<![CDATA[Background: Indonesian public administration is accelerating the use of artificial intelligence (AI) to improve social protection targeting. The legal and digital governance foundations are in place through SPBE and Satu Data regulations, complemented by the Data Protection Law, Public Information Law, and the PSTE regulation. Recent policy integrates Regsosek, DTKS and P3KE into a single national data basis (DTSEN) for beneficiary determination. Objective: This paper proposes an operational model of algorithmic accountability and multi?layer oversight for AI used in public services to accelerate poverty alleviation while protecting fundamental rights. Methods: A normative?doctrinal approach augmented by a targeted socio?legal case mapping is used, combining legal gap analysis and design?science techniques. The model is benchmarked against global practices (EU AI Act and FRIA, Canada’s AIA/DADM, the Netherlands’ Algorithm Register, the UK ATRS, and NIST AI RMF). Results: We outline a three?stage governance architecture—ex ante (mandatory AIA+FRIA for high?risk systems; legality, data quality and bias testing), in?process (human?in?the?loop at decision thresholds; logging/versioning; explainability), and ex post (reason?giving and appeal; algorithm register; periodic audits)—tightly linked to SPBE/Satu Data controls and the DTSEN pipeline. During the transition to a full PDP authority, external oversight is bridged by the Ombudsman and the Information Commission. Conclusion: The proposed model operationalizes administrative due process for AI?assisted decisions, strengthens transparency and accountability, and is expected to reduce inclusion/exclusion errors and improve exit and persistence rates above the poverty line. A 12?month implementation roadmap and measurable indicators are provided.]]>
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