What to Audit Before Your AI Deployment Becomes a Liability
Standard evaluations tell you whether your AI system performs. They don’t tell you whether it still knows what it’s talking about.
The distinction matters because performance and epistemic reliability can decouple
Open-Loop Generation: On the Architectural Basis of LLM Output Errors
A recent paper from Tsinghua University identifies what it calls H-Neurons — a sparse subpopulation of neurons whose activation patterns predict hallucination events in large language models [1]. The finding is real and probably
The Measurement Problem in AI Risk: Why Output Variance Doesn't Capture Epistemic Drift
Anthropic's recent paper "The Hot Mess of AI: How Does Misalignment Scale with Model Intelligence and Task Complexity?" makes an important empirical observation: frontier models show increasing output variance
When Regulators Ask the Impossible
Why AI Governance Frameworks and Generative AI Are Fundamentally Incompatible
The Scenario
You're eight months into deploying an AI system for clinical decision support. Internal reviews are passed, you decided not
Constitutional AI and the Compression Target Problem
The recent surge in "Constitutional AI" discourse reveals both progress and confusion in AI governance. While Constitutional AI represents an improvement over pure RLHF, it doesn't solve the fundamental