Right-to-Explanation Principles in Automated Public Administration

Alexander Bennett

Right-to-Explanation Principles in Automated Public Administration

Author(s)	Dr. Alexander Bennett
Country	United States
Abstract	Automated and artificial-intelligence-supported systems are increasingly used within public administration to assess eligibility for benefits, prioritize inspections, evaluate applications, detect suspected irregularities, support migration and border decisions, allocate public services, and structure other administrative processes. Although automation may improve consistency, processing capacity, and administrative efficiency, it can weaken procedural accountability when affected individuals cannot understand why a decision was reached or how algorithmic outputs influenced official reasoning. This article develops a rights-oriented framework for explanation in automated public administration. It distinguishes technical model explainability from legally meaningful explanation and argues that an adequate administrative explanation should identify the role of automation, the material information used, the principal factors affecting the individual outcome, the relationship between those factors and the final decision, significant limitations, and a practical route for correction, human review, or contestation. Contemporary European law provides particularly important developments. The General Data Protection Regulation requires meaningful information concerning the logic, significance, and envisaged consequences of qualifying automated decision-making, while the Court of Justice of the European Union strengthened the practical content of these rights in SCHUFA Holding and, particularly, Dun & Bradstreet Austria. The latter judgment confirmed in February 2025 that meaningful information must enable the individual to understand how personal data were actually used to obtain the particular automated result. The EU Artificial Intelligence Act separately contains an express Article 86 right to obtain clear and meaningful explanations concerning certain individual decisions based on outputs of Annex III high-risk AI systems. A synthetic analytical framework comprising 480 hypothetical administrative decisions compares four explanation conditions: no substantive explanation, generic notification of AI use, case-specific explanation of material reasons, and a comprehensive model combining individualized reasons with contestability safeguards. The composite procedural-accountability score increases from 34.9 to 51.4, 73.5, and 86.6 respectively. These values are illustrative rather than empirical. The study concludes that a meaningful right to explanation should operate as a gateway to effective administrative justice rather than as a narrow requirement to describe algorithmic technology. Explanation becomes legally significant when it enables an affected person to understand the decision, identify a potentially material error, and exercise available rights of review or challenge.
Keywords	right to explanation, automated public administration, administrative law, artificial intelligence, automated decision-making, algorithmic accountability, procedural fairness, GDPR, EU AI Act, contestability
Field	Engineering
Published In	Volume 2, Issue 6, November-December 2021
Published On	2021-11-22

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Right-to-Explanation Principles in Automated Public Administration

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