AI Safety
New research reveals frontier language models frequently skip or contradict their own chain-of-thought reasoning, raising serious questions about AI transparency and the reliability of systems that "show their work."
Explainable AI
New research introduces FAME, a framework using formal methods to generate mathematically guaranteed minimal explanations for neural network decisions, advancing AI interpretability.
Explainable AI
Learn how to implement SHAP-IQ for understanding feature importance and interaction effects in AI models, enabling transparent decision breakdowns essential for trustworthy systems.
Agentic AI
New research framework bridges traditional ML explainability methods with emerging agentic AI systems, proposing action-based interpretability for autonomous AI agents.
AI Regulation
New York legislators are considering two significant AI bills that could establish transparency requirements and safety standards for AI companies operating in the state.
AI Safety
New research shows AI models frequently omit key reasoning steps in their explanations, raising critical questions about whether we can trust AI transparency and the reliability of chain-of-thought prompting.
AI Transparency
Researchers propose comprehensive workflow for tracking AI decision-making processes from data input to final output, addressing transparency and accountability challenges in modern AI systems through systematic documentation and verification methods.