Explainability Methods & Evaluation

अवलोकन

This course explores advanced Explainable AI (XAI) techniques for interpreting and validating machine learning model behavior. It focuses on methods that move beyond simple feature importance toward mathematically grounded insights into black-box models.

Through structured lessons and practical demonstrations, you will learn how Shapley theory underpins fair feature attribution, how SHAP methods generate local and global explanations, and how surrogate and rule-based approaches approximate model behavior. You will also work with counterfactual and contrastive explanations, including how to generate actionable alternatives and evaluate plausibility under perturbations and adversarial conditions.

The course progresses from mathematical foundations to applied evaluation, emphasizing fidelity, faithfulness, stability, and reliability. Rather than treating explanations as visual outputs, it focuses on critically analyzing whether they accurately reflect model behavior.

By the end of this course, you will be able to:

- Explain the mathematical foundations of Shapley values and fair feature attribution - Apply SHAP techniques such as TreeSHAP, KernelSHAP, and interaction values - Design and evaluate surrogate and rule-based explanation methods - Generate and assess counterfactuals using practical evaluation metrics - Measure explanation quality through fidelity, faithfulness, stability, robustness, and sparsity - Test explanation reliability under perturbations and adversarial manipulation This course is ideal for machine learning engineers, AI researchers, data scientists, and professionals building trustworthy AI systems. A foundational understanding of ML concepts and Python-based model development is recommended; prior experience with explainability techniques is not required.

Join us to learn how to design and validate XAI systems that deliver transparent, reliable insights into machine learning models.

पाठ्यक्रम

• Feature Attribution and Interpretable Modeling
Build a strong foundation in feature attribution and interpretable modeling by learning how predictions can be explained using contribution-based methods. Explore SHAP techniques, simplify black-box models with surrogates, and apply these concepts through hands-on analysis of model behavior and explanation quality.
• Counterfactual and Contrastive Methods
Explore model decisions using alternative and comparison-based explanations. Learn how counterfactuals show what must change for different outcomes, apply constraints for realism, and evaluate their quality. Gain hands-on experience generating and validating explanations, and extend your understanding with contrastive methods to identify differences in predictions.
• Evaluating Explanation Methods
Assess the reliability and meaning of explanation methods by exploring criteria like faithfulness, stability, and robustness. Learn how explanations respond to input changes and adversarial effects, and gain hands-on experience comparing methods from both technical and human perspectives.
• Course Wrap-Up and Assessments
This final module evaluates your understanding of explanation methods and their real-world use. You will explain model predictions using feature attribution, generate counterfactual and contrastive explanations, and assess explanation quality using criteria like faithfulness, stability, and robustness. By the end, you’ll be able to evaluate and communicate reliable, trustworthy model explanations.

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द्वारा पढ़ाया गया

Edureka

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विषय

Artificial Intelligence