Artificial Intelligence Integration in Recruitment: Enhancing Efficiency while Navigating Algorithmic Bias and Candidate Experience
DOI:
https://doi.org/10.66931/jmb-365Keywords:
artificial intelligence, recruitment, efficiency, algorithmic bias, candidate experience, SEM-PLSAbstract
This study investigates the integration of Artificial Intelligence (AI) in recruitment and its impact on recruitment efficiency, algorithmic bias, and candidate experience within the context of HIPMI Kota Malang. A quantitative approach was employed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with a sample of 160 respondents consisting of business owners and recruitment practitioners. Data were collected through structured questionnaires and analyzed to examine both direct relationships among variables. The results indicate that AI integration significantly enhances recruitment efficiency, confirming its role in streamlining hiring processes and improving decision-making. However, AI integration is also found to significantly influence algorithmic bias, suggesting potential risks related to fairness and transparency. Furthermore, recruitment efficiency positively affects candidate experience, while algorithmic bias negatively impacts it, highlighting the dual effects of AI implementation. This study contributes theoretically by integrating technological and behavioral perspectives into a comprehensive framework of AI-driven recruitment outcomes. It extends existing literature by demonstrating the simultaneous benefits and risks of AI adoption, particularly in entrepreneurial contexts. Practically, the findings suggest that organizations should adopt responsible AI strategies by balancing efficiency gains with ethical considerations, including transparency and bias mitigation. The study is limited by its regional focus and cross-sectional design; therefore, future research is recommended to explore broader contexts and longitudinal approaches. Overall, this research underscores the importance of aligning technological innovation with fairness and human-centered recruitment practices.
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Copyright (c) 2026 Wisma Miftahur Rozi, Wilni Wahda Ulwiyati (Author)
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