https://matjournals.net/engineering/index.php/JoCPP/issue/feed 2025-09-12T11:47:21+00:00 Open Journal Systems <p><strong>JoCPP</strong> is a peer reviewed journal in the discipline of Computer Science published by the MAT Journals Pvt. Ltd. It is a print and e-journal focused towards the rapid publication of fundamental research papers on all areas of Parallel Programming. This journal involves the basic principles of writing parallel programs which can be compiled and executed.</p> https://matjournals.net/engineering/index.php/JoCPP/article/view/2438 2025-09-12T11:47:21+00:00 Surya Teja Gunipe chandrasekhar.koppireddy@gmail.com Shaik Fuzaila Farhatunnisa chandrasekhar.koppireddy@gmail.com Chandra Sekhar Koppireddy chandrasekhar.koppireddy@gmail.com

<p><em>As Artificial Intelligence (AI) systems become more sophisticated, the demand for transparency and interpretability grows. This paper explores the emerging domain of Explainable Artificial Intelligence (XAI), highlighting its importance in enhancing trust, accountability, and decision support in AI-driven systems. It examines both symbolic and sub-symbolic models, with a particular focus on deep neural networks and their ability to aggregate and process complex data. Furthermore, this survey reviews recent research efforts in XAI across key sectors such as finance, healthcare, and autonomous systems. The study outlines the advantages of XAI, including improved user trust, reduced cognitive burden, and better management of computational overhead. It also identifies critical gaps in current research, proposing future directions that emphasize human-centered design, human-cognitive alignment, and explainability directives. Ultimately, the paper argues that explainability is essential to the efficacy and ethical deployment of AI-operated decision-making systems.</em></p>

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