On the Relationship Between Interpretability and Explainability in Machine Learning

arXiv:2311.11491v2 Announce Type: replace
Abstract: Interpretability and explainability have gained more and more attention in the field of machine learning as they are crucial when it comes to high-stakes decisions and troubleshooting. Since both provide information about predictors and their decision process, they are often seen as two independent means for one single end. This view has led to a dichotomous literature: explainability techniques designed for complex black-box models, or interpretable approaches ignoring the many explainability tools. In this position …

abstract arxiv attention cs.ai cs.lg decision decisions explainability independent information interpretability machine machine learning process relationship troubleshooting type