Abstract
This study investigates how Artificial Intelligence (AI) biases in Human Resource Management (HRM) contribute to the persistent 25 .7% gender wage gap in Kazakhstan . The research aims to establish how algorithmic systems, often perceived as «objective», codify historical and structural discrimination into mathematical justifications for unequal pay . Utilizing an integrative literature review methodology, the study analyzed 51 peer-reviewed articles to synthesize diverse theoretical and empirical perspectives on AI’s socio-technical impact . The findings identify four themes based on thematic analysis with its critical mechanisms through which bias is automated (historical mirroring, neutrality assumption, the codification of desired salary, the motherhood penalty) . By specifically examining the architec-tural features of dominant national recruitment platforms HeadHunter .kz and Enbek .kz , the research results suggest that as Kazakhstan rapidly adopts digital tools under national initiatives, it risks institutionalizing systemic inequality . The study implies an urgent need for local algorithmic audits and regulatory frameworks to ensure that the technological transition promotes social justice rather than automating the exist-ing wage gap .