77 stories tagged Transparency · page 3 of 4
Major platforms like YouTube, Instagram, and TikTok are automatically tagging AI-generated content. But users still can't fully filter it out of their feeds, and the article argues platforms should give people that power.
Researchers developed PEEL, a new framework to improve transparency in AI-assisted research. It combines text analysis tools with AI interpretation to spot distortions in research findings.
OpenAI is launching new tools to help people access accurate election information and combat misinformation. They're also working to increase transparency in AI to ensure fair and informed voting.
OpenAI has published a guide for third-party evaluators to assess AI models. This playbook helps ensure models are safe, reliable, and effective for public use.
Nexa-gauge is a new framework that helps developers evaluate AI models more precisely. It allows for detailed, per-node scoring controls, making AI assessments more transparent and customizable.
OpenAI has teamed up with Grupo Folha and Grupo UOL to integrate Brazilian journalism into ChatGPT, ensuring news is sourced transparently. This partnership aims to provide users with reliable, attributed news content directly within the AI chatbot.
Researchers created a system to track how much energy AI agents use to complete real-world tasks. This could help make AI systems more efficient and transparent about their environmental impact.
The European Union's draft AI Act transparency guidelines have sparked controversy by exempting law enforcement from certain regulations. This could impact how AI is used in policing and public safety across the EU.
OpenAI introduced new tools to label and verify AI-generated media, aiming to build trust in digital content. These tools help users identify AI-created images, videos, and text, promoting transparency.
Researchers created a tool called the Belief Engine that tracks how AI agents change their opinions during debates. This makes AI discussions more understandable and trustworthy for everyone.
Scientists have developed a method to identify who created an AI agent. This could help with accountability and security in AI systems. Researchers used unique patterns in AI outputs to trace them back to their origin. This breakthrough aims to make AI more transparent and trustworthy.
Scientists have developed a way to track when AI language models commit to their answers. This helps us understand how AI reasoning works and could make AI more reliable.
Researchers propose a unified graph representation to track AI agents' decisions, making it easier to audit their actions. This could help ensure AI systems behave as intended and follow security protocols.
Researchers have developed a method called Cognitive Agent Compilation (CAC) to make AI learning systems more transparent and controllable. This could help educators better understand and adjust how AI tutors teach.
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.