Using Explainable AI in Decision-Making Applications

AI explainability refers to techniques by which a model can interpret its findings in a way humans can understand. It’s a part of AI functionality that is responsible for explaining how the AI came up with a specific output. The concept can be represented as a black box, as the decision process inside the algorithm is hidden and often can’t be understood even by its designer. We need to compare three factors: Data input, patterns found by the model, model …

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