Open Data vs Open Source: What Your AI Was Trained On Matters

Training data transparency for AI models is as important as open-sourcing model code. Without knowing what data a model was trained on, users can't accurately assess its capabilities or blind spots. The analogy used is food labeling: just as consumers want to know ingredients and sourcing, companies deploying AI should demand training data transparency. Models trained on censored or incomplete data — such as those omitting certain historical events or security vulnerability information — will have systematic blind spots that can silently harm enterprise users who rely on them for security-sensitive tasks.