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Duration 14 hours
Course Outline
Foundations of Explainable AI
- Defining Explainable AI (XAI).
- The role of transparency in AI models.
- Major challenges in AI interpretability.
Core XAI Methodologies
- Model-agnostic approaches: LIME, SHAP.
- Explainability methods specific to model architectures.
- Interpreting decisions made by black-box models.
Practical Application of XAI Tools
- Overview of open-source XAI libraries.
- Implementing XAI in basic machine learning models.
- Visualizing model explanations and behavior.
Navigating Explainability Challenges
- Trade-offs between accuracy and interpretability.
- Constraints of existing XAI methods.
- Addressing bias and fairness in explainable models.
Ethical Perspectives in XAI
- Evaluating the ethical impact of AI transparency.
- Balancing explainability with model performance.
- Addressing privacy and data protection issues in XAI.
Practical Applications of XAI
- XAI in sectors such as healthcare, finance, and law enforcement.
- Regulatory standards for explainability.
- Fostering trust in AI systems through transparency.
Advanced XAI Topics
- Investigating counterfactual explanations.
- Explaining neural networks and deep learning models.
- Interpreting complex AI systems.
Future Directions in Explainable AI
- New techniques in XAI research.
- Opportunities and hurdles for future AI transparency.
- The influence of XAI on responsible AI development.
Requirements
- A solid grasp of fundamental machine learning concepts.
- Proficiency in Python programming.
Target Audience
- Individuals new to AI.
- Enthusiasts in data science.
