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

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

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 …

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.

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

Aug 20, 2025 · To better understand their inner workings, two main approaches exist: mechanistic interpretability (precise but impractical) and representation interpretability (practical but imprecise).

Interpretability is defined as the degree to which an algorithm's internal workings or parameters can be understood and examined by humans. It involves how the effectiveness of the algorithm's output is …

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 …

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

AI interpretability, often referred to as Explainable AI (XAI), is the ability for humans to understand the reasoning behind an AI system's decisions or predictions.