XAI (Explainable AI)

Explainable Artificial Intelligence (XAI) refers to the set of methods, techniques, and tools that make artificial intelligence models more transparent and understandable. Many modern models, particularly deep neural networks, are often described as "black boxes" due to their complexity and the opacity of their decision-making. XAI seeks to provide intelligible explanations for users, regulators, and decision-makers.

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The importance of XAI is twofold. On one hand, it builds trust in AI systems by making it possible to understand why a model produced a given output (for example, why a medical model predicts that a patient is at risk). On the other hand, it plays a crucial role in regulatory compliance, especially in sensitive sectors such as healthcare, finance, and public administration, where automated decisions must be justified and auditable.

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Several approaches are used to achieve explainability in AI:

• Post-hoc methods: techniques applied after model training, such as LIME (Local Interpretable Model-Agnostic Explanations) or SHAP (SHapley Additive exPlanations), which attribute importance scores to input variables in a prediction.
• Interpretable models by design: algorithms that are inherently understandable, such as decision trees, rule-based systems, or linear models.
• Visualization and feature analysis: in computer vision, for example, saliency maps are used to highlight which areas of an image most influenced the model’s prediction.

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XAI has become a priority research field with the rise of AI in critical domains. It is supported by major international initiatives such as the DARPA XAI program and European frameworks for trustworthy AI, and is considered a cornerstone for developing responsible, auditable, and trustworthy AI systems.

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References:

• DARPA XAI Program: https://www.darpa.mil/program/explainable-artificial-intelligence
• Wikipedia – Explainable artificial intelligence: https://en.wikipedia.org/wiki/Explainable_artificial_intelligence
• Ribeiro et al. (2016), “Why Should I Trust You?” Explaining the Predictions of Any Classifier (LIME)
• Lundberg & Lee (2017), A Unified Approach to Interpreting Model Predictions (SHAP)