Unlocking Transparency in AI: A Comprehensive Guide to Explainable AI (XAI)
Unlocking Transparency in AI: A Comprehensive Guide to Explainable AI (XAI) Research Disclaimer: This guide is based on SHAP v0.44+, LIME v0.2.0+, Captum v0.7+ (PyTorch), and scikit-learn v1.3+ official documentation. All code examples use production-tested patterns for model interpretability. XAI techniques have computational overhead and may not perfectly capture complex model behaviors—always validate explanations against domain expertise. As AI systems make increasingly critical decisions in healthcare, finance, and criminal justice, understanding why a model made a specific prediction is as important as the prediction itself. Explainable AI (XAI) provides interpretability techniques to demystify black-box models, enabling stakeholders to trust, audit, and improve AI systems. ...