Data-Protection Impact Assessments in Public-Sector AI Projects
Keywords:
data protection, AI, impact assessment, public sector, privacyAbstract
Background: Questions surrounding data protection are administrative as well as legal because institutional practice can influence AI, consistency, and accountability.
Objective: Our objective was to determine how institutional arrangements concerning data protection influenced implementation consistency and outcomes related to AI.
Methods: A structured review covered 531 statutes, regulations, policies, contracts, guidance documents, administrative records, or public disclosures. A predefined two-stage coding framework assessed legal clarity, transparency, procedural safeguards, oversight, and implementation requirements, with discrepancies resolved through independent review.
Results: Coverage of data protection was uneven: 52% of documents included formal requirements, compared with 32% that provided adequate implementation or oversight detail. Transparency or review mechanisms were absent or incomplete in 23%. Stronger instruments were distinguished by clearer institutional responsibility and fewer implementation gaps.
Conclusion: Implementation conditions appear decisive for the effectiveness of policy relating to data protection. Accordingly, AI governance in the public sector needs enforceable controls, capable staff, documented accountability, and meaningful human review.
Study design: document audit study.
Publication history: Originally published in print on 15 June 2023, Volume 3, Article 16. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.
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Copyright (c) 2023 Jayden Houghton, Paula Javiera Ahumada Franco

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