The following are the top three Explainable AI (XAI) aspects:
Transparency: Refers to the clarity with which an AI system operates. A transparent AI system is going to process the extract model parameters from training data and generate labels from testing data which then would be described and motivated by the approach designer.
Interpretability: Refers to the degree to which a user can understand the cause of a decision made by an AI system. Essentially, interpretability is providing meaningful insights into the key factors behind an AI's operations.
Explainability: Refers to the ability of AI to explain its decisions in a way that is understandable to users. An explainable AI model should provide detailed and understandable explanations for its predictions, helping users to comprehend the decision-making process of the AI based system.
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