Introduction

As artificial intelligence becomes increasingly integrated into decision-making processes, understanding and interpreting these models is crucial for building trust and ensuring ethical AI systems. This 5-day professional course on "Explainable AI and Model Interpretability" is designed to equip participants with the knowledge and skills needed to make AI models transparent, understandable, and accountable.

Objectives

• Understand the importance of explainability in AI systems.
• Explore various techniques and tools for model interpretability.
• Analyze case studies and real-world applications of explainable AI.
• Develop skills to evaluate and implement interpretability methods.
• Address ethical concerns and regulatory requirements related to AI transparency.

Course Outlines

Day 1: Introduction to Explainable AI

• Overview of AI development and the need for explainability.
• Key concepts and definitions in explainable AI.
• The role of transparency in AI model trustworthiness.
• Introduction to tools and frameworks for AI explainability.
• Discussion on the ethical implications of non-explainable models.

Day 2: Techniques for Model Interpretability

• Local interpretability versus global interpretability.
• Introduction to SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations).
• Visualization techniques for interpreting model decisions.
• Hands-on practice with model interpretability tools in Python.
• Comparison of different interpretability techniques and their applications.

Day 3: Case Studies and Applications

• Real-world examples of explainable AI in healthcare, finance, and other industries.
• Analysis of successful and unsuccessful attempts at model interpretability.
• Guest speakers sharing experiences with implementing explainable AI solutions.
• Interactive group activity: Developing an interpretability plan for a sample model.
• Discussion on the challenges of scaling interpretability methods in large systems.

Day 4: Evaluating and Enhancing Model Interpretability

• Criteria for assessing model interpretability effectiveness.
• Strategies for improving model transparency and user comprehension.
• The role of feedback loops in iterative model improvement.
• Exploring the boundaries of interpretability: What can and cannot be explained?
• Practical session: Evaluating interpretability in student projects.

Day 5: Ethical and Regulatory Considerations

• Understanding the regulatory landscape around AI Transparency (GDPR, CCPA, etc.).
• Addressing bias and fairness in AI models.
• Developing guidelines for ethical AI deployment.
• Workshop: Creating an ethical framework for AI development in an organization.
• Concluding remarks and presentation of certificates.