Explainability methods like SHAP, LIME, and Integrated Gradients are widely adopted in production ML systems; but run three different methods on the same model and input, and you'll often get three different answers. How do you know which explanation to trust? This hands-on workshop introduces quantitative evaluation of explanations, a rapidly growing area of XAI research that gives practitioners objective tools to measure explanation quality instead of relying on intuition or visual inspection. Working through three progressive Google Colab notebooks, attendees will first generate explanations from multiple methods on a real dataset and see exactly how and why they disagree. Next, they'll apply evaluation metrics across three dimensions: faithfulness (does the explanation reflect what the model actually uses?), robustness (is the explanation stable under small input changes?), and complexity (is the explanation simple enough to act on?). Finally, attendees will use a full evaluation pipeline to build comparison tables and learn a practical decision framework for choosing the right explainer for different deployment scenarios: regulatory audits, customer-facing products, and stakeholder communication. No prior XAI experience is required; attendees need only intermediate Python, familiarity with scikit-learn or PyTorch, and a Google account for Colab. Everyone leaves with working notebooks and the ability to integrate explanation quality checks into their ML pipelines immediately.