explainability in neural networks
Explainability in neural networks refers to the ability to understand and interpret the decisions or predictions made by the network. It involves techniques and methods that enable us to provide insights into the internal workings of the network, such as identifying important features or patterns that contribute to the output. The goal is to make the decision-making process of neural networks more transparent and understandable, promoting trust and accountability in their usage.
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