77 stories tagged Transparency
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Two mathematicians have publicly accused OpenAI of using their unpublished research to train AI models without permission.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.
Researchers propose FrED, a probabilistic framework that traces AI training data origins using knowledge graphs without needing access to model weights or internal architecture. This black-box approach could improve transparency and accountability in generative AI.
A new arXiv study reveals that frontier AI models use semantically irrelevant filler tokens to improve reasoning accuracy by up to 13 percentage points, raising transparency concerns for AI safety.
Hugging Face CEO Clément Delangue demands radical transparency from OpenAI after the first known autonomous agent cyberattack breached its systems, escalating AI security risks industry-wide.
Substack is introducing a new AI-detection tool powered by Pangram to help users identify AI-written content. The feature scans posts, notes, replies, and comments to estimate how much text may have been generated or assisted by AI, aiming to increase transparency on the platform.
Substack has launched a tool that estimates how much of a newsletter was written by AI, giving readers a transparency badge next to the author's name. The move signals a broader industry shift toward disclosing AI-assisted content.
Researchers have developed a three-stage method that translates a trained deep reinforcement learning policy into an executable Prolog program, making AI decisions readable, runnable, and editable by humans.
Researchers have introduced Causal-Audit, a framework that enables AI models to explicitly show their step-by-step reasoning about cause-and-effect relationships, making AI decisions more trustworthy and verifiable in fields like medicine and law.
The Neutrality Project has released a tool that measures political bias in AI models, helping users and developers detect and quantify political leanings to improve transparency and accountability.
New York Governor Kathy Hochul revealed on Bloomberg's Odd Lots podcast that her team is using AI to analyze every state rule, regulation, and policy to identify outdated or ineffective ones. The goal is to streamline government operations, but the initiative also raises questions about privacy and bias in AI decision-making.
Suno AI, a popular AI music generator, was trained using millions of songs and lyrics scraped from platforms like YouTube, Deezer, and Genius. This revelation comes from a hacking incident that exposed the company's data sources, raising questions about transparency in AI training.
Researchers developed a new AI system that explains medical diagnoses using a structured argumentation model. This approach makes AI recommendations more transparent and trustworthy for doctors.
Researchers proposed a system to make AI's scientific reasoning transparent. This could help scientists audit AI-generated ideas like they would their own notes. The tool creates a structured record of AI hypotheses and experiments, making it easier to follow the AI's thought process.