Explain Black-Box Models

Ready to unlock the mystery behind your most powerful models? This Short Course was created to help data analysis professionals accomplish transparent and trustworthy AI implementation. By completing this course, you'll master SHAP values for executive communication, systematically compare explainability methods, and align explanation strategies with stakeholder needs. By the end of this course, you will be able to: Apply SHAP values to a black-box model and produce feature-importance visuals interpretable by non-technical executives Evaluate two XAI methods (LIME vs. SHAP) for fidelity and stability on the same model and dataset Apply counterfactual and surrogate-model explanations to the same black-box model and compare stakeholder preference scores Evaluate explanation completeness using fidelity metrics and recommend the superior approach This course is unique because it bridges advanced explainability techniques with business communication, ensuring complex model insights drive informed decision-making. To be successful in this project, you should have a background in Python programming and machine learning fundamentals

• Explain model behavior clearly for non-technical stakeholders
• Select explanation methods based on fidelity and stability
• Align explanation strategies with stakeholder needs
• Evaluate explanation quality using fidelity metrics

• Basic familiarity with relevant terminology
• Willingness to engage in applied exercises or case studies