OpenAI Policy Paper: Four Strategies for Industry Cooperation on AI Safety
OpenAI published a policy research paper identifying four strategies to foster long-term industry cooperation on AI safety norms: communicating risks and benefits, technical collaboration, increased transparency, and incentivizing standards. The paper argues that competitive pressures risk creating a collective action problem where AI companies under-invest in safety. The analysis frames industry-wide coordination as essential to ensuring AI systems are safe and beneficial.
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OpenAI publishes public policy agenda covering safety, youth protection, and global standards
OpenAI released a formal public policy agenda outlining its positions on AI safety, youth protection, workforce transition, and international standards. The document represents OpenAI's stated priorities for engaging with governments and regulators. As a tier-1 primary source from a leading frontier lab, it signals how OpenAI intends to shape AI governance discussions.
Moving AI Governance Forward: OpenAI and Leading Labs Make Voluntary Safety Commitments
OpenAI and other leading AI laboratories announced voluntary commitments aimed at reinforcing AI safety, security, and trustworthiness. The commitments represent a coordinated industry response to governance concerns ahead of anticipated regulatory action. This move signals alignment among frontier labs on baseline safety standards, though the voluntary nature leaves enforcement questions open.
Our approach to AI safety
OpenAI published a high-level overview of its approach to AI safety, framing safe development and deployment as central to its mission. The post appears to be a brief, top-level statement rather than a detailed technical or policy document. It signals OpenAI's public positioning on safety at a time of growing regulatory and public scrutiny.
Preparing for malicious uses of AI
OpenAI co-authored a multi-institutional paper forecasting how malicious actors could misuse AI technology, produced in collaboration with FHI, CSER, CNAS, EFF, and others over nearly a year. The paper outlines potential threat vectors and proposes prevention and mitigation strategies. This represents an early coordinated effort among AI safety and policy organizations to systematically address AI misuse risks.
AI Safety Needs Social Scientists
OpenAI published a paper arguing that long-term AI safety research requires social scientists to address uncertainties in human psychology, rationality, emotion, and biases that affect alignment algorithms. The paper contends that aligning advanced AI with human values cannot be solved by machine learning alone. OpenAI announced plans to hire social scientists full-time to work on these problems.
Frontier AI regulation: Managing emerging risks to public safety
OpenAI published a policy position on regulating frontier AI systems, focusing on managing emerging risks to public safety. The piece outlines OpenAI's perspective on how governments and regulatory bodies should approach oversight of the most capable AI models. This represents a formal public stance from a leading AI lab on the shape of future AI governance frameworks.
Anthropic Responds to White House AI Action Plan, Calls for Transparency Standards and Export Controls
Anthropic published a policy response to the White House's 'Winning the Race: America's AI Action Plan,' endorsing its focus on AI infrastructure, federal adoption, and safety research while urging additional steps on export controls and mandatory AI development transparency standards. The company highlighted alignment between the plan and its prior OSTP submissions, and noted its proactive activation of ASL-3 protections with Claude Opus 4 as evidence that safety and innovation are compatible. Anthropic called for a single national standard for frontier model transparency rather than a state-by-state patchwork, and encouraged continued investment in NIST's CAISI for evaluating frontier models on national security risks including CBRN capabilities.
Concrete Problems in AI Safety
OpenAI, Google Brain, Berkeley, and Stanford researchers co-authored 'Concrete Problems in AI Safety,' a foundational paper exploring research challenges in ensuring modern ML systems operate as intended. The paper identifies and frames specific technical safety problems for the field. Published in June 2016, it became a landmark reference for AI safety research agendas.