WIA StandardsKOEN

WIA Explainable AI Standard Guide

A guide to the WIA-AI-009 standard

Contents

Chapter 1Introduction to Explainable AIArtificial Intelligence has permeated every aspect of modern life, from recommending movies to diagnosing diseases, from approving loans to piloting autonomous… Chapter 2Explanation Types and FrameworksExplainable AI encompasses a diverse ecosystem of methods, techniques, and approaches. To navigate this complexity, we need robust taxonomies that categorize… Chapter 3SHAP: Shapley Additive ExplanationsSHAP (SHapley Additive exPlanations) represents one of the most theoretically grounded and widely adopted approaches to explainable AI. Developed by Scott… Chapter 4LIME and Local ExplanationsLIME (Local Interpretable Model-agnostic Explanations), introduced by Marco Ribeiro, Sameer Singh, and Carlos Guestrin in 2016, is a representative… Chapter 5Attention Mechanisms and Neural InterpretabilityAttention mechanisms greatly improved the performance of deep learning and also offer clues for looking inside models. Unlike SHAP and LIME, which explain… Chapter 6Trust Metrics and ValidationAn explanation is only valuable if it's trustworthy. But how do we measure the quality of an explanation? The WIA-AI-009 standard defines comprehensive trust… Chapter 7Implementation and IntegrationExplainability should not be an afterthought. The WIA-AI-009 standard advocates for "explainability by design," integrating XAI capabilities throughout the… Chapter 8Ethics, Regulation, and Future DirectionsExplainable AI is not merely a technical challenge—it is a moral imperative. As AI systems make decisions that affect lives, livelihoods, and fundamental…

About this book

Artificial Intelligence has permeated every aspect of modern life, from recommending movies to diagnosing diseases, from approving loans to piloting autonomous vehicles. As these systems make increasingly consequential decisions, a fundamental question arises: Can we trust what we don't understand?

Explainable AI (XAI) emerges as a critical response to this question. It represents a paradigm shift from the traditional "black box" approach to AI, where models make decisions through opaque processes, to a transparent framework where AI systems can justify, explain, and be held accountable for their outputs.

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