Intermediate · Safety

Model Interpretability

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

TL;DR. The degree to which a human can understand the causality of an AI model's prediction.

Technical Definition

The degree to which a human can understand the causality of an AI model's prediction.

How it works

Model interpretability is a core aspect of Explainable AI (XAI), focusing on deciphering how an AI system arrived at a particular decision. It's crucial for building trust, enabling debugging, and ensuring fairness, especially in 'black box' deep learning models. Techniques range from feature importance scores to visualizing internal activations.

Related Concepts

• Explainable AI (XAI) — A set of techniques that allow humans to understand the output of AI models, especially deep learning models.
• Algorithmic Transparency — The ability to understand how and why an algorithm makes specific decisions or predictions.