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Duration 14 hours
Course Outline
Foundations of Explainable AI
- Defining Explainable AI (XAI)
- The significance of transparency in AI models
- Primary challenges in AI interpretability
Core XAI Methodologies
- Model-agnostic approaches: LIME and SHAP
- Explainability techniques specific to certain models
- Interpreting decisions from black-box models
Practical XAI Implementation
- Overview of open-source XAI libraries
- Integrating XAI into basic machine learning models
- Visualizing model explanations and behavior
Navigating Explainability Challenges
- Balancing accuracy against interpretability
- Limitations of existing XAI methods
- Addressing bias and ensuring fairness in explainable models
Ethical Dimensions of XAI
- Examining the ethical consequences of AI transparency
- Striking a balance between explainability and performance
- Managing privacy and data protection issues in XAI
Practical Applications of XAI
- Use of XAI in healthcare, finance, and law enforcement
- Regulatory mandates for model explainability
- Enhancing trust in AI systems via transparency
Advanced XAI Concepts
- Investigating counterfactual explanations
- Interpreting neural networks and deep learning architectures
- Making sense of complex AI systems
Future Directions in Explainable AI
- Novel developments in XAI research
- Future challenges and opportunities for AI transparency
- The role of XAI in promoting responsible AI development
Requirements
- A fundamental grasp of machine learning concepts
- Basic proficiency in Python programming
Intended Audience
- Individuals new to AI concepts
- Data science enthusiasts
