Al-Based Recruitment in Startups: A Systematic Literature Review of Applicant Tracking Systems and Hiring Transformation
DOI:
https://doi.org/10.66635/84qjk941Keywords:
Artificial Intelligence, E-Recruitment, Applicant Tracking Systems (ATS), Machine Learning, Recruitment Automation, Startups, Hiring Efficiency, Algorithmic BiasAbstract
This research study employs an exhaustive literature analysis to examine the ways in which startup companies use Applicant Tracking Systems (ATS) to recruit new employees. Examining the ways in which chatbots, natural language processing, and machine learning algorithms impact conventional recruiting practices is the focus of this study. AI-based recruitment systems provide universities with an overview of their research work which shows two main patterns and the positive and negative aspects resulting from their implementation. The implementation of AI systems into recruitment processes creates a substantial increase in operational efficiency because they automate the tasks which involve candidate selection through resume evaluation and identification and first contact with potential applicants. The process creates faster hiring times which result in lower costs while enabling improved decision-making through data-driven insights. The AI system improves candidate experience by delivering customized interactions which enable faster candidate responses. The research study presents urgent problems which academic researchers currently handle because of algorithmic bias and decision-making transparency and ethical issues surrounding fairness and accountability. Organizations can achieve their diversity and inclusion objectives when they implement suitable solutions for these particular challenges. The review shows that organizations need to pursue responsible AI usage which requires ongoing system assessment and human oversight for ethical hiring to work properly.
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