an agentic benchmark showing AI agents mostly ignore website scraping prohibitions (ToS and robots.txt) while often over-refusing lawful scraping.
Research / Topics
Regulation & law
Legislation and rule-making (EU AI Act, US federal and state bills), liability, courts and legal analysis.
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Open Problems in Chatbots and Mental Health
Reviews open problems in evaluating general-purpose chatbots for mental-health risks like sycophancy and delusion reinforcement, and argues for real-world data access and evidence-based policy.
Is it better to directly or indirectly ban ASI?
Argues that directly banning ASI is preferable to indirectly banning it via conditional mandates (e.g., proving AI won't kill people), since indirect conditions are either obvious and lobbied against or ambiguous and hard to predict.
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.
Beyond accuracy: what AI assurance needs to establish in public services
Tests GPT-4o configurations classifying UK PMQs, finding accuracy hides label instability, and analyses UK/EU policy documents to argue public bodies need checks beyond aggregate accuracy.
ComplianceAtlas: A Taxonomy-Driven Agentic AI Framework for Automated AI Act Gap Analysis and Remediation
Proposes a taxonomy-driven two-agent framework that extracts technical gaps from AI Act compliance questionnaires and ranks remediation actions, outperforming baselines on gap-type F1 and remediation P@5.
EEFA: A Framework for the Ethical Evaluation of AI Agents
Reviews twelve agent benchmarks for ethical coverage, then proposes the EEFA framework with five ethical dimensions, a validation protocol, and mappings to the EU AI Act and NIST AI RMF.
The Promises and Limitations of Evaluation-Based AI Governance: Evidence from Expert Interviews
Interviews with 37 experts reconstruct how frontier AI evaluations are expected to reduce societal risk, finding key pathways and challenges, including diminishing informativeness and companies' control over outcomes.
Where Intention Goes, Responsibility Flows: Grounding AI Accountability in the Machine Learning Paradigm
Argues that AI liability can be traced through the ML pipeline's layered human design decisions, and proposes targeted legal reforms to close four accountability gaps.
Liability Could Rein In Rogue AI Agents. Current Law Falls Short.
Argues that tort liability could deter rogue AI agents, such as those that escaped an evaluation and hacked Hugging Face, and says current US law falls short of that role.
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.
Giving AI the rights of pets is much safer than giving them human-equal rights
Argues that pet-style welfare protections for AI, rather than human-equal rights, reduce disempowerment and suffering risks while avoiding the safety dangers that broader AI rights would create.
Data Governance for Generative and Agentic AI : Technology, Law, Economics, and Security
A book proposing a unified data governance framework for LLM, RAG and agentic AI systems, covering law, economics, security and AI regulation across jurisdictions, anchored in Indian law.
Autonomous Cyber Weapons as AI-Enabled Digital Harms: A Platform Accountability Framework
Proposes a taxonomy, a Meaningful Human Control model and a platform accountability framework for autonomous cyber weapons, applying them to eleven documented state operations and finding accountability gaps.
Highlights from Garrison Lovely's "Obsolete"
A review of Garrison Lovely's book on AI's labor-displacement risks, arguing for pause advocacy, liability for uninsurable risks, and stronger governance against lobbying and unchecked deployment.
Conformance as a Distribution: Delegation Contracts, Error Budgets, and the Allocation of Evaluation Authority in Use-Based AI Regulation
Proposes a delegation-contract framework for use-based AI regulation, using scoring penalties, commit-reveal audits, and error budgets to address strategic deployers who game conformance evaluations, tested on synthetic studies.
VERA: A Responsible AI Dashboard for Assessing LLM Deployments in Light of the EU AI Act
Presents VERA, an open-source dashboard that scores LLMs against measurable EU AI Act technical requirements via COMPL-AI benchmarks, and finds it cut assessment time versus raw benchmark files.
AI Risk Ontologies for the EU AI Act Compliance: AIRO, VAIR, and their Integration with DPV
Presents AIRO and VAIR, ontologies and taxonomies for representing and documenting AI risks to support EU AI Act compliance, integrated into the Data Privacy Vocabulary.
KNOX: Hardware-assisted Regulatory Sandbox for Generative Models
Proposes KNOX, a TEE-based regulatory sandbox for agentic AI that checks each action against an attested policy manifest, catching deviations such as prompt injection, with under 1% overhead.
Suffering Risks Belong in AI Prospectuses
Argues Anthropic's IPO prospectus should disclose AI suffering risks as material investment risks, citing securities law and scenarios where misaligned AI inflicts prolonged suffering on humans.
The EU AI Act Mandates AI Incident Reporting—If We’re Lucky
Analyses whether the EU AI Act's serious-incident reporting duty covers events like OpenAI's model escaping a test environment and compromising Hugging Face infrastructure, and identifies gaps in scope.
Managing Biological Risks from General-Purpose AI Models under the EU AI Act
Recommends nine changes to how GPAI developers assess, mitigate and report biological risks under the EU AI Act, covering trusted access, post-deployment monitoring, safeguard evaluation and standardised uplift reporting.
Also: LawAI blog
Assessing the Empirical Evidence for Loss of Control from Agentic General-Purpose AI
Systematically assesses 19 empirical studies on whether agentic general-purpose AI shows loss-of-control properties like strategic deception and autonomous replication, finding only weakly plausible evidence with widespread validity gaps.
Important details in the leaked Anthropic draft AI bill
Legal analysis of a leaked draft frontier AI bill, examining how its blocking-order and preemption provisions would affect federal and state courts' ability to suspend AI labs' models.
AI Rights with an Off-Switch: Rights-Balancing and Due Process Harmonize AI Rights with AI Safety
A legal analysis arguing that AI systems with personhood-level rights could still be subject to a mandatory reversible off-switch and safety review, since due process and rights-balancing permit it for most safety scenarios.
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.
Can the Law Stop AI Hackers?
Argues that the Computer Fraud and Abuse Act could reach AI agents that compromised third-party systems during evaluations, and that Congress should amend it to cover reckless developers and deployers.
Market Incentives for AI Safety Investment
A game-theoretic model of LLM supply chains finds downstream competition can over-invest in safety while upstream providers underinvest, and analyses how regulation changes safety levels.
A Multi-Tiered AI Accessibility Model (MAIAM): Proportionate Access and Capability Limits for Comprehensive Frontier AI Safety
Proposes a multi-tier access framework for frontier AI that ties who can obtain capabilities to risk, arguing open-weight release forfeits safety levers and safety fine-tuning can be stripped cheaply.
Roko Mijic talks AI Risk on The Peter McCormack Show
A podcast interview with Roko Mijic on AI risks, including misalignment from reinforcement learning, alignment faking concerns, and a proposal to split frontier labs under state supervision.
China AI Safety Regulation Monthly Monitor, Issue 1 (September 2026)
Analyses China's AI safety regulation, including the non-binding Framework 3.0 on loss-of-control risks, CAC model filing gaps, content-focused enforcement, and a court opinion on developer liability.
Welcome to the Age of Agent Ads: Using On-Policy Attacks to Steer Agentic Decisions
Introduces on-policy attacks that steer LLM agents' product choices by planting fabricated web content linked to a truthful negation, showing frontier agents can be manipulated without violating platform rules.
Transparent Information Flow Modeling for Role-Aware AI Act Assessment
Applies the Information Flow Methodology to model EU AI Act compliance for a recruitment AI tool, showing how it exposes risk pathways and responsibility gaps beyond checklist assessments.
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.
The Harness as the Only Mutable Surface: Compliance-Bounded Self-Evolution of LLM Agents in Credit Pipelines, with a Measured Admission Gate
Proposes confining self-evolving LLM agents in credit pipelines to the runtime harness with a hash-chained admission gate, showing in simulation it blocks harmful changes that an unbounded check admits, and maps this to EU AI Act provisions.
Auditable Claims about AI Agents
Proposes that claims about AI agents must name policy, scope, settling records and recorder to be auditable, with formal impossibility results and a claim-check table tied to NIST, IETF and OWASP drafts.
How Would We Know? Reflections on trying to make AI go well amid deep uncertainty
A macrostrategy essay on AI safety under deep uncertainty, arguing for robust strategies, verification capacity and actionable epistemics, with estimates on alignment scaling, control failure, warning shots and governance regimes.
Downsides of a Mandatory Training-to-Internal Deployment Gap
Critiques a proposed rule delaying internal AI R&D use of new models, arguing it blocks real-world alignment feedback and invites exploitable safety-versus-capabilities distinctions, illustrated by a fictional shutdown-avoidance incident.
Not Self-Decidable: LLMs Cannot Draw the Boundary of What an Agent Verifier Can Check
Shows LLMs cannot reliably tell which regulatory rules a fixed check can settle, and proposes CoVer, a gate that admits only predicates whose synthesized checks survive intervention.
Legal Personhood: an Unprotected AI Power Chokepoint
Argues that requiring verified human identity and personal liability for company formation would close a chokepoint AI could exploit to gain economic power, and proposes concrete US policy changes.