ML-powered scientific research: Possibilities and pitfalls

From applications in structural biology to the analysis of electronic health record data, predictions from machine learning models increasingly complement costly gold-standard data in scientific inquiry. While "using predictions as data" enables scientific studies to scale in an unprecedented manner, appropriately accounting for inaccuracies in the predictions is critical to achieving trustworthy conclusions from downstream statistical inference.

In this talk, I will explore the methodological and practical impacts of using predictions as data across various applications. I will introduce our recently proposed method for bias correction and draw connections with modern methods and classical statistical approaches dating back to the 1960s. I will also discuss ethical challenges of using predictions as data, underscoring the need for careful and thoughtful adoption of this practice in scientific research.