Transformations in Talent Acquisition: Measuring AI’s Effects on Recruitment Efficiency and Bias

Abstract

<ns3:p>Background Artificial Intelligence (AI) is reshaping recruitment processes, offering new opportunities for efficiency, precision, and fairness. Yet, its real impact on organisational hiring practices remains underexplored through robust empirical methods. This study investigates the effects of AI tools on recruitment across multiple dimensions. Methods A quantitative cross-sectional survey was conducted among 423 human resource professionals across multiple industries (manufacturing, technology, financial services, and logistics) between January and March 2025. The structured questionnaire comprised 28 closed-ended items organised into four dimensions: recruitment efficiency, candidate experience, perceived fairness and bias, and trust and transparency. Data were analysed using descriptive statistics, exploratory factor analysis (EFA), and multiple linear regression models. Results AI significantly reduced time-to-hire and improved initial screening accuracy (β = 0.61, p &lt; 0.001 for recruitment efficiency). Respondents reported enhanced candidate experience (β = 0.38, p &lt; 0.01) due to more structured and responsive communication. However, bias mitigation showed only modest effects (β = 0.21, p &lt; 0.05), and trust and transparency were not significantly improved by AI deployment alone (β = 0.08, n.s.). Concerns persisted regarding algorithmic opacity, data privacy, and the potential for amplifying hidden biases. Conclusions AI tools significantly improve recruitment efficiency and contribute to structured candidate experiences. However, these technological gains are accompanied by persistent concerns related to fairness, transparency, and the risk of reproducing systemic biases. Meaningful adoption of AI in human resources requires hybrid human–machine decision-making models with deliberate ethical safeguards, transparency features, and human oversight.</ns3:p>