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

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

5 days ago · Interpretability is a concept in trustworthy AI research that is focused on understanding of the inner workings and decisions that come from the AI system. Our previous research revealed that …

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.

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 …

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 …

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

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.

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

Interpretability in machine learning refers to the ability to understand, explain, and describe how AI models make decisions. It’s about making the inner workings of complex algorithms transparent …