Responsible Artificial Intelligence and Algorithmic Accountability in Public Services

Sarah M. Thompson

Responsible Artificial Intelligence and Algorithmic Accountability in Public Services

Author(s)	Dr. Sarah M. Thompson
Country	United States
Abstract	Artificial intelligence is increasingly used in public services to support eligibility determination, risk assessment, resource allocation, fraud detection, healthcare administration, education planning, taxation, policing, and citizen-service delivery. Although algorithmic systems may improve consistency and administrative efficiency, their use creates substantial concerns regarding discrimination, opacity, privacy, procedural fairness, and institutional responsibility. These concerns are particularly important in the public sector because algorithm-supported decisions can affect access to essential services, legal rights, public benefits, and individual opportunities. This study examines the relationship between responsible artificial intelligence and algorithmic accountability in public-service environments. It develops an integrated accountability framework based on legality, transparency, explainability, human oversight, contestability, auditability, data governance, and institutional responsibility. Because no verified public-sector dataset was supplied, the analytical component employs an explicitly simulated assessment of four governance models: no formal governance, basic compliance controls, standardized human review, and an integrated accountability framework. The simulated analysis evaluates transparency, fairness safeguards, human oversight, auditability, and citizen redress. The integrated framework achieves the highest illustrative accountability index of 91.4, compared with 56.8 for the absence of formal governance. These values are methodological illustrations rather than empirical findings. The study concludes that public-sector AI cannot be considered responsible merely because it is technically accurate. Legitimate implementation requires clear public authority, proportionate risk assessment, traceable decisions, meaningful human intervention, independent evaluation, and accessible mechanisms through which affected individuals can obtain explanations and challenge adverse outcomes.
Keywords	responsible artificial intelligence, algorithmic accountability, public services, digital governance, administrative fairness, human oversight, algorithmic transparency.
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2019
Published On	2019-02-26

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Responsible Artificial Intelligence and Algorithmic Accountability in Public Services

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