<![CDATA[Artificial intelligence (AI) has emerged as a transformative technology in enterprise talent acquisition, offering significant opportunities to improve recruitment efficiency through automated candidate screening, intelligent job matching, workforce analytics, and predictive hiring strategies. Organizations increasingly adopt AI-driven recruitment systems to reduce hiring costs, accelerate decision-making processes, and enhance workforce planning capabilities. However, despite these operational advantages, concerns remain regarding algorithmic bias, fairness, transparency, and regulatory compliance. This study investigates the balance between AI-enabled recruitment efficiency and the ethical, legal, and governance challenges associated with algorithmic decision-making in talent acquisition. A systematic review of 34 peer-reviewed studies spanning computer science, organizational psychology, human resource management, and legal scholarship was conducted to identify key trends, opportunities, and risks in AI-based recruitment systems. The analysis reveals the emergence of five levels of talent acquisition maturity, ranging from traditional applicant tracking systems and data-driven workforce acquisition to predictive talent acquisition and fully autonomous recruiting models. The findings indicate that advanced machine learning techniques, including XGBoost and Random Forest algorithms, can achieve predictive accuracies of up to 96% in employee attrition forecasting and workforce optimization tasks. Nevertheless, the study also demonstrates that such systems frequently inherit demographic and historical biases embedded within training datasets, potentially leading to discriminatory hiring outcomes when adequate oversight mechanisms are absent. Furthermore, the review identifies significant compliance challenges related to emerging regulations, including New York City Local Law 144, Illinois HB 3773, and the European Union AI Act. The findings suggest that sustainable AI-driven recruitment requires the integration of bias auditing frameworks, explainability mechanisms, human-in-the-loop governance, and continuous regulatory compliance monitoring. The study concludes that the long-term success of AI-enabled talent acquisition depends not only on technological performance but also on the ability to ensure fairness, accountability, transparency, and ethical decision-making throughout the recruitment lifecycle]]>
