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Algorithmic Hiring and Organizational Justice: A Conceptual Framework for Balancing Recruitment Efficiency, Procedural Fairness, and Human Judgment
Citation. Bakers, A. (2025). Algorithmic hiring and organizational justice: A conceptual framework for balancing recruitment efficiency, procedural fairness, and human judgment. International Journal of Advanced Engineering Science and Information Technology, 8(6). https://doi.org/10.15662/ijaesit.2025.0806018
Summary. This conceptual article frames algorithmic hiring as a balance among recruitment efficiency, procedural fairness, and human judgment. It argues that sustainable use requires transparency, explainability, bias mitigation, meaningful human oversight, and responsible AI governance, especially where decisions affect employment opportunities. The framework positions human judgment as complementary to automation for contextual interpretation, candidate appeals, and consequential decisions.
Key claims
- Algorithmic hiring can standardize and accelerate screening, ranking, assessment, and interview-related selection activities, but its organizational value depends on how applicants experience the decision procedure.
- Procedural fairness in algorithmic hiring depends on procedures being understandable, consistent, contestable, and supported by transparency, explainability, and bias mitigation.
- Human judgment remains necessary as a complementary mechanism for contextual interpretation, appeals, and consequential hiring decisions rather than as a simple alternative to automation.
Evidence & limitations
- Conceptual literature synthesis spanning human resource management, organizational behavior, AI, information systems, business ethics, and organizational justice research through December 2024; it reports no new empirical study or effect estimates.
- The artifact is a Jina markdown capture containing the abstract and references; the source record therefore treats the article’s framework as conceptual rather than as direct evidence that the proposed safeguards improve hiring outcomes.