International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 03 | Mar 2025
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
Next-Gen Recruitment Systems: AI for Automated Talent Screening and Interviewing Shivam Patel1, Dikesh Chouhan2, Vidhi Kaiwart3, Priyanka Rajak4 1 2 3 B.Tech Student, Department of Computer Science and Engineering, LCIT, Bilaspur (C.G.), India 4Assistant Professor, Department of Computer Science and Engineering, LCIT, Bilaspur (C.G.), India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract
- AI-driven systems have revolutionized recruitment by automating talent screening and interviews, improving efficiency, accuracy, and scalability. These systems leverage machine learning to evaluate candidates' qualifications and emotional intelligence while minimizing biases. However, ethical concerns around fairness, transparency, and privacy persist. This paper explores AI's role in transforming recruitment, its benefits, challenges, and future potential. It also examines the implications for HR management and ethical practices.
discrimination. Furthermore, candidates often express skepticism about AI-based interviews, fearing a lack of human interaction, impersonal assessments, and potential inaccuracies in evaluating soft skills. The black-box nature of some AI algorithms also raises concerns about accountability, as recruiters may struggle to understand or justify AIgenerated decisions. As AI recruitment continues to evolve, it is crucial to address these concerns and develop ethical, transparent, and inclusive hiring frameworks. Companies must ensure that AIdriven hiring tools are regularly audited for bias, trained on diverse datasets, and aligned with legal and ethical standards. Hybrid recruitment models, where AI automates initial screening while human recruiters make final hiring decisions, may offer a balanced approach to leveraging AI’s efficiency without compromising fairness. Additionally, advancements in explainable AI (XAI) could enhance transparency by allowing recruiters and candidates to understand how hiring decisions are made.
Keywords: AI, automated talent screening, recruitment, machine learning, ethical considerations.
1.INTRODUCTION The rapid advancement of Artificial Intelligence (AI) has revolutionized various industries, with recruitment being one of the most transformative areas. Traditional hiring processes often involve manual resume screening, timeconsuming interviews, and subjective decision-making, leading to inefficiencies, increased costs, and potential biases. As organizations strive for faster, data-driven, and fair hiring practices, AI-driven recruitment systems have emerged as a promising solution. These systems leverage machine learning algorithms, natural language processing (NLP), and predictive analytics to automate talent screening, rank candidates, and even conduct preliminary interviews. By analyzing vast amounts of candidate data, AI can identify top talent more efficiently than human recruiters, significantly reducing hiring time and operational expenses. Additionally, AI-powered tools can enhance objectivity by focusing on skills, qualifications, and performance metrics rather than unconscious human biases. The integration of AI in recruitment also enables organizations to access a wider talent pool, improve job-candidate matching, and streamline the hiring pipeline.
This research explores the effectiveness, limitations, and future potential of AI-driven recruitment systems, aiming to establish best practices for responsible AI implementation in talent acquisition. By examining both the technological advancements and ethical implications, this study seeks to contribute to the development of next-generation recruitment solutions that balance efficiency with fairness, ensuring a more inclusive and data-driven hiring process. As organizations continue to adopt AI in hiring, understanding its long-term impact on workforce diversity, candidate experience, and recruitment ethics will be essential for shaping the future of talent acquisition .
2. LITERATURE REVIEW The rapid development of Artificial Intelligence (AI) and machine learning (ML) technologies has revolutionized human resources (HR) and recruitment, particularly through AI-powered automated interview systems and talent searching tools. These systems use AI algorithms to assess candidates during interviews, analyzing responses, tone, language, and body language to enhance recruitment efficiency and scalability. AI-powered tools, such as those utilizing natural language processing (NLP) and predictive analytics, are increasingly adopted to improve accuracy,
However, despite these advantages, AI-driven hiring poses significant challenges, including algorithmic bias, lack of transparency, and ethical concerns related to data privacy and fairness. Many critics argue that AI models, if not properly trained, may reinforce existing hiring disparities and disadvantage certain demographic groups. Since AI relies on historical hiring data, there is a risk that biased past decisions could be perpetuated, leading to unintended
© 2025, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 391