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{194248,
        author = {Robin Kumar and Mr. Neeraj Kumar},
        title = {Explainable Artificial Intelligence Framework for Ethical and Transparent Recruitment Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {3163-3167},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194248},
        abstract = {Artificial intelligence has significantly transformed recruitment processes by enabling automated resume screening and candidate evaluation. Organizations increasingly rely on machine learning models to analyze candidate profiles and match them with job requirements. However, most AI-driven recruitment systems operate as black-box models, where decision-making processes remain opaque to recruiters and job applicants. This lack of transparency can lead to concerns regarding algorithmic bias, unfair candidate rejection, and reduced trust in automated hiring systems.
This research proposes an Explainable Artificial Intelligence (XAI) framework designed to improve transparency and fairness in recruitment systems. The proposed framework integrates machine learning classification models with interpretability techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations). These techniques enable recruiters to understand which features of candidate resumes influence classification decisions.
The framework utilizes natural language processing techniques for resume preprocessing and feature extraction using TF-IDF vectors. Machine learning models such as Random Forest and Support Vector Machines are used for candidate classification. Experimental results indicate that the proposed explainable AI framework maintains high classification accuracy while providing interpretable insights into model decisions. The study demonstrates that explainable AI improves trust, accountability, and fairness in AI-driven recruitment systems.},
        keywords = {Explainable Artificial Intelligence, Recruitment Systems, Machine Learning, Resume Classification, Ethical AI, Transparency},
        month = {March},
        }