How 'Random' Choices Expose AI Training Data

This experiment demonstrates that AI models' 'random' outputs are actually biased fingerprints of their training data. By analyzing which numbers models default to, researchers can profile a model's dataset and uncover encoded cultural references—essentially using OSINT techniques on the AI itself. This reveals critical vulnerabilities in how models encode their training sources and the bias embedded within.