What is Grad-CAM and how does it explain CNN decisions?

Grad-CAM computes the gradients of the target class score with respect to the final convolutional feature maps, creating a coarse 2D heatmap showing which image regions influenced the decision.

Why is SHAP mathematically preferred over simple feature importances?

Because SHAP guarantees efficiency, symmetry, dummy player fairness, and additivity based on axiomatic cooperative game theory.

Home ASSEB AI HubUnit 5: Explainability in AI / XAI

Class XIIASSEB-527Unit 5: Explainability in AI / XAI

Unit 5: Explainability in AI / XAI

Master official ASSEB Class XII AI Unit 5: Explainable AI (XAI), Model Interpretability, SHAP, LIME, Grad-CAM, and Accuracy vs Interpretability Trade-offs.

Theory: 8 Marks

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Practical: 5 Marks

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12 min read

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Learning Outcomes

Understand why Explainability in AI (XAI) is critical in regulated domains such as healthcare, justice, and credit scoring.
Analyze the fundamental trade-off between model accuracy and model interpretability.
Master SHAP (SHapley Additive exPlanations) and its cooperative game theory foundation.
Understand LIME (Local Interpretable Model-agnostic Explanations) and local surrogate modeling.
Explore Grad-CAM (Gradient-weighted Class Activation Mapping) for visualizing CNN visual decision regions.
Use SHAP and Scikit-Learn in Python to generate feature importance explanations for student evaluations.

Board Exam Scoring Tips

Draw the trade-off graph: X-axis = Interpretability, Y-axis = Accuracy. Place Deep Learning at High Accuracy / Low Interpretability, and Decision Trees at High Interpretability / Low Accuracy.
Define SHAP clearly: 'A game-theoretic approach to explain the output of any machine learning model using fair marginal contributions.'
Define Grad-CAM: 'A technique that produces a visual heatmap highlighting the important regions in an image for predicting a class concept.'

Key Takeaways

Complex deep neural networks are 'black boxes'; high predictive accuracy is dangerous if developers cannot explain why an automated decision was made.
Inherently interpretable models (Linear Regression, Decision Trees) offer direct transparent rules but lower accuracy on perceptual tasks.
SHAP uses game-theoretic Shapley values to fairly distribute credit among features for a given prediction.
Grad-CAM uses gradient backpropagation to the final convolutional layer to generate visual heatmaps showing which pixels triggered a classifier.
ASSEB examiners award full marks for connecting XAI directly to ethical AI accountability and compliance standards.

Practice Unit MCQ Quiz

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