Calibrated Confidence and Controlled Autonomy – Why Probability Discipline Determines the Fate of Agentic AI
Why raw scores lie, and why calibration saves reputations Raw model scores often look like probabilities while behaving like mood rings. Gradient boosting, SVMs, and many neural nets output numbers that rank cases well, yet those numbers rarely match real-world odds. A “0.8” score might mean 0.55 in production, and teams still treat it like…
You must be logged in to post a comment.