Explainable AI frameworks focus on making AI decisions interpretable and transparent, which is critical for accountability. Frameworks such as LIME, SHAP, and counterfactual explanations enable stakeholders to understand why an agent took certain actions, providing a basis for auditing and ensuring the agent’s decisions align with ethical and legal standards.
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