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.
