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Duration 14 hours
Course Outline
Foundations of Explainable AI
- Defining Explainable AI (XAI)
- The critical role of transparency in AI modeling
- Primary obstacles in achieving AI interpretability
Core XAI Methodologies
- Model-agnostic approaches: LIME, SHAP
- Explainability techniques specific to certain models
- Deciphering decisions generated by black-box systems
Practical Application of XAI Tools
- Overview of open-source XAI libraries
- Integrating XAI into simple machine learning workflows
- Visualizing model behavior and explanations
Navigating Explainability Challenges
- Trade-offs between accuracy and interpretability
- Constraints of contemporary XAI methods
- Addressing bias and ensuring fairness in explainable models
Ethical Dimensions of XAI
- Analyzing the ethical consequences of AI transparency
- Striking a balance between explainability and performance
- Managing privacy and data protection issues within XAI
Practical Implementations of XAI
- Applications in healthcare, finance, and law enforcement
- Compliance with regulatory explainability standards
- Establishing trust in AI systems via transparency
Advanced Topics in Explainability
- Investigating counterfactual explanation techniques
- Interpreting neural networks and deep learning architectures
- Decoding complex AI system behaviors
Emerging Trends in XAI
- Novel techniques advancing XAI research
- Future hurdles and prospects for AI transparency
- The influence of XAI on the development of responsible AI
Requirements
- Fundamental grasp of machine learning principles
- Proficiency in Python programming
Target Audience
- Novices in Artificial Intelligence
- Data science practitioners
