What is Explainable AI (XAI) and Why Does It Matter?

This article explores explainable AI (XAI) fundamentals and its role in building trustworthy models, covering responsible AI principles, development practices, and different types of explanations to help users understand AI decision-making.

Key Insights

• Responsible AI Foundation: Four key principles (fairness, transparency, accountability, privacy) explained through a pizza analogy, forming the foundation for trustworthy AI development.
• XAI Types: Three categories of explainability - data explainability (bias detection), model explainability (understanding architecture), and post-hoc explainability (decision reasoning).
• Audience-Tailored Explanations: Emphasizes that explanations should be customized for different audiences - regulatory, development, and end-user - with varying levels of technical detail.