Explainable AI (XAI) for Ethical AI Development

• 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

This course explores how Explainable AI (XAI) can be applied to build AI systems that are not only performant but also transparent, fair, and aligned with ethical guidelines. It aims to provide participants with the knowledge and skills to implement XAI techniques that promote fairness, detect bias, and ensure AI accountability.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to apply Explainable AI techniques to ensure fairness, transparency, and ethical AI systems.

By the end of this training, participants will be able to:

• Understand the role of XAI in ethical AI systems.
• Implement XAI techniques to detect and mitigate bias in AI models.
• Ensure transparency in AI model decision-making processes.
• Align AI development with ethical and regulatory standards.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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