Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{198336,
        author = {SUHAS BHIMRAO VEER},
        title = {Explainable Artificial Intelligence (XAI) for Decision Transparency},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11094-11105},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198336},
        abstract = {Artificial Intelligence (AI) systems are increasingly being used in critical domains such as healthcare, finance, and cyber security. However, many AI models, especially deep learning models, operate as “black boxes,” making their decision-making processes opaque.
This lack of transparency reduces trust, accountability, and adoption. Explainable Artificial Intelligence (XAI) aims to address this challenge by providing interpretable and understandable insights into AI decisions. This paper presents a comprehensive study of XAI techniques, their importance, methodologies, and applications.
It also proposes a lightweight XAI framework for enhancing decision transparency in machine learning models. Experimental results demonstrate improved interpretability without significantly compromising model performance.},
        keywords = {Explainable AI, XAI, Machine Learning, Decision Transparency, Interpretability, Deep Learning, Trustworthy AI},
        month = {April},
        }