AIを発見のために活用する方法 – 科学を迷わせることなく(How to use AI for discovery — without leading science astray)


2023-11-09 カリフォルニア大学バークレー校(UCB)

AIを発見のために活用する方法 – 科学を迷わせることなく(How to use AI for discovery — without leading science astray)
A new statistical technique allows researchers to safely use the predictions obtained from machine learning to test scientific hypotheses. This image shows an artistic interpretation of the technique, called prediction-powered inference, which has been generated by the DALL-E AI system.



予測による推論 Prediction-powered inference

Anastasios N. Angelopoulos,Stephen Bates,Clara Fannjiang ,Michael I. Jordan,and Tijana Zrnic
Science  Published:9 Nov 2023

Editor’s summary

Over the past decade, there has been rapid progress in the development of large-scale machine learning (ML) systems that provide predictions related to various scientific phenomena. Unfortunately, the standard statistical approaches used to calculate confidence intervals and P values from gold standard data lose their statistical validity for ML-derived data. Angelopoulos et al. introduced “prediction-powered inference,” a standardized protocol for constructing valid confidence intervals and P values that enables the power and scale of ML systems to be used as predictors while ensuring responsible and reliable scientific inferences. The method has been demonstrated on a broad range of real datasets and offers a promising statistical approach for using ML to derive scientific conclusions responsibly. —Yury Suleymanov


Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system. The framework yields simple algorithms for computing provably valid confidence intervals for quantities such as means, quantiles, and linear and logistic regression coefficients without making any assumptions about the machine-learning algorithm that supplies the predictions. Furthermore, more accurate predictions translate to smaller confidence intervals. Prediction-powered inference could enable researchers to draw valid and more data-efficient conclusions using machine learning. The benefits of prediction-powered inference were demonstrated with datasets from proteomics, astronomy, genomics, remote sensing, census analysis, and ecology.