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Artificial Neural Networks, in the context of Explainable AI, refer to a subset of algorithms designed to mimic the human brain's structure and function. These networks consist of interconnected nodes or "neurons" that process information through weighted inputs and activation functions, allowing them to learn and generalize patterns from data. The explainability aspect comes into play as researchers strive to make these models more interpretable, often by visualizing the network's architecture or highlighting crucial features in the input data that drive predictions. This subfield is closely related to Artificial Intelligence Algorithms, Artificial Intelligence, and Technology at large, contributing significantly to their advancement while also posing unique challenges in terms of transparency and accountability.