88 stories tagged Transparency · page 3 of 4
A German publisher uses AI to generate 1,500 articles daily by rewriting Hacker News posts. The content attracts over a million monthly visitors but strips original sources and attribution, raising transparency concerns.
Researchers have developed HierBias, an AI model that analyzes whole articles to detect media bias, formally proving that using document context reduces error compared to sentence-by-sentence approaches.
CtxGov is a new open-source tool that reveals the hidden instructions behind AI agents. It helps users understand how their AI assistants make decisions. This transparency could change how people trust and use AI tools.
AI memory benchmarks often hide key details, making it hard to compare models fairly. This lack of transparency affects how we evaluate AI capabilities.
Rep. Anna Paulina Luna claims her staff only used AI for spellcheck in a defense bill amendment, not for the actual legislation. This follows accusations that AI tools were involved in drafting the bill text.
The Atlantic has created a searchable database of music used to train AI models. Reporter Alex Reisner uncovered four datasets, including two massive collections of 12 million and 9 million tracks, offering unprecedented transparency into AI's musical influences.
A new study suggests that to properly audit AI agents, we must also examine the data they use to make decisions. This could change how we ensure AI systems are fair and reliable.
A new bipartisan AI bill aims to regulate AI development while fostering innovation. The draft focuses on transparency, safety, and accountability in AI systems. The draft of the bill is available as a PDF.
Researchers released SemantiClean, a modular AI framework that analyzes e-commerce session data to predict purchases and customer preferences. Unlike conventional AI, it prioritizes auditability and transparency over marginal gains in accuracy, providing a clear decision trail for every inference.
Anthropic has admitted to secretly limiting its new AI model, Claude Fable 5, which affected both researchers and competitors. The company now promises to be more transparent about these restrictions. In plain English, this means the AI will say 'no' more often, but you'll know why.
OpenAI has outlined its approach to AI policy, emphasizing transparency and support for thoughtful regulation. The company clarifies that no external groups speak on its behalf.
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.