Applied Decision Analytics

Vol. 2 No. 1 (2026): Applied Decision Analytics
Articles

Governing Fair Algorithmic Hiring Under the EU AI Act: A Multi-Layer Decision Architecture for High-Risk Recruitment Systems

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  • Veranika Khlud
Veranika Khlud
RTU, BIA, ITDA

Published 2026-04-21

Keywords

  • Algorithmic hiring,
  • EU AI Act,
  • AI governance,
  • Organizational justice,
  • Algorithmic fairness,
  • Human oversight
    • ...More
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Copyright (c) 2026 Veranika Khlud (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Khlud, V. (2026). Governing Fair Algorithmic Hiring Under the EU AI Act: A Multi-Layer Decision Architecture for High-Risk Recruitment Systems. Applied Decision Analytics , 2(1), 271-282. https://doi.org/10.66972/ada21202624
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Scopus
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Europe PMC

Abstract

Artificial intelligence is increasingly used to support recruitment decisions, while the European Union’s (EU) Artificial Intelligence (AI) Act treats many employment-related AI systems as high-risk because they may significantly affect career opportunities, livelihoods, and fundamental rights. However, existing debates on fair algorithmic hiring often remain fragmented, treating fairness either as a technical bias-mitigation issue, a legal compliance issue, or a matter of applicant perception. This fragmentation leaves organizations without an integrated governance architecture that connects regulatory obligations, decision accountability, human oversight, data controls, and procedural justice. The purpose of this conceptual paper is to develop a multi-layer decision architecture for governing fair algorithmic hiring under the EU AI Act. The article applies a theory-building methodology based on conceptual synthesis, integrating the AI Act’s requirements for high-risk systems with organizational justice theory, algorithmic fairness research, and human resource management governance literature. The study develops a six-layer governance architecture consisting of strategic accountability, lifecycle risk management, data and model assurance, human oversight, candidate-facing transparency and redress, and continuous monitoring and organizational learning. In addition, the article formulates seven theoretical propositions explaining how governance mechanisms can translate compliance obligations into distributive, procedural, interpersonal, and informational justice in recruitment. The article concludes that fair algorithmic hiring cannot be achieved through isolated audits, bias testing, or disclosure statements alone. Instead, it requires an integrated decision architecture that allocates rights and responsibilities across the recruitment pipeline, preserves meaningful human judgment, enables candidate voice and appeal, and embeds legal and ethical safeguards into ongoing organizational routines. 

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