(untitled) (Economics and region, 2025)
The study analyzes the dual effect of AI in public sector recruitment: it increases productivity but risks structural inequalities and algorithmic discrimination, undermining public authority. Current approaches to fairness remain fragmented, creating gaps between efficiency and societal perception. Bias emerges at all recruitment stages. Under the EU “AI Act”, such systems are classified as high-risk technologies requiring mandatory audits. To address this, a composite fairness and non-discrimination index is proposed, integrating technical, ethical, and legal metrics into a unified assessment system. This model enables the identification of vulnerabilities and supports evidence-based decisions. It is substantiated that digitalization requires a systemic methodology for ensuring fairness as a key condition for trust in government algorithms.