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Learning Modules for Explainable AI with Python

Here, we outline a learning structure for you, including some GitHub repositories to follow. This suggested order of curated list of resources, including video lectures, websites, and research papers, shall help students complete the lab exercise. These resources should provide you with a solid foundation for understanding the ethical considerations, explainability, and potential biases in AI systems. Since AI continues to advance, it's essential to address these issues to ensure responsible development and deployment.

Introduction to Explainable AI

Feature Importance and Local Interpretable Model-agnostic Explanations (LIME)

Layer-wise Relevance Propagation (LRP)

Class Activation Mapping (CAM)

Grad-CAM: Visual Explanations from Deep Networks

Image Saliency

Integrated Gradients

Ethics in AI

Data Bias and Model Understanding

Additional Resources & Case Studies

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