<![CDATA[The rapid digitalization of public-sector human resource management has introduced Algorithmic Management into Indonesian bureaucracy through the State Civil Apparatus Information System (SIASN) and the Computer Assisted Test (CAT) administered by the National Civil Service Agency (BKN), two systems that manage the career data of more than 4.3 million civil servants nationwide. This article analyzes that implementation as a case study, identifies the risks of algorithmic bias, and formulates recommendations for ethical artificial-intelligence governance in the public sector. The study employs a document-based case study combined with a critical literature review and comparative policy analysis, applying the Algorithmic Impact Assessment (AIA) framework across four dimensions (fairness, transparency, accountability, and effectiveness) together with a five-year longitudinal panel of eight BKN performance indicators (2020–2024). Methodologically, this design triangulates qualitative document analysis of regulatory and institutional texts with quantitative trend analysis of secondary panel data, allowing textual and measurable indicators of algorithmic implementation to be cross-validated[AM2.1].The findings show that SIASN and CAT have demonstrably reduced recruitment corruption and improved administrative efficiency; however, the AIA evaluation reveals significant deficits in fairness (geographic and cognitive bias) and accountability (the absence of a right to explanation), as well as a digital divide that structurally and consistently disadvantages civil servants in remote regions over the five-year period. The study concludes that algorithmic efficiency alone is an insufficient measure of reform success: Indonesia requires FATE-based AI ethics regulation, independent algorithmic auditing, a right to explanation for affected civil servants, and digital infrastructure and literacy equalization for remote regions.]]>
