63 stories tagged Transparency · page 3 of 3
Researchers developed DIAGRAMS, a tool to help AI explain its reasoning when answering questions about diagrams. This makes it easier to understand how AI arrives at its answers, improving transparency.
Researchers have developed a method called RSAT that helps small language models explain their reasoning when answering questions about tables. This makes it easier for users to verify the accuracy of the AI's answers.
Kepler used Claude to create AI systems that can explain their decisions, which is crucial for financial services. This helps build trust in AI-driven financial tools. In plain English, it means AI can now show its work, making it more reliable for everyday banking and investments.
Researchers propose a novel approach to estimate the parameter count of black-box language models by analyzing their factual capacity. This method could revolutionize benchmarking and comparison of proprietary models.
The Hugging Face Blog highlights how open-source AI is transforming cybersecurity by fostering collaboration and innovation. Transparency in AI models is crucial for building trust and resilience against cyber threats.
OpenAI has published the system card for GPT-5.5, detailing its improved performance and safety measures. The model shows significant advancements in reasoning and multilingual understanding.
DeepER-Med introduces AI agents that enhance evidence-based medical research by improving transparency and trustworthiness. The system integrates multi-hop information retrieval and reasoning to accelerate scientific discovery while mitigating errors.
Canada's Federal AI Register, launched in 2025, is more than a transparency tool—it actively shapes accountability. A new study reveals its limitations and biases in tracking AI systems. The register omits key details, raising questions about its effectiveness.
AI systems are increasingly advising government bodies without public disclosure. This raises concerns about transparency and accountability in policy decisions. The lack of oversight could lead to unchecked influence on critical issues. Experts call for stricter regulations to ensure public trust.
A comprehensive survey reviews the use of Explainable AI (XAI) to make surrogate models more interpretable. The study highlights the need for transparency in complex simulations across scientific and engineering fields. This work offers a roadmap for integrating XAI into decision-making processes.
DeepReviewer 2.0 introduces a new AI system for scientific peer review that emphasizes traceability and auditability. It generates review packages with anchored annotations, evidence, and executable follow-up actions, setting a new standard for transparency in AI-driven review processes.
OpenAI has released a comprehensive guide on responsible AI use, emphasizing safety, accuracy, and transparency. The guide offers best practices for users of tools like ChatGPT.
Researchers introduce Springdrift, a persistent runtime for LLM agents that prioritizes auditing. This could revolutionize how we track and verify AI agent actions.
OpenAI introduces the Model Spec, a public framework guiding model behavior to ensure safety while preserving user freedom. The approach emphasizes accountability as AI systems evolve.
The Waymo Rule proposes guidelines for AI-generated code. It aims to ensure accountability and transparency in AI development.