Intermediate · Evaluation

Model explainability

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

TL;DR. The ability to understand and interpret how an AI model arrives at its decisions or predictions, making its operations transparent.

Technical Definition

The ability to understand and interpret how an AI model arrives at its decisions or predictions, making its operations transparent.

How it works

Model explainability (often XAI, Explainable AI) is crucial for building trust, debugging, and ensuring fairness in AI systems. It provides insights into why a model made a particular prediction, rather than just what the prediction is. For LLMs, this can involve techniques to highlight influential parts of the input or show reasoning steps, aligning with regulatory requirements and ethical principles.

Related Concepts

• Interpretability — Understanding why a model makes the predictions it does, by inspecting its internals.
• Explainable AI (XAI) — A set of techniques that allow humans to understand the output of AI models, especially deep learning models.
• Trustworthy AI — A broad concept encompassing AI systems that are reliable, secure, transparent, fair, and accountable, adhering to ethical principles.
• AI Ethics — A field studying the moral principles and values that should guide the design, development, and use of artificial intelligence.