AI Explainability

What Is AI Explainability? The Simple Definition

The process of interpreting how a model arrived at its decision in a way that humans can understand. Explainability is important to gain trust in the AI system and to make sure AI is logical, robust, and fair.

The Technical Definition

AI explainability, also known as interpretable AI or XAI (Explainable AI), is the capability of an artificial intelligence system to provide clear and understandable explanations regarding its decision-making process, predictions, or recommendations in a human-readable manner.

It aims to make AI models and their inner workings more transparent and comprehensible to both experts and non-experts.

AI explainability is crucial for various reasons, including improving trust in AI systems, complying with regulatory requirements (especially in sectors like healthcare and finance), and enabling domain experts to diagnose and address model biases or errors effectively. Achieving AI explainability often involves the use of specific algorithms and tools, such as feature importance techniques, model visualization methods, and rule-based systems that make AI decisions more transparent and interpretable.

Explain It Like I’m Five

Sure! AI explainability is like asking a smart robot to explain why it made a decision. Imagine you have a robot that helps you pick what clothes to wear. Instead of just saying “wear this,” it tells you why.

It might say, “Wear this shirt because it’s hot outside,” or “Put on these pants because it might rain later.” AI explainability is about making AI systems tell us why they do things in a way we can understand, like having a conversation with a helpful robot friend.

Use It At The Water Cooler

How to use “AI explainability” in a sentence at work:

“At the team meeting, let’s discuss how we can improve AI explainability to make our AI-powered tools more user-friendly and transparent for our customers.”

Related Terms

Machine Learning

Additional Resources