Abstract

The increasing integration of Artificial Intelligence (AI) in recruitment is reshaping traditional hiring processes. While AI has proven effective in increasing efficiency in companies, its adoption raises concerns about transparency, bias, and ethical governance. This paper examines the role of Artificial Intelligence (AI) in Knowledge Management (KM) for talent acquisition, highlighting its potential to enhance efficiency while addressing critical ethical and transparency concerns. The primary objective of this research is to evaluate the mechanisms required for AI-driven recruitment systems to function ethically and effectively. Through a review and synthesis of contemporary literature and exploring perspectives on AI's role in talent acquisition. This research evaluates AI’s impact on recruitment processes, exploring governance mechanisms and hybrid models for ethical AI integration. Findings indicate that AI can streamline hiring practices but require regulatory oversight and algorithmic transparency to ensure fairness and remain compliant with evolving legal frameworks. The paper concludes that AI’s success in KM depends on structured governance frameworks, empirical validation, and an adaptive human-AI collaboration model to optimize recruitment outcomes while maintaining ethical integrity. This study contributes to ongoing discussions on AI recruitment by offering insights for both researchers and practitioners. By approaching AI adoption with intention and accountability, businesses can harness their potential without compromising fairness or human judgment. This paper contributes to the body of knowledge by presenting strategies for mitigating biases in AI systems, such as using diverse datasets and retraining algorithms, which can directly influence recruitment outcomes. For practitioners, the real-world implications are clear: AI-powered recruitment systems must be carefully implemented with a focus on human oversight and bias mitigation strategies. Managers and executives can apply the insights from this research to establish AI governance frameworks within their organizations that promote fairness and transparency while ensuring compliance with ethical standards. The paper also emphasizes the importance of fostering collaboration between AI tools and human recruiters to ensure that AI enhances rather than replaces the human aspect of candidate evaluation. Future research could build upon this study by conducting empirical research to validate the proposed hybrid AI-human collaboration model and assess its effectiveness in different organizational contexts. Case studies and quantitative data could help in measuring the impact of AI tools on recruitment outcomes, including the diversity and quality of hires, as well as the cost-benefit analysis of implementing AI systems. Further studies could also explore how AI’s role in recruitment evolves over time and how recruiters and candidates adapt to these technological changes. Additionally, research focusing on industry-specific applications of AI in recruitment could provide valuable insights into the nuanced challenges and opportunities in sectors that heavily rely on AI, such as healthcare or finance. Finally, exploring the ethical and legal dimensions of AI adoption in recruitment, including data privacy concerns and the development of comprehensive governance frameworks, would further enhance our understanding of responsible AI integration in talent acquisition.

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