References & Citations
Computer Science > Machine Learning
Title: On the Relationship Between Interpretability and Explainability in Machine Learning
(Submitted on 20 Nov 2023 (v1), last revised 25 Apr 2024 (this version, v2))
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 paper, we challenge the common idea that interpretability and explainability are substitutes for one another by listing their principal shortcomings and discussing how both of them mitigate the drawbacks of the other. In doing so, we call for a new perspective on interpretability and explainability, and works targeting both topics simultaneously, leveraging each of their respective assets.
Submission history
From: Benjamin Leblanc [view email][v1] Mon, 20 Nov 2023 02:31:08 GMT (3538kb,D)
[v2] Thu, 25 Apr 2024 12:06:39 GMT (3027kb,D)
Link back to: arXiv, form interface, contact.