Artificial intelligence, particularly machine learning (ML) models, more understandable to humans. Right now, many AI systems work in a way that can be difficult to follow, almost like a "black box" where the inner workings are hidden.In many AI models, particularly deep learning, the system processes large amounts of data through multiple complex layers. Each layer learns different patterns in the data, but as this happens, it becomes harder for us to understand each step of the decision-making.

Examples of Explainable AI Techniques.
Feature Importance.
Saliency Maps in Medical Imaging.
Decision Trees.
Model-Agnostic Methods.

Why Explainability Matters.
Building Trust.
Improving AI Models.
Safety and Accountability.

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