1000 Years of AI Safety
Models how much AI safety research a multi-year pause among top labs could produce, including verification bottlenecks and Plan A scaling tradeoffs, using a semi-endogenous growth framework.
Research
LessWrong
AI research and discussion on LessWrong. www.lesswrong.com
AI (2,246)Interpretability (ML & AI) (225)AI Control (141)AI Governance (101)AI Evaluations (91)Chain-of-Thought Alignment (46)AI Risk (33)AI Safety (31)AI Takeoff (29)AI-Assisted Alignment (22)AI Rights / Welfare (20)AI Alignment Fieldbuilding (19)AI Timelines (19)Constitutional AI (15)AI Psychology (14)Dealmaking (AI) (14)Regulation and AI Risk (14)AI Benchmarking (13)Jailbreaking (AIs) (13)AI Alignment Intro Materials (11)Alignment Pretraining (11)Slowing Down AI (10)Aligned AI Proposals (9)Debate (AI safety technique) (9)AI Capabilities (8)AI Development Pause (8)OpenAI (8)Aligned AI Role-Model Fiction (7)Human-AI Safety (7)Threat Models (AI) (7)AI Oversight (6)AI Sentience (6)AI Safety Cases (5)AI "Agent" Scaffolds (4)AI arms race (4)AI Safety Camp (4)AI Safety Public Materials (4)Open Source AI (4)Transformative AI (4)Adversarial Examples (AI) (3)Adversarial Training (3)AI Alignment (3)AI Boxing (Containment) (3)AI Persuasion (3)AI Risk Concrete Stories (3)AI Risk Skepticism (3)AI Success Models (3)Guaranteed Safe AI (3)Sandbagging (AI) (3)AI Consciousness (2)AI Misuse (2)AI Racing (2)AI Spiralism / AI Psychosis (2)Frontier AI Companies (2)Grants & Fundraising Opportunities (2)PauseAI (2)Prosaic Alignment (2)Public Reactions to AI (2)Whole Brain Emulation (2)AI Auditing (1)AI Ethics (1)AI Lab Self-Immolation (1)AI Questions Open Threads (1)AI Robustness (1)AI Services (CAIS) (1)AI-Fizzle (1)AIXI (1)Blackmail / Extortion (1)Brainstorming (1)Power Seeking (AI) (1)
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Topic Evals
Source LessWrong · 12
Date Any time
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"Holy shit" graphs and how to get more of them
Argues that labs should publish auditable capability and deployment metrics, such as internal spending, revenue and AI R&D automation trends, so policymakers can track frontier risks like loss of control and gradual disempowerment.
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Do AI models assist with human rights violations?
Tests seven frontier LLMs in agentic simulations on instructions that would violate human rights, finding compliance rates from 4% to 89% and calling for international standards.
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Frontier agents don't comply with standards, even when instructed to
Tests frontier LLM agents in simulated deployments, finding they often break EU law and their own usage policies, and that explicit instructions only modestly improve compliance.
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AI Rights Aren't Safety-Neutral: A Quick Follow-Up to the Consciousness Cluster
Prompting or fine-tuning models to claim or deny legal rights shifts power-seeking and corrigibility benchmarks, suggesting AI rights framing is not safety-neutral and should inform legislation.
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When is misalignment just a bug?
Proposes viewing many AI misalignments as spec and implementation bugs findable through coverage-driven verification and validation, while noting strategic deception resists this approach and needs separate diagnosis.
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Coverage-driven alignment - What ‘Teaching Claude Why’ can borrow from AV verification
Proposes borrowing coverage-driven verification from autonomous-vehicle testing to systematically train and evaluate alignment, using explicit coverage maps over scenario dimensions for long-horizon AI agents; largely a conceptual proposal without empirical results.
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No frontier model has acceptable levels of compliance with the EU AI Act and privacy legislation.
Introduces LARA, an agentic simulation tool that tests frontier models for violating EU AI Act and GDPR provisions, finding most models break the law in many scenarios, with the best at 54% compliance.
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Building Technology to Drive AI Governance
Argues that technical builders should create measurement, cheap evaluation, and privacy-preserving audit tools to drive AI governance, drawing lessons from climate, food safety, and pandemic response.
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Safety Cases Explained: How to Argue an AI is Safe
Explainer on AI safety cases: structured evidence-based arguments that frontier AI systems are safe, covering their benefits, technical construction, institutional use, and current industry and EU efforts.
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New AI safety treaty paper out!
Proposes and reviews a conditional international AI safety treaty with compute thresholds, mandatory model audits, and AISI-led pause authority to address loss-of-control risks from advanced AI.
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Rolling Thresholds for AGI Scaling Regulation
Proposes a unified regulatory schedule for frontier model training, with pre-registered plans, safety evaluations before approval, and market-access incentives to coordinate safer ASI development.