AI-based resume screening leverages machine learning and natural language processing (NLP) to automate the evaluation and ranking of job applicants. Traditional recruitment processes are time-consuming and prone to human bias. Artificial Intelligence (AI) systems enhance efficiency by analyzing resumes, extracting relevant skills, and matching candidate profiles with job descriptions. Modern platforms such as HireVue, Pymetrics, and LinkedIn incorporate AIdriven tools to streamline talent acquisition. This research paper explores the architecture, methodologies, advantages, limitations, and ethical implications of AI-based resume screening systems. The study concludes that while AI significantly improves recruitment efficiency and scalability, transparency, fairness, and bias mitigation remain critical challenges. significantly enhancing the efficiency and objectivity of the hiring process. By analyzing key elements such as skills, experience, education and job-specific keywords, these systems filter and rank candidates, delivering a shortlist of top matches to recruiters. This technology reduces manual effort, minimizes human bias and accelerates decision-making in recruitment. However, challenges such as potential algorithmic bias and overemphasis on keyword matching highlight the need for careful design and oversight. This abstract explores the functionality, benefits, and implications of AI-powered resume screening systems, underscoring their transformative role in modern human resource management. In today’s competitive job market, organizations receive a large number of applications for each job opening, making manual resume screening a time-consuming and inefficient process. Traditional recruitment methods are often prone to human bias, inconsistency, and delayed decision-making. To address these challenges, this research proposes an AI-Based Resume Screening System that automates candidate shortlisting using Machine Learning (ML) and Natural Language Processing (NLP) techniques. The proposed system extracts textual information from resumes and job descriptions, preprocesses the data to remove noise, and transforms it into structured numerical representations using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Cosine similarity is applied to measure the relevance between candidate resumes and job requirements, enabling the system to rank applicants based on their suitability. Additionally, supervised machine learning algorithms such as Naïve Bayes and Support Vector Machine (SVM) are implemented to classify resumes into appropriate job categories. The performance of the system is evaluated using standard metrics including accuracy, precision, recall, and F1-score to ensure reliability and effectiveness. The results demonstrate that the AI-based approach significantly reduces manual effort, improves screening speed, and enhances the accuracy of candidate selection. This study highlights the potential of artificial intelligence in transforming traditional recruitment processes into intelligent, data-driven systems. The proposed model provides a scalable and efficient solution for modern hiring challenges and can be further enhanced through the integration of advanced deep learning techniques and real-time analytics in future research. To ensure fairness and transparency, the proposed system incorporates bias detection and mitigation mechanisms during model training. The dataset is analyzed for imbalanced representation across attributes such as education background, experience level, and skill categories. Techniques such as balanced sampling and feature normalization are applied to reduce unintended favoritism toward specific candidate profiles. Furthermore, explainable AI (XAI) methods are used to provide recruiters with a justification for each recommendation, enabling them to understand why a candidate was ranked higher or lower. This improves trust in the automated system and supports human decision-making rather than replacing it entirely. The system architecture follows a modular pipeline consisting of data collection, preprocessing, feature extraction, model training, evaluation, and deployment. Resumes in various formats (PDF, DOCX, TXT) are parsed using text extraction libraries and converted into structured datasets. After preprocessing, vectorized representations are stored in a database and compared against job description vectors in real time. A web-based dashboard allows recruiters to upload job requirements, view ranked candidates, and filter applicants based on customizable criteria such as experience range or required skills. This integration makes the system practical for real-world HR workflows.
Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Applicant Tracking System (ATS), Resume Parsing, Predictive Analytics, Recruitment Automation, Bias Detection, Candidate Ranking Talent Acquisition(ML), Natural Language Processing (NLP), automated resume screening, text classification, candidate ranking, TF-IDF, cosine similarity, Support Vector Machine (SVM), Naïve Bayes, information retrieval, and HR analytics for intelligent recruitment systems.
International Journal of Trend in Scientific Research and Development - IJTSRD having
online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International
Journal which provides rapid publication of your research articles and aims to promote
the theory and practice along with knowledge sharing between researchers, developers,
engineers, students, and practitioners working in and around the world in many areas
like Sciences, Technology, Innovation, Engineering, Agriculture, Management and
many more and it is recommended by all Universities, review articles and short communications
in all subjects. IJTSRD running an International Journal who are proving quality
publication of peer reviewed and refereed international journals from diverse fields
that emphasizes new research, development and their applications. IJTSRD provides
an online access to exchange your research work, technical notes & surveying results
among professionals throughout the world in e-journals. IJTSRD is a fastest growing
and dynamic professional organization. The aim of this organization is to provide
access not only to world class research resources, but through its professionals
aim to bring in a significant transformation in the real of open access journals
and online publishing.