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

6 days ago · Artificial intelligence Mechanistic interpretability New techniques are giving researchers a glimpse at the inner workings of AI models.

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 …

2 days ago · Comprehensive study notes on explainability, interpretability, and human oversight in AI, covering practical examples, industry relevance, and regulatory requirements.

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.

3 days ago · Why interpretability lags behind capability Interpretability has not kept pace with model size for structural reasons.

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 …

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

Jul 23, 2024 · Interpretability is about how well a human can understand the internal mechanics of a machine learning system, while explainability is about how well the internal mechanics of a machine …

Jan 16, 2025 · The article highlights the differences between explainability and interpretability, and explains how these concepts contribute to building trust in AI systems, while also addressing their …