Algorithmic Screening in Public-Benefit Administration

Abstract

Background: Algorithmic Screening in Public-Benefit Administration 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 algorithmic decision-making, focusing on consistency, institutional conditions, and outcomes in relation to public benefits.

Methods: The documentary sample comprised 279 statutes, regulations, policies, contracts, guidance documents, administrative records, or public disclosures. Two-stage coding evaluated clarity, transparency, procedural safeguards, oversight, and implementation requirements; disagreements were independently reconciled.

Results: Formal requirements relevant to algorithmic decision-making appeared in 84% of the materials, yet only 64% specified implementation or oversight in sufficient detail. Transparency and review arrangements were absent or incomplete in 21% of cases. More comprehensive instruments also showed clearer institutional responsibility and fewer implementation gaps.

Conclusion: The evidence on algorithmic decision-making shows that policy design and administrative execution cannot be separated. Accordingly, responsible AI adoption requires governance arrangements that can be implemented consistently across agencies, not merely high-level principles.

Study design: audit and case-study design.

Publication history: Originally published in print on 15 June 2022, Volume 2, Article 4. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.