Explainable AI: Bridging the Gap between AI Models and Human Interpretability
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Abstract
Explainable Artificial Intelligence (XAI) has emerged as a critical research area aimed at enhancing the transparency and interpretability of AI models. This paper reviews the significance of XAI in addressing the "black-box" nature of complex AI algorithms and discusses various techniques for explaining AI decision-making processes, including feature importance analysis, model-agnostic methods, and post-hoc explanations. Through a comparative analysis of XAI approaches and their implications for different stakeholders, this paper elucidates the importance of integrating interpretability into AI systems for improved trust, accountability, and societal acceptance.
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