The Role of Explainable AI in Enhancing Trust and Transparency
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Abstract
As AI systems become increasingly integrated into decision-making processes, the need for explainability has gained prominence. This paper investigates the role of Explainable AI (XAI) in fostering trust and transparency among users and stakeholders. We explore various XAI techniques, including SHAP, LIME, and counterfactual explanations, and evaluate their effectiveness across domains such as healthcare, finance, and law. Experimental results demonstrate how XAI can improve user understanding and acceptance of AI decisions while addressing ethical concerns. Recommendations for implementing XAI in real-world applications are provided.
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