This research article explores the broader relevance and practical applications of Interpretable Machine Learning (IML) in geoscience, emphasizing its potential to enhance process understanding. The authors highlight that IML, which goes beyond traditional machine learning by providing insights into the reasoning behind predictions, can be a valuable tool for geoscientists. They describe a workflow for effectively using IML, including translating research questions into IML tasks, preparing and preprocessing data, training and validating ML models, and implementing interpretations to ensure robustness. The article also identifies common pitfalls in IML applications, such as misinterpreting model interpretations as truths about the underlying data-generating process, and provides good practices to avoid these pitfalls. The goal is to facilitate a broader and more thoughtful integration of IML into Earth science research, making it a valuable tool for advancing our understanding of complex Earth systems.This research article explores the broader relevance and practical applications of Interpretable Machine Learning (IML) in geoscience, emphasizing its potential to enhance process understanding. The authors highlight that IML, which goes beyond traditional machine learning by providing insights into the reasoning behind predictions, can be a valuable tool for geoscientists. They describe a workflow for effectively using IML, including translating research questions into IML tasks, preparing and preprocessing data, training and validating ML models, and implementing interpretations to ensure robustness. The article also identifies common pitfalls in IML applications, such as misinterpreting model interpretations as truths about the underlying data-generating process, and provides good practices to avoid these pitfalls. The goal is to facilitate a broader and more thoughtful integration of IML into Earth science research, making it a valuable tool for advancing our understanding of complex Earth systems.