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Learn to design transparent and interpretable AI systems through a practical framework that replaces black-box models with explainable architectures. Explore how to break down complex AI tasks into smaller, modular components where each model detects specific features, then connects these outputs through domain-driven knowledge graphs that provide clear reasoning paths. Discover the architecture behind this approach, examine real-world applications in high-stakes industries like healthcare and manufacturing, and see a live demonstration of how the system works on classification tasks. Gain practical tools for building responsible AI systems that not only make accurate predictions but also explain their decision-making process step-by-step, making artificial intelligence more trustworthy and understandable for both developers and end users.
