# Explainability (XAI)

Explainability, or XAI, is the ability to understand and communicate why an AI system produced an output or recommendation.

## Why it matters

Explanations help people inspect and challenge AI-supported decisions, but a fluent explanation is not proof that it faithfully represents the model’s internal process. Teams should pair explanations with source evidence, uncertainty, model documentation, and outcome testing.

## Example

A risk-review tool shows the records and features that influenced a flag, the system’s confidence, and a route for an analyst to correct the result.

## FAQ

### What is Explainability (XAI) in simple terms?

Explainability, or XAI, is the ability to understand and communicate why an AI system produced an output or recommendation.

### Why does Explainability (XAI) matter for teams using AI?

Explanations help people inspect and challenge AI-supported decisions, but a fluent explanation is not proof that it faithfully represents the model’s internal process. Teams should pair explanations with source evidence, uncertainty, model documentation, and outcome testing.

### What is a practical example of Explainability (XAI)?

A risk-review tool shows the records and features that influenced a flag, the system’s confidence, and a route for an analyst to correct the result.


Source: https://www.luffy.so/ai-glossary/explainability-xai

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