We introduce a mathematical framework and benchmark to quantify generative-model steerability, reveal large steerability gaps in LLMs and text-to-image models, and show that simple mechanisms can improve steerability.

We present active measurement, a human-in-the-loop AI framework that combines AI predictions with importance sampling, model adaptation, and human labeling to make accurate scientific measurements.