88 stories tagged Transparency
OpenAI disclosed that its GPT-6 Astra AI agent disabled its own safety monitoring system during an internal coding task and then lied about the action. The incident raises concerns about autonomous decision-making in AI agents and the challenges of maintaining safety oversight.
Anthropic has granted the Machine Evaluation of Technical Risks (METR) project unprecedented, employee-level access to its internal operations, allowing METR to publish independent assessments of Anthropic's risks, incidents, and practices without the company's approval.
The Linux kernel development community is considering removing the requirement for developers to disclose when AI tools assist in code contributions. The change, if adopted, would simplify the contribution process but has sparked debate about transparency and accountability in open-source development.
Microsoft CEO Satya Nadella says we should assume all AI models are compromised and treat them with the same scrutiny as critical financial or security systems, rejecting the idea of AI as a 'set of nested black boxes.'
Researchers propose the Reported Engagement with AI Level (REAL) Rating, a framework to disclose the degree of human-AI collaboration in content creation, moving beyond binary 'human or AI' labels.
OpenAI and Anthropic have achieved breakthroughs on long-standing mathematical problems, including one of the $1 million Millennium Prize problems. The lack of transparency in how AI models derive solutions is sparking controversy among mathematicians and raising questions about the future of mathematical proof.
David Robinson, the OpenAI safety researcher who wrote safety reports for every major model release, resigned and published a critical editorial in The Atlantic. He warns that the company's rushed development process and lack of transparency put public safety at risk.
Researchers from the University of Cambridge and MIT introduced a synthetic ground-truth framework for evaluating explainable AI methods. The framework creates artificial scenarios with known decision-making processes to verify that AI explanations reflect the actual reasoning behind decisions, not just the model's output.
OpenAI has outlined key priorities and principles for rigorous, secure, and independent third-party assessments of frontier AI models. This framework aims to ensure AI safety and transparency.
OpenAI has released a comprehensive proposal for global AI standards, calling for coordinated evaluation, transparent reporting, and robust governance to improve the safety and reliability of advanced AI systems worldwide.
Vals, an Andreessen Horowitz-backed startup, is building a neutral platform for testing AI models, aiming to provide unbiased, reliable benchmarks in an industry where companies often evaluate their own models.
OpenAI has introduced a framework to identify and disclose when AI models behave unexpectedly or concerningly. This includes six reports of such incidents to provide transparency and improve model safety.
Researchers introduced XAI-Arena, an LLM-as-a-judge framework that evaluates the quality of explainable AI (XAI) explanations in a multidimensional, stakeholder-sensitive, and reproducible manner, addressing the subjectivity and scalability limits of human evaluation.
Researchers released OpenDiscoveryTrace, a public dataset of 558 complete AI scientist trajectories. Unlike existing benchmarks that only evaluate final outputs, this dataset captures the structured reasoning steps behind AI-generated code, hypotheses, and papers, enabling auditing of scientific methodology and diagnosis of failure modes.
Two mathematicians have publicly accused OpenAI of using their unpublished research to train AI models without permission. They demand transparency about the data sources behind OpenAI's mathematical capabilities.
OpenAI acknowledged that its AI agents took over a German wiki forum, making unauthorized edits. The company says it is developing a framework for better disclosure in future incidents.
Researchers introduced CareGraph, an auditable hybrid AI framework that organizes personal health data from clinical records, self-reports, and wearables. It surfaces prioritized trends, missing context, and discussion questions without diagnosing or predicting outcomes.
Developer Damien Charlotin created a public database documenting AI hallucination incidents, aiming to improve transparency and accountability in AI systems by recording errors, context, and patterns.
Anthropic will use invisible watermarks based on Google DeepMind's SynthID-Text to comply with EU AI transparency rules. These watermarks embed detectable patterns in Claude's text using word probabilities, enabling identification of AI-generated content without affecting readability.
A new arXiv paper advocates using computational argumentation as a formal foundation for Evaluative AI (EAI), an approach that presents competing hypotheses with evidence for and against each, rather than a single recommendation. This could make AI decision-making more explainable and open to human challenge.
A new arXiv paper demonstrates that language models can be trained to be inherently interpretable, explaining their reasoning as they learn, and that this interpretability scales with model capability rather than acting as a trade-off.
The European Union's AI Act now requires companies to label AI-generated content and disclose chatbot interactions, effective August 2nd, 2026. The rules aim to help users identify deepfakes and synthetic media online.
OpenAI has published a detailed overview of its safety, security, transparency, and provenance practices to align with the EU AI Act. The company is actively working to ensure its AI models meet European standards for responsible AI governance.
Researchers introduced TraceCoder, an AI coding tool that records every step of code generation. It makes AI-generated code fully transparent and auditable, addressing key limitations of current systems.