Active Measurement: Efficient Estimation at Scale

Max Hamilton, Jinlin Lai, Wenlong Zhao, Subhransu Maji, Daniel Sheldon

Keywords: importance sampling, image detection, ai for science, counting, human-in-the-loop, unbiased estimators

TL;DR: 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.

Abstract: AI has the potential to transform scientific discovery by analyzing vast datasets with little human effort. However, current workflows often do not provide the accuracy or statistical guarantees that are needed. We introduce \emph{active measurement}, a human-in-the-loop AI framework for scientific measurement. An AI model is used to predict measurements for individual units, which are then sampled for human labeling using importance sampling. With each new set of human labels, the AI model is improved and an unbiased Monte Carlo estimate of the total measurement is refined. Active measurement can provide precise estimates even with an imperfect AI model, and requires little human effort when the AI model is very accurate. We derive novel estimators, weighting schemes, and confidence intervals, and show that active measurement reduces estimation error compared to alternatives in several measurement tasks.

Primary Area: General machine learning (supervised, unsupervised, online, active, etc.)

Submission Number: 4935