Explainable AI: Bridging the Gap between AI Models and Human Interpretability

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Professor Emily Chen

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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Explainable AI: Bridging the Gap between AI Models and Human Interpretability. (2024). International Meridian Journal, 6(6). https://meridianjournal.in/index.php/IMJ/article/view/50
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Articles

How to Cite

Explainable AI: Bridging the Gap between AI Models and Human Interpretability. (2024). International Meridian Journal, 6(6). https://meridianjournal.in/index.php/IMJ/article/view/50

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