Intermediate · Fundamentals

Explainability (XAI)

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Techniques making AI decisions understandable to humans, crucial for trust and regulatory compliance.

Technical Definition

Techniques making AI decisions understandable to humans, crucial for trust and regulatory compliance.

How it works

XAI techniques include LIME (local interpretable approximations), SHAP (game-theory feature attribution), attention visualization, and saliency maps. Regulations like the EU AI Act increasingly require explainability for high-risk applications.

Related Concepts

• Neural Network — A computing system inspired by biological neural networks that learns patterns from data through interconnected layers of nodes.
• Attention Mechanism — A technique that lets models dynamically focus on the most relevant parts of the input when producing each output element.
• Deep Learning — A subset of machine learning using neural networks with many layers to learn hierarchical representations from large datasets.