Algorithmic Accountability in Technology-Enabled Welfare Administration

Rachel Thompson

Algorithmic Accountability in Technology-Enabled Welfare Administration

Author(s)	Dr. Rachel Thompson
Country	United States
Abstract	Technology-enabled welfare administration increasingly relies on data integration, automated eligibility screening, risk classification, fraud detection, predictive analytics, and artificial intelligence-assisted decision support. These systems can improve administrative speed, consistency, targeting, and service coordination, but their use in decisions affecting income support, housing assistance, disability benefits, child welfare, unemployment protection, and other essential services creates a distinctive accountability problem. Administrative responsibility may become distributed across software developers, private vendors, public agencies, data infrastructures, frontline officers, and automated decision processes, making it difficult for affected individuals to determine why a decision occurred or who is responsible for correcting it. Contemporary public-sector governance therefore requires algorithmic accountability to extend beyond technical transparency toward institutional responsibility, procedural fairness, traceability, meaningful review, and accessible redress. OECD evidence indicates that artificial intelligence is already used in at least one governmental area in 35 of 36 surveyed OECD countries, while higher-stakes uses continue to demand stronger transparency, data quality, and assurance mechanisms. This paper develops an integrated accountability framework for technology-enabled welfare administration through a structured conceptual synthesis of public-administration scholarship, automated decision-making research, AI governance standards, and regulatory developments. The framework organizes accountability around seven interdependent dimensions: traceability, explainability, bias testing, meaningful human review, appeal and redress, data governance, and independent auditability. Rather than treating a human official's presence as sufficient protection, the study argues that accountability must operate throughout the administrative and technological lifecycle. An illustrative maturity analysis demonstrates how a welfare agency could evaluate these dimensions before and after implementing the proposed framework. The paper concludes that legitimate welfare automation depends not merely on whether an algorithm is accurate, but on whether public institutions can justify its use, monitor its consequences, explain consequential decisions, identify responsible actors, and provide effective mechanisms for correction.
Keywords	algorithmic accountability; welfare administration; automated decision-making; artificial intelligence; public administration; administrative justice; algorithmic transparency; social protection
Field	Engineering
Published In	Volume 2, Issue 4, July-August 2021
Published On	2021-07-19

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American Journal of Advanced Multidisciplinary Innovation and Research

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Algorithmic Accountability in Technology-Enabled Welfare Administration

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