[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127229-en":3,"doc-seo-127229-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127229,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Fair and Transparent AI-Driven Resume Screening - Enhancing Recruitment with Bias-Aware Machine Learning","Growing job-application volumes make resume screening time-consuming and difficult for recruiters. Traditional keyword-based approaches often miss true semantic relevance and can introduce unfair candidate selection. An AI-driven Intelligent Resume Sorting System is proposed using NLP and machine learning for automated categorization. The workflow combines TF-IDF, BERT embeddings, and deep learning classifiers to extract key attributes, achieving 93% accuracy and reducing processing time by over 50%, while standardizing evaluation criteria to mitigate bias and improve fairness in hiring.","Fair and Transparent AI-Driven Resume Screening: Enhancing Recruitment with Bias-Aware Machine Learning  \nSEEJPH Volume XXVI, 2025, ISSN: 2197-5248; Posted:04-01-25  \nFair and Transparent AI-Driven Resume Screening: Enhancing Recruitment with Bias-Aware Machine Learning  \nKamal Shah1,Manish Rana2,Trusha Pimple3  \n1St. John College of Engineering & Management (SJCEM) Palghar, Mumbai, India 2St. John College of Engineering & Management (SJCEM) Palghar, Mumbai, India  \n3St. John College of Engineering & Management (SJCEM) Palghar, Mumbai, India  \nKEYWORDS  \nAI-driven recruitment, resume screening, Natural Language Processing, Machine Learning, bias mitigation, deep learning, recruitment automation.  \nABSTRACT:  \nThe increasing volume of job applications has made resume screening a timeconsuming and challenging task for recruiters. Traditional keyword-based filtering methods often fail to capture the true relevance of resumes to job descriptions, leading to inefficiencies and potential biases in candidate selection. To address these challenges, we propose an AI-driven Intelligent Resume Sorting System that leverages Natural Language Processing (NLP) and Machine Learning techniques for automated resume categorization. The system employs TF-IDF, BERT embeddings, and deep learning classifiers to extract and analyze key resume attributes, ensuring accurate classification based on job roles. Our model achieves 93% accuracy, significantly outperforming traditional screening methods while reducing processing time by over 50% . Additionally, by minimizing human intervention, our approach enhances fairness and mitigates biases in recruitment. This research contributes to the advancement of AI-driven hiring solutions, offering a scalable, efficient, and equitable method for modern talent acquisition.  \n1. Introduction  \nIn today's competitive job market, organizations face the daunting task of efficiently and effectively screening a vast number of resumes to identify the most suitable candidates. Traditional resume screening methods, which often involve manual review, are increasingly proving inadequate due to several inherent challenges. Recruiters frequently encounter an overwhelming volume of applications, many of which are irrelevant to the job requirements, leading to significant time consumption and potential fatigue. This scenario not only delays the hiring process but also increases the risk of overlooking qualified candidates.  \nMoreover, traditional screening methods tend to focus heavily on candidates' past experiencesand educational backgrounds, which may not accurately predict future job performance. This emphasis can result in the exclusion of individuals with unconventional career paths or those who have acquired relevant skills through non-traditional means. Additionally, manual screening is susceptible to unconscious biases, potentially leading to a lack of diversity within the organization.  \nTo address these challenges, many organizations are turning to artificial intelligence (AI) and machine learning (ML) technologies to enhance the resume screening process. AI-driven systems can efficiently process large volumes of applications, identifying candidates whose skills and experiences align closely with job requirements. By leveraging natural language processing (NLP) techniques, these systems can analyze the context and relevance of information presented in resumes, going beyond simple keyword matching to assess the true suitability of candidates.  \nFurthermore, AI-powered screening tools have the potential to mitigate human biases by standardizing the evaluation criteria and focusing on objective data points. However, it is  \n1Dr. Kamal Shah: Principal (SJCEM), Professor of Information Technology, St. John College of Engineering &  \nManagement (SJCEM) Palghar-401404, [INDIA. E-Mail: kamal.shah@sjcem.edu.in](INDIA. E-Mail: kamal.shah@sjcem.edu.in).  \n2Dr. Manish Rana: Associate Professor of Information System, St. John Col","cbCaihnNGS3LxtEj","https://ap.wps.com/l/cbCaihnNGS3LxtEj","pdf",304054,1,16,"English","en",105,"# 1. Introduction\n# 2. Problem Definition\n## Bias and fairness challenges\n## Interpretability and transparency issues","[{\"question\":\"What limitations affect traditional resume screening methods?\",\"answer\":\"Traditional methods often rely on keyword filtering and manual review, which can miss true relevance and increase inefficiencies. They also tend to overemphasize past experience and education while being vulnerable to unconscious bias.\"},{\"question\":\"How does the proposed system perform resume screening?\",\"answer\":\"The system uses an AI-driven approach with NLP and machine learning to automate resume categorization. It leverages TF-IDF, BERT embeddings, and deep learning classifiers to extract and analyze key resume attributes for classification.\"},{\"question\":\"How does the approach address fairness and bias in recruitment?\",\"answer\":\"By minimizing human intervention and standardizing evaluation criteria using objective data, the approach reduces bias risks. It also emphasizes continuous monitoring and refinement because AI can inherit biases from training data.\"}]","Fair and Transparent AI-Driven Resume Screening - Enhancing Recruitment with Bias-Aware Machine Learning | PDF",1785937639,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"fair-and-transparent-ai-driven-resume-screening-enhancing-recruitment-with-bias-aware-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/fair-and-transparent-ai-driven-resume-screening-enhancing-recruitment-with-bias-aware-machine-learning/127229/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What limitations affect traditional resume screening methods?","Question",{"text":76,"@type":77},"Traditional methods often rely on keyword filtering and manual review, which can miss true relevance and increase inefficiencies. They also tend to overemphasize past experience and education while being vulnerable to unconscious bias.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed system perform resume screening?",{"text":81,"@type":77},"The system uses an AI-driven approach with NLP and machine learning to automate resume categorization. It leverages TF-IDF, BERT embeddings, and deep learning classifiers to extract and analyze key resume attributes for classification.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the approach address fairness and bias in recruitment?",{"text":85,"@type":77},"By minimizing human intervention and standardizing evaluation criteria using objective data, the approach reduces bias risks. 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