What is Explainable AI (XAI)?

Explainable AI (XAI) refers to methods and techniques in the field of AI that make the decision-making process of AI systems understandable to humans. The goal of XAI is to create AI systems that are transparent, interpretable, and trustworthy.

Why is XAI important?

XAI is important for several reasons. It can help users understand and trust the decisions made by AI systems, which is particularly important in high-stakes domains like healthcare or finance. It can also help developers debug and improve AI systems, and can contribute to the ethical and fair use of AI.

Despite its importance, achieving XAI is challenging, particularly for complex models like deep neural networks, which are often described as "black boxes".

How is XAI achieved?

XAI can be achieved through various methods, depending on the type of AI system. For rule-based systems, the rules themselves provide a form of explanation. For machine learning models, techniques like feature importance, partial dependence plots, or surrogate models can be used. For deep learning, techniques like saliency maps, activation maximization, or layer-wise relevance propagation can be used.