AI-Based Resume Screening and Job Matching Systems: A Comprehensive Review of NLP, Machine Learning, Explainable AI, and Fairness-Aware Recruitment Approaches

Abstract: Now machines help find workers fast - no need to shuffle piles of paper anymore. Instead of people flipping through endless applications, software reads them in seconds. Because it works quicker, teams avoid delays that slow down hiring. With fewer eyes on each detail, some errors slip away quietly. Machines compare abilities to job needs without tiring. This shift lets staff spend energy talking to promising applicants instead of sorting names. Tasks once done by hand now happen before lunch. Focus moves where it matters most: real conversations after filters do the math. Now machines grasp what words really mean inside a resume, moving past just spotting keywords. Because of smarter algorithms, connections among skills, jobs held, degrees earned, licenses obtained, and role requirements become clear. These links let software spot people who fit well without needing every phrase copied exactly. Starting with patterns found in massive amounts of text, systems build understanding that matches human judgement more closely. Hidden layers within networks detect subtle clues others miss during screening. As training improves, so does precision when sorting applicants by relevance. Less time gets spent reviewing unfit profiles thanks to these behind-the-scenes shifts. Even with progress, hurdles still block broad use of AI in hiring. Problems like skewed algorithms pop up when old hiring records shape new tools. Decisions made by machines often hide how they work, raising red flags around fairness. Personal details of job seekers can slip into grey zones without clear safeguards. Outcomes might tilt against certain groups if past imbalances feed the system. Making sense of AI choices matters more now than ever before. Efforts focus on building methods that reveal their reasoning step by step. Fairness built into code helps balance the scales where bias once lived unseen. Trust grows only when processes open up to inspection. Researchers dig into ways that show not just results, but why those results happen. This piece looks into recent studies about how artificial intelligence helps sort resumes and match people to jobs, using tools like TF-IDF, cosine similarity, transformers, and small neural setups, along with big language models. Each method shows distinct pros and cons when tested in varied hiring environments – some work faster, others understand context better. Efficiency gets a boost through AI, alongside sharper matches between applicants and roles, raising the standard of who gets hired. Still, what comes next needs clearer logic behind decisions, room for human judgment, strong ethics, plus ongoing checks for unfair patterns. When smart tech teams up with mindful choices, companies might shape hiring systems that perform well while staying just, open, and dependable - for those offering jobs as much as those seeking them.

Keywords: Artificial Intelligence, Resume Screening, Job Matching, Natural Language Processing, Machine Learning, Explainable Artificial Intelligence, Recruitment Automation, Fairness, Semantic Similarity, Large Language Models.