International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Autonomous HR Agent using Machine Learning |
|---|---|
| ABSTRACT | Human Resource Management (HRM) is a fundamental organizational function encompassing recruitment, employee development, performance evaluation, and strategic workforce planning. As organizations undergo digital transformation, HR departments increasingly face challenges associated with managing vast volumes of heterogeneous data, including structured records, resumes, performance appraisals, job descriptions, and unstructured feedback reports [1]. Traditional HR workflows, which rely heavily on manual screening, hu- man judgment, and rule-based decision-making, are often time- consuming, inconsistent, and susceptible to subjective biases [2]. Such inefficiencies can lead to delays in recruitment, suboptimal talent allocation, and increased operational costs. Recent advancements in Artificial Intelligence (AI) and Ma- chine Learning (ML) have introduced the possibility of devel- oping intelligent systems capable of automating complex, data- driven decision-making processes. In particular, Natural Language Processing (NLP) techniques have demonstrated significant potential in analyzing unstructured text, enabling tasks such as automated resume parsing, skill extraction, semantic similarity assessment, and job-candidate matching [4]. Despite these advances, most current AI-based HR tools function primarily as assistive technologies, requiring human oversight and lacking full autonomy or adaptive capabilities. This paper proposes an Autonomous HR Agent that integrates state-of-the-art NLP and machine learning techniques to provide end-to-end recruitment automation, including resume analysis, candidate evaluation, ranking, and decision support. The system is designed to operate with minimal human intervention, ensuring consistent, scalable, and objective assessments of candidates. The proposed approach also incorporates fairness-aware algorithms to mitigate potential biases in automated decision-making. Experimental evaluation demonstrates the system’s capability to significantly reduce processing time, enhance recruitment efficiency, and improve decision consistency relative to conventional HR approaches. The findings underscore the feasibility and benefits of deploying autonomous AI agents in real-world HR environments [3]. |
| AUTHOR | VAYUPUTHRA RAJ URS, AKSHATHA S PATIL, SHALINI H S, THOUDURU SHIVANANDA, PROF. SHADAKSHARAIAH C Department of Computer Science and Design, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312136 |
| pdf/136_Autonomous HR Agent using Machine Learning.pdf | |
| KEYWORDS | |
| References | [1] R. Feldman and J. Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data, Cambridge University Press,2007. [2] C. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008. [3] T. Mikolov et al., “Efficient Estimation of Word Representations in Vector Space,” Proceedings of the International Conference on Learning Representations (ICLR), 2013. [4] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd Edition, Morgan Kaufmann, 2012. [5] J. Brown et al., Language Models for Intelligent Decision Making, OpenAI Research Publications, 2024. [6] T. Mikolov et al., Distributed Representations of Words and Phrases, MIT Press, Extended Edition, 2022. [7] A. Ghosh and S. Ghosh, Intelligent Recruitment Systems Using Artificial Intelligence, Elsevier Publications, 2023. [8] D. Jurafsky and J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 3rd Edition, Pearson Education, 2023. [9] M. Allahyari et al., A Brief Survey of Text Mining: Classification, Clustering and Information Extraction, Springer Nature, 2022. [10] S. Singh and R. Sharma, Intelligent HR Analytics Using Machine Learning and NLP, Springer Nature, 2025. |
