A Review of XAI Models and Applications: Recent Developments and Future Trends

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Mote Ashwini Gopinath

A Review of XAI Models and Applications: Recent Developments and Future Trends

Authors: Mote Ashwini Gopinath

Unique Paper ID: 165353
Volume: 11
Issue: 1
PageNo: 849-853

Keywords: Machine Learning Deep Learning Explanation Explainable AI Healthcare

Abstract:
Explainable Artificial Intelligence (XAI) has become increasingly indispensable as AI systems permeate diverse sectors, necessitating transparency and interpretability for user trust and regulatory compliance. This systematic review paper comprehensively examines recent developments and future trends in XAI models and applications. Artificial Intelligence (AI) uses systems and machines to simulate human intelligence and solve common real-world problems. Machine learning and deep learning are Artificial intelligence technologies that use algorithms to predict outcomes more accurately without relying on human intervention. However, the opaque black box model and cumulative model complexity can be used to achieve. Explainable Artificial Intelligence (XAI) is a term that refers to Artificial Intelligence (AI) that can provide explanations for their decision or predictions to human users. XAI aims to increase the transparency, trustworthiness and accountability of AI system, especially when they are used for high-stakes application such as healthcare, finance or security. This paper offers systematic literature review of XAI approaches with different application

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 1
PageNo: 849-853

A Review of XAI Models and Applications: Recent Developments and Future Trends

Available:https://ijirt.org/article?manuscript=165353

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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