About the Course
The FAME Project offers a comprehensive and accessible online course designed to introduce you to Explainable Artificial Intelligence (XAI). In the rapidly evolving AI landscape, the lack of transparency and interpretability in AI models presents significant challenges. Hence, understanding the inner workings of these models and being able to explain their decisions and predictions is crucial for building trust, ensuring fairness, and addressing ethical concerns. This tutorial in nature course introduces explainable AI and provides some practical insights into this field.
The course explores the importance of explainability in AI models and the ethical considerations surrounding this topic, leading to more trustworthy and responsible AI applications. It covers various explainability metrics, their use in interpreting model predictions and decisions, and how they help evaluate fairness and bias in AI models.
Key aspects include model-agnostic explainability techniques like LIME and SHAP, which work across different machine learning models, and rule-based explainability metrics, such as certainty-factor-based and fuzzy logic-based explainability. It consists of the following parts:
1. Introduction to Explainable Artificial Intelligence
1.1 Overview of Explainable Artificial Intelligence
1.2 Importance of explainability in AI models
1.3 Ethical considerations in Explainable AI
2. Explainability Metrics for AI Models
2.1 Different types of explainability metrics
2.2 Evaluating fairness and bias in AI models
2.3 Interpreting model predictions and decisions
3. Model-Agnostic Explainability Techniques
3.1 Introduction to model-agnostic explainability techniques
3.2 LIME (Local Interpretable Model-agnostic Explanations)
3.2 SHAP (SHapley Additive exPlanations)
4. Rule-based Explainability Metrics
4.1 Overview of rule-based explainability metrics
4.2 Certainty-Factor based explainability
4.3 Fuzzy Logic-based explainability
5. Evaluating Explainability Metrics
5.1 Comparing and selecting explainability metrics
5.2 Case studies and real-world applications
6. Best Practices and Future Trends
6.1 Implementing explainability in AI models
6.2 Challenges and limitations of explainability
