AI in Job Matching and Recruitment: Analyzing the Efficiency and Equity of Automated Hiring Processes

The pervasive integration of Artificial Intelligence (AI) into job matching and recruitment processes has ignited significant discourse around its dual promises of efficiency enhancement and the promotion of equity. This paper undertakes a meticulous examination of the ramifications of AI in automated hiring, concentrating on both its efficiency and equity facets. We introduce a pioneering framework designed to assess and ameliorate the effectiveness of AI-powered recruitment systems. A comprehensive literature survey illuminates the prevailing landscape of $A I$ in recruitment, elucidating the associated advantages, challenges, and ethical considerations. Our proposed methodology involves the creation of an implementation model founded on principles of algorithmic transparency, fairness, and accountability. This model is then applied in an empirical study, encompassing data collection and analysis, to evaluate accuracy, identify biases, and measure the system’s overall performance. The implementation model, detailed in this paper, encapsulates best practices for the design and deployment of AI-driven recruitment systems, emphasizing the mitigation of biases and the enhancement of transparency. The results of the empirical study furnish insights into the efficiency and equity dimensions of the proposed model. This research contributes to the ongoing dialogue on AI in recruitment by addressing concerns of biases and offering pragmatic recommendations for refining these systems’ efficiency and equity.