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Course Outline
Introduction to Explainable AI and Ethics
- The necessity of explainability in AI systems.
- Challenges related to AI ethics and fairness.
- Overview of regulatory and ethical standards.
XAI Techniques for Ethical AI
- Model-agnostic methods: LIME, SHAP.
- Techniques for detecting bias in AI models.
- Managing 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 various demographic groups.
- Implementing ethical guidelines in AI development.
Regulatory and Ethical Frameworks
- Overview of AI ethics standards.
- Understanding AI regulations across 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 environments and frameworks.
- A strong interest in AI ethics and transparency.
Target Audience
- AI ethicists.
- AI developers.
- Data scientists.
14 Hours
