Artificial Intelligence Governance in Public Services to Accelerate Poverty Alleviation: Accountability Model and Oversight Mechanism for Indonesia

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.