Introduction to Explainable AI (XAI)

Name: Introduction to Explainable AI (XAI)
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Master Explainable AI: Understand, Interpret, and Apply AI Decisions Transparently in Real-World Systems.

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Learning Outcomes

In this free course, you will learn how to:

Explain why explainability is essential for trust, fairness, and accountability in AI
Describe the black-box problem and its ethical and practical implications
Distinguish between intrinsic interpretability and post-hoc explanation methods
Identify common XAI techniques such as LIME, SHAP, and counterfactual explanations
Compare global and local explanations and their uses for different stakeholders
Apply explainability concepts to real-world cases in healthcare, finance, and public services
Evaluate AI explanations using clarity, faithfulness, and usefulness criteria
Create human-centred explanations tailored to different audiences

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