AI Glossary
Explainable AI
An approach that helps people understand how AI systems generate outputs, recommendations, or decisions.
Explainable AI
Overview
Many AI systems are highly capable.
However, understanding how they reach a particular result can sometimes be difficult.
This challenge has led to the development of Explainable AI, often abbreviated as XAI.
Explainable AI refers to methods and practices that help people understand how AI systems generate outputs, recommendations, or decisions.
A helpful way to think about explainability is showing your work on a math problem.
The final answer matters.
But understanding how the answer was reached can be equally important.
Explainability helps improve trust, accountability, and confidence in AI systems.
It is particularly important in areas such as healthcare, finance, government, and other environments where decisions can have significant consequences.
As organizations increasingly rely on AI, explainability is becoming an important component of responsible AI programs.
Why It Matters
Explainable AI helps people understand, evaluate, and trust AI-generated outputs.
Real-World Example
A financial institution may provide explanations for why an AI system recommended approving or declining an application.