• Raj Sunil Shah
• Pragati Ganpat Dehankar
• Vaibhav Vinod Sathe
• Gaurav Chandrashekhar Durge
• Mrunali Vaidya

This review presents an in-depth examination of how artificial intelligence is being utilized to reduce algorithmic bias in modern hiring systems. As companies increasingly rely on automated tools to screen resumes, concerns surrounding fairness, transparency, and unintentional discrimination have become more prominent. Although AI-supported recruitment tools offer substantial improvements in efficiency, they also pose notable risks when the underlying algorithms inherit biased training data or flawed decision patterns. This paper analyzes previously documented instances of AI-driven hiring to identify the benefits achieved as well as the limitations and ethical issues reported in practice. It further discusses the broader consequences of algorithmic bias on organizational culture and society, along with the mitigation techniques recommended in existing research. Using a structured literature review methodology, the study synthesizes findings from scholarly articles, industry assessments, and empirical investigations that propose technical and procedural approaches to minimize bias. The objective is to clarify how AI, natural language processing, and deep learning models are currently being applied to enhance fairness in the recruitment process. The review underscores the ongoing importance of human supervision, transparent system development, and collaborative design frameworks to ensure that AI-enabled hiring remains ethical and equitable.

Resume Screening, AI Recruitment, OCR, BERT, Random Forest, NLP, Machine Learning.

"Smart Hiring AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.b680-b683, December-2025, Available :http://www.jetir.org/papers/JETIR2512185.pdf

"Smart Hiring AI", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. ppb680-b683, December-2025, Available at : http://www.jetir.org/papers/JETIR2512185.pdf