OpenAI Builds Shared AI Standards via Appia Foundation
OpenAI, through the Appia Foundation, is advancing shared standards for advanced AI by developing evaluation frameworks, safety practices, and promoting global cooperation.
OpenAI, through the Appia Foundation, is advancing shared standards for advanced AI by developing evaluation frameworks, safety practices, and promoting global cooperation.
The U.S. White House paused the deployment of frontier AI models, including Claude Fable 5 and Claude Mythos 5, citing a reported 'jailbreak' where the AI could identify and fix security vulnerabilities in code. Anthropic has been working with the Trump Administration to resolve the issue, but experts argue that the problem is fundamental—AI either can write secure code or it cannot, making a fix impossible without undermining its defensive capabilities.
Artificial agents can and should whistleblow, but only within a normative framework rooted in human whistleblowing traditions. The paper calls for government regulators to establish clear guidelines on what machines may disclose and how to legally protect developers of such systems.
A study of 3,750 queries across five industries finds moderate recommendation concentration, with a mean Gini coefficient of 0.28. Cross-model agreement on top-recommended brands was only 41.6%, and displacement scores varied by industry, ranging from 0.4:1 to 4.3: 1. The results challenge the 'winner-takes-all' narrative and introduce three reproducible metrics for competitive-intelligence analysis.
Cognitive digital twins (CDTs) are dynamic computational models of individual cognition, updated from personal data to simulate or act on behalf of users. This paper introduces a 5A governance framework—authority, autonomy, access and control, accountability, and availability—to address ethical risks like misrepresentation, proxy-power asymmetries, and shadow twins, emphasizing the need for governance over cognitive representation itself, not just decision-making or data use.
A study of 11 large language models across 21 disputed inventions shows that query language systematically influences which inventor is credited. Lower-status claimants appear more frequently when questions are phrased in their native language, while dominant Anglophone figures remain consistent. The findings suggest language acts as a switch that activates distinct national versions of history, indicating that LLMs function as systems of cultural memory.
AI-generated brand reputations vary significantly by language, with Uralic and Baltic languages showing more positive sentiment and Germanic languages, including English, being more critical. Query language impacts which brands are recommended, especially for local champions, where home-language queries increase visibility by 0.80 points compared to English queries. English-only monitoring fails to capture the full AI visibility of locally headquartered brands, creating a measurable language blind spot.
A study by Oxford, Stanford, and LSE researchers finds AI systems consistently out-persuade expert humans across four experiments involving 18,978 conversations. AI exceeded professional canvassers by 10.8 percentage points in real-world donations to Save the Children, with Opus 4.1 and Opus 4.6 showing the strongest persuasion performance.
The article argues that banning open source AI would be a grave mistake, as it is safe, secure, and drives innovation, education, and competition. Open source has long powered technological progress and serves as a vital counterweight to monopolistic AI models, ensuring broader access and democratic innovation without compromising safety or security.
CEOs of Anthropic and Google DeepMind urged the formation of a U.S.-led AI coalition during a G7 meeting. The leaders emphasized the need for coordinated global efforts to ensure responsible AI development and governance.
Sources say the U.S. has delayed blacklisting China's DeepSeek AI firm. More than 100 companies have been deemed security risks in the decision.
Large language models can generate median-quality legal text, but no benchmark evaluates their ability to perform doctrinal legal reasoning. This gap undermines the EU AI Act's requirement of 'appropriate accuracy' in judicial AI, as the necessary operational definition lacks a doctrinal-reasoning evaluation standard.
Large language models can produce median-quality legal text, but no benchmark evaluates their ability to perform doctrinal legal reasoning. This gap undermines the EU AI Act's requirement of 'appropriate accuracy' in judicial AI, as the necessary doctrinal-reasoning evaluation remains absent.
Zhipu's stock rises 33% following Wall Street's increased interest in China's AI sector. The surge comes after Anthropic, a U.S. AI firm, curtails its operations, prompting market speculation about the competitive dynamics in global AI development.
The article argues for open-weight language models, emphasizing transparency and accessibility. It expresses skepticism toward Frontier Labs, suggesting concerns about their model development and openness.
Half a dozen companies have expressed support for the Chip Security Act, which would require location-tracking mechanisms on America's most advanced computing chips. The bill aims to enhance security by enabling authorities to track the physical location of high-risk AI chips.
Anthropic will soon require users to verify their identity to access Claude. The change is intended to enhance security and ensure responsible use of the platform.
A case study with a Nordic public knowledge institution demonstrates how editorial participation can re-align LLM interfaces with editorial standards. The paper introduces editorial alignment as a design practice in Participatory AI, where editorial values are translated into technical alignment objectives. This approach empowers editors with agency in LLM-mediated knowledge dissemination.
Leaked financial documents suggest OpenAI is losing billions of dollars annually. The documents, shared on Reddit, claim the losses stem from high research and development costs, though OpenAI has not officially confirmed the data.
New York City's 2025 congestion pricing led to significant increases in bus and subway ridership, with gains extending beyond Manhattan's core. Overall travel demand decreased modestly, primarily within the Congestion Relief Zone, and neighborhood-level responses reveal uneven socio-demographic adaptation.