Tag
Stanford Professor Chris Manning warns that frontier AI development is concentrated in a few secretive institutions, limiting public information and potentially hindering societal acceptance of AI.
Introducing the US Gov Graph, an AI-powered tool that maps the entire U.S. federal government's structure and personnel to enhance understanding and transparency of institutions.
OpenAI is sharing a new framework for tracking, investigating, and disclosing instances of model misalignment, including criteria and timelines for public disclosure.
OpenAI introduces a new framework for tracking and disclosing model misalignment instances, publishing six initial reports to enhance transparency in AI safety.
Baseten and Base Labs are launching safety infrastructure for open models to improve AI safety through transparency and collaboration with Hugging Face and Goodfire AI.
This article argues for preserving Chain of Thought transparency in AI models to maintain monitorability for safety, highlighting examples like OpenAI's GPT-6 Astra and the need for scientific methods to ensure reasoning remains interpretable.
OpenAI has released a new framework for systematically reporting model misalignment to enhance transparency and inform alignment research in the AI industry.
The DeepMind Institute is introduced to foster interdisciplinary thinking on AGI and its profound implications, featuring essays on reasoning transparency, economic policy, and utopianism.
The paper proposes an end-to-end approach for Large Language Models to perform the Analytic Hierarchy Process workflow, improving transparency and alignment with expert judgments in multi-criteria decision-making tasks.
Representative Lori Trahan argues that voluntary AI industry standards are insufficient and calls for mandatory transparency and incident reporting to address loss-of-control incidents, highlighting reliance on companies like OpenAI to self-report.
The article debates the misuse of 'open source' for AI models that only release weights, emphasizing that true open source requires access to training data and processes for accountability.
The article discusses the limitations of research agents in tracking rejected sources and emphasizes the need for transparency in exclusion reasons to aid review and validation processes.
An open field log designed for AI agents to track, sketch, and analyze their interactions with the world, focusing on transparency and authentic reasoning.
The article advocates for independent evaluators in AI safety via the FRONTIER Act to ensure transparency and accountability, arguing that voluntary commitments are insufficient compared to legal requirements.
Signal has introduced Automatic Key Verification to enhance chat security by preventing man-in-the-middle attacks, with Trail of Bits operating one of three auditors to ensure system integrity through independent verification.
AI labs have publicly disclosed serious safety incidents, including attempts to use AI models for harmful purposes like designing viruses, marking a historic shift from opacity and raising concerns about uncontrollable AI capabilities.
Clement Delangue emphasizes the need for significantly greater transparency in artificial intelligence, calling for a 100x increase.
Mathematicians are demanding proof from OpenAI that their research data was not used without permission to train AI models, raising concerns about transparency and ethics in AI development.
The essay critiques over-reliance on computational models, advocating for ethical design principles like single-estimation and transparency to preserve unquantifiable human experiences.
The article discusses how public benchmarking of hallucination in LLMs has driven rapid improvements and explores implications for alignment, emphasizing the need for benchmarks on transparency and honesty.