Advanced Resume Screening with Artificial Intelligence to Enhance Recruitment Process

Abstract

This research provides a critical analysis of how artificial intelligence (AI) influences biases in recruitment within the changing landscape of human resources and talent acquisition. It presents a comprehensive analysis and implementation of an A.I. powered resume analyzer and screening system that uses generative artificial intelligence to enhance the efficiency, fairness and scalability of the recruitment process. The core of this system is built using Google’s Gemini generative AI model, integrated with a Streamlit-based web application, designed to evaluate the quality and validity of a candidate’s resume against a given job description with precision and explainability. Instead of using traditional machine learning techniques which involve using large resume data and training and evaluating the model, this project utilizes prompt engineering and instruction-based reasoning to guide the AI model, allowing it to interpret resumes, extract relevant skills, match keywords, and generate insightful summaries without prior dataset-specific training. This technique allows for a faster and flexible development cycle. The system provides structured outputs in JSON format which includes a resume matching (JD-score) score, missing and matching keywords in the job description, candidate’s strengths and weaknesses in the resume, further resume improvement suggestions, and an XAI (Explainable AI) feature that elaborates the parameters used to calculate the score. It evaluates aspects such as the presence of key technical skills, toolsets, project relevance and domain expertise, all while reducing the human bias and human workload that often comes with manual screening. The system's XAI module further enhances the recruiter’s trust by explaining the logic behind each score, highlighting where points were awarded or deducted based on skill presence, experience, formatting, and domain relevance. While the system significantly improves speed, accuracy, and impartiality in candidate shortlisting, it relies on human intervention in assessing soft skills, cultural background, and interpersonal attributes that AI cannot effectively evaluate. Thus, this hybrid AI-human model offers an effective solution for resume analysis and recruitment process which combines AI’s speed and human intervention to evaluate soft skill, thus creating the ideal way to recruit candidates. The study concludes that by combining the analytical strength of AI with the contextual intelligence of human evaluators, organizations can create a complete hiring technology that minimizes bias, enhances decision-making, and improves transparency.

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