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Duration 14 hours
Course Outline
Foundations of Explainable AI
- Defining Explainable AI (XAI)
- The role of transparency in AI model development
- Primary obstacles in achieving AI interpretability
Core XAI Methodologies
- Model-agnostic approaches: LIME, SHAP
- Model-specific interpretability techniques
- Decoding decisions from black-box models
Practical Application of XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into simple machine learning models
- Visualizing model behavior and explanations
Challenges in Explainability
- Managing the trade-off between accuracy and interpretability
- Current limitations in XAI methods
- Addressing bias and ensuring fairness in explainable models
Ethical Dimensions of XAI
- Evaluating the ethical impact of AI transparency
- Striking a balance between explainability and model performance
- Navigating privacy and data protection issues in XAI
Practical Applications of XAI
- Utilizing XAI in healthcare, finance, and law enforcement
- Complying with regulatory standards for explainability
- Fostering trust in AI systems via transparency
Advanced XAI Principles
- Investigating counterfactual explanations
- Interpreting neural networks and deep learning architectures
- Understanding complex AI systems
Emerging Trends in Explainable AI
- Novel techniques in XAI research
- Future challenges and opportunities for AI transparency
- The influence of XAI on responsible AI development
Requirements
- A foundational grasp of machine learning concepts
- Proficiency in Python programming
Target Audience
- Individuals new to AI
- Data science practitioners and enthusiasts
