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

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 …

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.

4 days ago · Model interpretability techniques are methods that help humans understand how an artificial intelligence model makes decisions. These techniques reveal which inputs influence …

19 hours ago · Most discussions about interpretability in AI start with the wrong question. Researchers, practitioners, and even regulators often ask whether a model is interpretable. But this framing …

We argue that artificial networks are explainable and offer a novel theory of interpretability.

3 days ago · Interpretability vs. Explainability: Interpretability is intrinsic transparency, while explainability uses tools to clarify complex model outputs. Ethics & Bias Detection: Explainability …

In practice, the term interpretability is used to refer to a number of distinct concepts. We want to answer the question ‘to what extent does machine learning need to be interpretable to provide …

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

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 …