Evaluating Explainable AI: Stability and Fairness Metrics

As artificial intelligence increasingly influences critical decisions, understanding why a model makes a prediction is no longer optional—it must be explainable, stable, and fair. This text-based course guides you through the fundamental principles of evaluating Explainable AI (XAI) systems to ensure they are both robust and equitable. You will transition from simply generating AI explanations to critically evaluating their quality and consistency. By studying key evaluation metrics, you will learn how to detect when explanations are unstable, inconsistent, or biased, enabling you to develop machine learning models that stakeholders can genuinely trust. What you'll learn: - Understand the foundational definitions of Explainable AI and why rigorous evaluation metrics are essential. - Analyze Relative Input Stability to measure the robustness of explanations against minor data perturbations. - Evaluate fairness metrics to identify and mitigate bias in AI model explanations, with a focus on image classifiers. - Compare local and global explanation methods to determine the best evaluation strategy for your system. - Apply modern ethical AI guidelines to assess the social impact and equity of algorithmic decisions. The course begins with core terminology and foundational concepts of explainability before guiding you through the practical application of stability and fairness metrics. You will read through clear theoretical explanations and study structured code snippets to solidify your conceptual understanding. This course is designed for beginner data scientists, AI ethicists, software developers, and technical product managers. No prior experience with advanced explainability frameworks is required. Start reading today to master the metrics that make artificial intelligence transparent, stable, and fair.