Corporate AI Safety Responses

This RAND Corporation research report analyzes the common reasons AI projects fail in practice, examining organizational, technical, and governance challenges. It provides evidence-based recommendations for improving AI project outcomes across government and industry contexts. The report is particularly relevant for understanding the gap between AI capabilities and successful real-world deployment.

Partnership on AI (PAI) is a nonprofit coalition of AI researchers, civil society organizations, academics, and companies working to develop best practices, conduct research, and shape policy around responsible AI development. It brings together diverse stakeholders to address challenges including safety, fairness, transparency, and the societal impacts of AI systems. PAI serves as a coordination hub for cross-sector dialogue on AI governance.

The official website of the Future of Humanity Institute (FHI), an Oxford University research center that was foundational in establishing the fields of existential risk research and AI safety. FHI closed on 16 April 2024 after approximately two decades of influential work. The site now serves as an archived record of the institution's history, research agenda, and legacy.

ISO/IEC JTC 1/SC 42 is the primary international standards committee responsible for AI standardization, operating under joint ISO/IEC governance with ANSI as secretariat. It develops and coordinates AI standards across topics including trustworthiness, bias, transparency, and AI system lifecycle, with 41 published standards and 48 under development. The committee serves as the focal point for AI standardization guidance to other ISO, IEC, and JTC 1 committees.

METR is an organization conducting research and evaluations to assess the capabilities and risks of frontier AI systems, focusing on autonomous task completion, AI self-improvement risks, and evaluation integrity. They have developed the 'Time Horizon' metric measuring how long AI agents can autonomously complete software tasks, showing exponential growth over recent years. They work with major AI labs including OpenAI, Anthropic, and Amazon to evaluate catastrophic risk potential.

The NIST AI RMF is a voluntary, consensus-driven framework released in January 2023 to help organizations identify, assess, and manage risks associated with AI systems while promoting trustworthiness across design, development, deployment, and evaluation. It provides structured guidance organized around core functions and is accompanied by a Playbook, Roadmap, and a Generative AI Profile (2024) addressing risks specific to generative AI systems.

DeepMind's official responsibility page outlines the company's core principles and commitments for developing AI safely and beneficially. It articulates DeepMind's approach to responsible AI development, including safety research priorities, ethical considerations, and governance frameworks guiding their work.

OpenAI's Preparedness initiative outlines a framework for tracking, evaluating, and mitigating catastrophic risks from frontier AI models. It establishes risk thresholds across categories like cybersecurity, CBRN threats, and persuasion, and defines safety standards that must be met before model deployment.

The Center for AI Safety (CAIS) is a research organization focused on mitigating catastrophic and existential risks from advanced AI systems. It conducts technical research, publishes surveys and statements, and supports field-building efforts across academia and industry. CAIS is notable for its broad coalition-building, including its widely-cited statement on AI extinction risk signed by leading researchers.

AI Lab Watch's Commitments Tracker monitors and evaluates the public safety commitments made by major AI laboratories, tracking whether frontier AI companies are honoring pledges related to safety, governance, and responsible deployment. It serves as an accountability tool by systematically documenting what labs have promised and assessing follow-through.

METR (Model Evaluation and Threat Research) provides analysis related to frontier AI safety cases, likely examining evaluation frameworks and safety benchmarks for advanced AI systems. The resource appears to document METR's methodological approach to assessing dangerous capabilities and safety properties of frontier models.

Anthropic's Responsible Scaling Policy (RSP) is a formal commitment outlining how the company will evaluate AI systems for dangerous capabilities and adjust deployment and development practices accordingly. It introduces 'AI Safety Levels' (ASL) analogous to biosafety levels, establishing thresholds that trigger specific safety and security requirements before proceeding. The policy aims to prevent catastrophic misuse while allowing continued AI development.

SaferAI critiques Anthropic's updated Responsible Scaling Policy (RSP), arguing that recent revisions weaken safety commitments rather than strengthening them. The analysis contends that the updated policy relaxes key thresholds and evaluation requirements, reducing accountability for frontier AI deployment. This represents a critical external perspective on how voluntary safety frameworks can erode over time.

OpenAI's Preparedness Framework outlines a structured approach to evaluating and managing catastrophic risks from frontier AI models, including threats related to CBRN weapons, cyberattacks, and loss of human control. It defines risk severity thresholds and ties model deployment decisions to safety evaluations. The framework represents OpenAI's operational policy for responsible frontier model development.

Google DeepMind outlines updates to its Frontier Safety Framework, which sets out protocols for identifying and mitigating potential catastrophic risks from advanced AI models. The post details how the company evaluates models for dangerous capabilities thresholds and what safety measures are triggered when those thresholds are approached or crossed. It represents DeepMind's evolving commitment to responsible deployment of frontier AI systems.

Meta's blog post introduces Llama Guard 3, a safety classifier model designed to detect unsafe content in LLM inputs and outputs, released alongside Llama 3.1. It outlines Meta's responsible deployment approach including red-teaming, safety evaluations, and open-source safety tooling for the broader AI ecosystem.

A collection of voluntary safety commitments made by leading AI companies at the AI Seoul Summit 2024, building on the Bletchley Declaration. Companies pledge to publish safety frameworks, conduct pre-deployment evaluations, share safety information, and establish responsible scaling thresholds before deploying frontier AI models.

OpenAI's Preparedness Framework v2 outlines the company's structured approach to evaluating and managing catastrophic risks from frontier AI models, including definitions of risk severity levels and thresholds that determine whether a model can be deployed or developed further. It establishes a systematic process for tracking, evaluating, and preparing for frontier model risks across domains such as CBRN threats, cyberattacks, and loss of human control. The framework represents OpenAI's operationalized safety commitments with concrete governance mechanisms.

Google DeepMind's Frontier Safety Framework (v3.0) defines protocols for identifying Critical Capability Levels (CCLs) at which frontier AI models may pose severe risks, and outlines mitigation approaches across three risk categories: misuse, ML R&D acceleration, and misalignment. The framework specifies risk assessment processes, response plans, and criteria for evaluating whether mitigations are sufficient before deployment.

METR analyzes the safety policies of 12 frontier AI companies to identify common elements, commitments, and gaps in how organizations approach responsible deployment of advanced AI systems. The analysis synthesizes patterns across responsible scaling policies, model cards, and safety frameworks to provide a comparative overview of industry norms. It serves as a reference for understanding where consensus exists and where significant variation or absence of commitments remains.

METR analyzes the common structural elements across frontier AI safety policies published by major AI companies, identifying shared frameworks around capability thresholds, model evaluations, weight security, deployment mitigations, and accountability mechanisms. The December 2025 version covers twelve companies including Anthropic, OpenAI, Google DeepMind, Meta, and others, and incorporates references to the EU AI Act's General-Purpose AI Code of Practice and California's Senate Bill 53.