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CSA 803: Responsible and Explainable AI
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Module 1: Introduction to Explainable AI (XAI)
Module Notes
Motivation for responsible and explaInable AI
Components of responsible AI
The Necessity of Explainable AI for Responsible AI
Reflect on Your Journey
Quiz
What's Next
Module 2: Intrinsically Interpretable Models
Module Notes
Intrinsic Explainability of Common Models
Interpretability of the K-nearest neighbor algorithm
Factors Reducing Explainability in Simple Models
Quiz
What's Next
Module 3: Permutation Feature Importance
Module Notes
Decline in Model Performance Due to Attribute Shuffling in PFI
Reflect on Your Journey
Quiz
What's Next
Module 4: Individual Condition Expectation Plots and Partial Dependence Plots
Module Notes
Relationships between ICE and PDP plots
Goal of an ICE Plot
Quiz
What's Next
Module 5: Shapley Additive Explanations
Module Notes
Use of SHAP for Local Explanation
Shapley Value and Model Interpretability
Quiz
What's Next
Module 6: Local Interpretable Model-agnostic Explanations (LIME)
Module Notes
Creating Explanations of Image Prediction Using LIME
Advantages of LIME
Quiz
What's Next
Module 7: Class Activation Mapping
Module Notes
Importance of a Global Average Pooling Layer in the CAM Technique
CAM in Explaining the Output of an AI Model
Quiz
What's Next
Module 8: Anchors
Module Notes
The Anchors Algorithm: Choosing Between Competing Rules
Advantages of Anchors
Quiz
What's Next
Module 9: Counterfactual Explanations
Module Notes
Enhancing Fairness in AI Models with Counterfactual Explanations
Purpose of Counterfactual Explanations
Quiz
FINAL EXAMINATIONS
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CSA 803: Responsible and Explainable AI
Lecturer:
Group A
Lecturer:
Selina Ochukut
Course Duration in Hours
:
45
Skill Level
:
Beginner
Course rating
:
Course Objectives
:
