Course: Explainable Artificial Intelligence (XAI) Concepts
This chapter, titled "Introduction to Explainable AI," lays the foundation for understanding how AI systems justify their decisions. Explainable AI, or XAI, encompasses methods that render AI outputs understandable to humans. Unlike traditional black-box models, XAI seeks to illuminate the reasoning behind predictions and actions. The module underscores why explainability matters as AI becomes embedded in healthcare, finance, and governance. By clarifying the inputs and the inferred logic, XAI helps users evaluate reliability and appropriateness. A key distinction is between interpretability and explainability, where interpretability deals with human comprehension of causes, and explainability concerns explicit reasons given for decisions. Model-agnostic methods like LIME and SHAP, as well as inherently transparent models such as decision trees, illustrate different paths to explainability. Visual explanations, such as feature attributions and saliency maps, make complex models more accessible to non-experts. The chapter acknowledges challenges, including the trade-off between accuracy and interpretability and the lack of a one-size-fits-all solution. It argues that explanations need to be tailored to different audiences, since what is meaningful to a data scientist may differ from what a patient or a jurist requires. The ultimate aim of XAI is to build trust, support regulatory compliance, and enable better decision-making. It also highlights potential biases to be uncovered through explanation and the obligation to address them. The chapter concludes by previewing the techniques and real-world examples that will be explored in subsequent sections. In this way, readers are invited to embark on a thoughtful journey toward transparent and responsible AI.