Interpretability In mathematical logic, interpretability is a relation between formal theories that expresses the possibility of interpreting or translating one into the other.

AI interpretability is the ability to understand and explain the decision-making processes that power artificial intelligence models.

Jul 23, 2025 · What is Model Interpretability? Model interpretability refers to the ability to understand and explain how a machine learning or deep learning model makes its predictions or decisions.

Lipton (2018) says of interpretability that it “reflects several distinct concepts,” which is to say that it is used inconsistently, or at best equivocally.

Oct 10, 2024 · Interpretability describes how easily a human can understand why a machine learning model made a decision. In short, the more interpretable a model is, the more straightforward it is to …

Explainability refers to the ability of a model to provide clear and understandable explanations for its predictions or decisions. Interpretability, on the other hand, focuses on the ability to understand and …

Interpretability is about mapping an abstract concept from the models into an understandable form. Explainability is a stronger term requiring interpretability and additional context.

Learn how model interpretability helps teams understand model decisions, reduce bias, meet regulatory requirements, and build trust in AI-powered workflows.

Aug 13, 2025 · Model interpretability is all about making a machine learning model’s decisions understandable to humans. Instead of being a black box where inputs go in and predictions come out …

The meaning of INTERPRETABILITY is the quality or state of being interpretable. How to use interpretability in a sentence.