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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
- Basic knowledge of machine learning models
- Familiarity with AI development and frameworks
- Interest in AI ethics and transparency
Audience
- AI ethicists
- AI developers
- Data scientists
14 Hours
Testimonials (1)
The details of the mathematical formulas that highlight the biases and limitations as well as the important parameters to consider for the practical application of LIME and SHAPE methods.
Heddy Bouron Cardey - Bayer
Course - Introduction to Explainable AI (XAI) for Beginners
Machine Translated
