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Duration 14 hours
Course Outline
Foundations of Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI models
- Primary obstacles to AI interpretability
Core XAI Methodologies
- Model-agnostic approaches: LIME, SHAP
- Explainability strategies specific to model types
- Decoding decisions from black-box models
Practical XAI Tooling
- Overview of open-source XAI libraries
- Applying XAI to straightforward machine learning models
- Visualizing model behavior and explanations
Navigating Explainability Challenges
- The trade-off between accuracy and interpretability
- Constraints of existing XAI methods
- Addressing bias and fairness in explainable models
Ethical Dimensions of XAI
- Evaluating the ethical impact of AI transparency
- Striking a balance between explainability and performance
- Managing privacy and data protection in XAI
Practical XAI Implementations
- XAI applications in healthcare, finance, and law enforcement
- Compliance with regulatory explainability mandates
- Fostering trust in AI systems via transparency
Advanced XAI Frameworks
- Investigating counterfactual explanations
- Interpreting neural networks and deep learning architectures
- Analyzing complex AI systems
Future Directions in Explainable AI
- Novel techniques in XAI research
- Prospective challenges and opportunities for AI transparency
- The influence of XAI on responsible AI development
Requirements
- Foundational knowledge of machine learning principles
- Proficiency in Python programming
Target Audience
- AI novices
- Data science enthusiasts
