Administrative Liability for Errors Produced by AI Decision-Support Systems

Abstract

Background: Administrative Liability for Errors Produced by AI Decision-Support Systems has become a practical governance concern because differences in institutional design and administrative capacity can shape how policy is applied in practice.

Objective: This study assessed the implementation of administrative liability, focusing on consistency, institutional conditions, and outcomes in relation to AI errors.

Methods: A cross-sectional survey of 317 relevant officials, professionals, service users, or regulated actors measured implementation quality, institutional capacity, procedural clarity, transparency, and perceived effectiveness. Descriptive statistics and multivariable regression were used to examine factors associated with stronger implementation.

Results: For administrative liability, 313 complete responses were available for analysis. Consistent implementation was reported by 60%, whereas 26% identified material procedural or capacity barriers. After adjustment for organization size and jurisdiction, clearer roles, stronger staff capability, and access to formal guidance remained positively associated with implementation quality.

Conclusion: The evidence on administrative liability shows that policy design and administrative execution cannot be separated. Accordingly, legal safeguards are most effective when they are translated into consistent operational practice and accessible review mechanisms.

Study design: case and policy analysis with practitioner survey.

Publication history: Originally published in print on 15 June 2026, Volume 6, Article 6. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.