Explainable AI: Methods and Applications


Authors : Jishnu Setia

Volume/Issue : Volume 8 - 2023, Issue 10 - October

Google Scholar : https://tinyurl.com/d7s9xnm4

Scribd : https://tinyurl.com/55tbxpm8

DOI : https://doi.org/10.5281/zenodo.10021461

Abstract : Explainable Artificial Intelligence (XAI) has emerged as a critical area of research, ensuring that AI systems are transparent, interpretable, and accountable. This paper provides a comprehensive overview of various methods and applications of Explainable AI. We delve into the importance of interpretability in AI models, explore different techniques for making complex AI models understandable, and discuss real-world applications where explainability is crucial. Through this paper, I aim to shed light on the advancements in the field of XAI and its potentialto bridge the gap between AI's predictions and human understanding.

Keywords : Explainable AI (XAI), Interpretable Machine Learning, Transparent AI, AI Transparency, Interpretability in AI, Ethical AI, Explainable Machine Learning Models, Model Transparency, AI Accountability, Trustworthy AI, AI Ethics, XAI Techniques, LIME (Local Interpretable Model- agnostic Explanations), SHAP (SHapley Additive exPlanations), Rule-based Explanation, Post-hoc Explanation, AI and Society, Human-AI Collaboration, AI Regulation, Trust in Artificial Intelligence.

Explainable Artificial Intelligence (XAI) has emerged as a critical area of research, ensuring that AI systems are transparent, interpretable, and accountable. This paper provides a comprehensive overview of various methods and applications of Explainable AI. We delve into the importance of interpretability in AI models, explore different techniques for making complex AI models understandable, and discuss real-world applications where explainability is crucial. Through this paper, I aim to shed light on the advancements in the field of XAI and its potentialto bridge the gap between AI's predictions and human understanding.

Keywords : Explainable AI (XAI), Interpretable Machine Learning, Transparent AI, AI Transparency, Interpretability in AI, Ethical AI, Explainable Machine Learning Models, Model Transparency, AI Accountability, Trustworthy AI, AI Ethics, XAI Techniques, LIME (Local Interpretable Model- agnostic Explanations), SHAP (SHapley Additive exPlanations), Rule-based Explanation, Post-hoc Explanation, AI and Society, Human-AI Collaboration, AI Regulation, Trust in Artificial Intelligence.

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