EXPLAINABLE AI (XAI): ENHANCING TRANSPARENCY AND TRUST IN AI SYSTEMS
Keywords:
Explainable Artificial Intelligence (XAI), Trust, Transparency, SHAP (SHapley Additive exPlanations), Healthcare DiagnosticsAbstract
Explainable Artificial Intelligence (XAI) has emerged as a critical field addressing transparency and trust issues inherent in AI systems. This paper presents a comprehensive review of XAI methodologies, with a focus on their applications and the trade-offs between model accuracy and interpretability. A simulated case study in healthcare diagnostics demonstrates the practical utility of XAI techniques like SHAP (SHapley Additive exPlanations). The analysis highlights the importance of features such as tumor size and texture in predicting malignancy, providing insights into the model’s decision-making process. The paper concludes with a discussion of future directions in XAI, emphasizing the need for standardized evaluation metrics and hybrid models that balance transparency and performance.
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