Learning Explainable Artificial Intelligence Foundations

Purpose of Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence (XAI) is a research field focused on making AI decisions and predictions understandable to humans. Its main goal is to provide intellectual oversight by explaining the reasoning behind an AI’s outputs, improving transparency and helping users scrutinize automated decision-making—especially in high-stakes settings where safety, fairness, and accountability matter. XAI addresses the “black box” problem in machine learning, where even designers may not know why a model produced a particular result. XAI is closely related to interpretability and transparency. Transparency concerns whether the model’s parameter-learning and label-generation processes can be described and motivated; interpretability concerns whether humans can comprehend the model and its decision basis; and explainability concerns how the system arrived at a given result. XAI techniques include feature- and instance-level explanations (e.g., SHAP, LIME, saliency maps), as well as methods for understanding internal mechanisms (e.g., attention analysis, probing, causal tracing, and circuit discovery). Ultimately, XAI aims to support trust, verification, auditing, and better user experience, while also enabling users to confirm, challenge, and improve knowledge derived from AI systems.

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Black-box versus white-box machine learning

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