Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities

TL;DR

This paper reviews the role of interpretable machine learning in scientific discovery, emphasizing validation challenges and opportunities for trustworthy, reproducible data-driven insights across various domains.

Contribution

It provides a comprehensive overview of techniques, validation methods, and open challenges in using interpretable machine learning for discovery in large datasets.

Findings

Discusses types of discoveries in supervised and unsupervised settings

Reviews practical validation approaches like data-splitting and stability

Highlights theoretical results on model selection and uncertainty quantification

Abstract

New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only process, visualize, and make predictions from this big data, but also to make data-driven discoveries. These discoveries are often made using Interpretable Machine Learning, or machine learning models and techniques that yield human understandable insights. In this paper, we discuss and review the field of interpretable machine learning, focusing especially on the techniques as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using Interpretable Machine Learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation from both a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude by highlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven-discoveries.