Anthropic publishes structured harm assessment framework covering physical, psychological, economic, and societal impacts
Anthropic has released a policy document describing their evolving framework for assessing and mitigating AI harms across five dimensions: physical, psychological, economic, societal, and individual autonomy impacts. The framework complements their existing Responsible Scaling Policy and informs decisions on usage policies, red-teaming, detection, and enforcement. Concrete examples include safeguards for computer use capabilities (fraud, phishing) and a reported 45% reduction in unnecessary refusals in Claude 3.7 Sonnet through improved handling of ambiguous prompts. Anthropic frames this as a work-in-progress and invites collaboration from the broader AI ecosystem.
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Anthropic publishes framework for safe and trustworthy agent development
Anthropic released a formal framework for responsible agent development, articulating principles around human oversight, transparency, value alignment, and privacy for autonomous AI agents. The document draws on Claude Code as a reference implementation and cites enterprise deployments at Trellix and Block as real-world examples. The framework is positioned as a contribution to emerging industry standards for agentic AI systems, acknowledging open technical challenges in value alignment measurement and oversight calibration.
Anthropic publishes Responsible Scaling Policy with AI Safety Level framework
Anthropic released its Responsible Scaling Policy (RSP), a formal framework of technical and organizational protocols for managing catastrophic risks from increasingly capable AI systems. The policy introduces AI Safety Levels (ASL-1 through ASL-5+), modeled on US biosafety level standards, requiring progressively stricter safety, security, and operational standards as models become more capable. Current Claude models are classified as ASL-2; ASL-3 triggers stricter deployment constraints including adversarial red-teaming requirements. The policy has been approved by Anthropic's board and is intended as a template for industry-wide adoption.
Anthropic launches initiative to fund third-party AI safety evaluations
Anthropic announced a funded initiative to source third-party evaluations measuring advanced AI capabilities and safety risks, with priority areas including cybersecurity, CBRN threats, model autonomy, national security risks, social manipulation, and misalignment. The initiative is tied to Anthropic's Responsible Scaling Policy and AI Safety Level (ASL) framework, aiming to address a gap between demand and supply of high-quality safety-relevant evals. Proposals are solicited via an application form, with Anthropic framing the effort as benefiting the broader AI safety ecosystem rather than just internal use.
Anthropic Releases Responsible Scaling Policy Version 3.0
Anthropic has published the third version of its Responsible Scaling Policy (RSP), a voluntary framework for mitigating catastrophic risks from increasingly capable AI systems. The update reflects two-plus years of experience with the original RSP, reinforcing what worked (ASL-3 safeguards activated in May 2025, industry adoption by OpenAI and Google DeepMind, informing early AI policy) while addressing shortcomings in accountability and transparency. The new version refines the AI Safety Level (ASL) framework and introduces new measures for decision-making transparency. Anthropic acknowledges that some elements of its original theory of change—particularly multilateral coordination and government action at higher capability thresholds—have not fully materialized as hoped.
Anthropic publishes foundational 'Core Views on AI Safety' position paper
Anthropic released a detailed position paper outlining their core views on AI safety, arguing that transformative AI could arrive within a decade driven by predictable scaling laws, and that no one currently knows how to train powerful AI systems to robustly behave well. The document explains Anthropic's founding rationale and research strategy, highlighting four priority areas: scaling supervision, mechanistic interpretability, process-oriented learning, and understanding AI generalization. Originally published March 2023, this represents Anthropic's canonical public statement of their safety philosophy and strategic priorities.
Anthropic submits AI accountability recommendations to NTIA, covering evals, red teaming, and pre-registration
Anthropic submitted a formal response to the NTIA's Request for Comment on AI Accountability, outlining a multi-part policy framework for governing advanced AI systems. Key recommendations include increased government funding for evaluation research, mandatory disclosure of evaluation methods, pre-registration of large training runs with national governments, mandated external red teaming before model release, and antitrust guidance to enable industry safety collaboration. The submission reflects Anthropic's core policy positions and advocates for risk-tiered oversight proportional to model capabilities.
Anthropic publishes policy brief calling for targeted AI regulation within 18 months
Anthropic published a policy position paper arguing that governments have an 18-month window to enact narrowly-targeted AI regulation before risks in cyber and CBRN domains become acute. The post cites rapid capability gains—SWE-bench scores rising from 1.96% to 49% in a year, GPQA scores approaching human expert level—as evidence that frontier models are approaching meaningful misuse thresholds. Anthropic also reviews its Responsible Scaling Policy as a model for adaptive, proportionate risk governance and calls for similar frameworks to be adopted industry-wide and codified in law.
Anthropic responds to California Governor Newsom's AI working group draft report
Anthropic published a formal response to the California Governor's Working Group on AI Frontier Models draft report, endorsing its emphasis on transparency and evidence-based policy. Anthropic argues that light-touch mandatory disclosure of safety and security practices would be beneficial without impeding innovation, noting that current voluntary practices are uneven across frontier labs. The response also references Anthropic's Responsible Scaling Policy and Economic Index as examples of existing transparency efforts, and signals urgency given Anthropic's view that powerful AI systems may arrive as early as end of 2026.