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Course Outline
Introduction to Explainable AI and Ethics
- The need for explainability in AI systems
- Challenges in AI ethics and fairness
- Overview of regulatory and ethical standards
XAI Techniques for Ethical AI
- Model-agnostic methods: LIME, SHAP
- Bias detection techniques in AI models
- Handling interpretability in complex AI systems
Transparency and Accountability in AI
- Designing transparent AI systems
- Ensuring accountability in AI decision-making
- Auditing AI systems for fairness
Fairness and Bias Mitigation in AI
- Detecting and addressing bias in AI models
- Ensuring fairness across different demographic groups
- Implementing ethical guidelines in AI development
Regulatory and Ethical Frameworks
- Overview of AI ethics standards
- Understanding AI regulations in different industries
- Aligning AI systems with GDPR, CCPA, and other frameworks
Real-World Applications of XAI in Ethical AI
- Explainability in healthcare AI
- Building transparent AI systems in finance
- Deploying ethical AI in law enforcement
Future Trends in XAI and Ethical AI
- Emerging trends in explainability research
- New techniques for fairness and bias detection
- Opportunities for ethical AI development in the future
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning models.
- Familiarity with AI development processes and frameworks.
- A keen interest in AI ethics and transparency.
Audience
- AI ethicists.
- AI developers.
- Data scientists.
14 Hours
