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Master Explainable AI using AIX360
Learn practical techniques on how to make AI systems transparent, explainable, and trustworthy with IBM's AIX360 library.
Imagine you're applying for a mortgage to buy your dream home. After weeks of gathering documents and waiting anxiously, you receive a simple rejection letter with no explanation. You're left wondering: What went wrong? Was it your income, your down payment, or something in your credit history? Without clear reasons, you have no idea what to fix before trying again. This is where Explainable AI (XAI) makes a difference—transforming vague rejections into specific, actionable feedback that gives you a clear path forward.
As AI becomes increasingly embedded in critical decision-making processes across industries, the ability to understand why and how models reach specific conclusions is essential for building trust, ensuring fairness, and meeting regulatory requirements.
Here's a glimpse into the IBM's AI Explainability 360 (AIX360) decision framework you'll master in this learning path—helping you navigate which explainability techniques work best for different scenarios
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The XAI methodology flowchart illustrates how to choose the right explainability approach based on your specific needs. Don't worry if you find this confusing at first—you'll understand these concepts step by step as you progress through the learning path. The framework starts with a fundamental decision: Do you need to understand the data or the model? Then it guides you through choosing between sample-based or feature-based explanations, local explanations (for individual predictions) or global explanations (for the entire model). The flowchart shows various techniques available in the AIX360 toolkit, including ProtoDash for prototype-based explanations, SHAP for feature importance, TED for user-friendly explanations, and BRCG for directly interpretable models.
By completing this learning path, you'll gain practical experience with diverse explainable AI techniques using IBM's AI Explainability 360 (AIX360) open-source toolkit. Through four guided projects across different domains, you'll develop skills to create AI systems that are not only accurate but also transparent and trustworthy—expertise that's increasingly valuable as organizations face growing demands for responsible AI from regulators, customers, and internal stakeholders.
This expertise is increasingly valuable as organizations face growing demands for responsible AI. Whether enhancing existing models or building new systems with explainability by design, this learning path provides the foundation to implement XAI effectively in your work.
