Algorithmic Management in AI-Driven Recruitment: The AI Recruitment Governance Framework (ARGF) for Responsible AI Governance

Prestini, Dawid Krystian (2026) Algorithmic Management in AI-Driven Recruitment: The AI Recruitment Governance Framework (ARGF) for Responsible AI Governance. Science Discovery Artificial Intelligence, 1 (2). ISSN 2997-3216

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Abstract

The rapid integration of artificial intelligence (AI) into organizational recruitment processes is transforming how organizations identify, evaluate, and select job candidates. AI-driven recruitment systems enable firms to process large volumes of applicant data and increase the efficiency of hiring processes. However, the growing reliance on algorithmic decision systems also introduces significant governance challenges related to transparency, accountability, and candidate trust. This study examines AI-driven recruitment systems through the lens of algorithmic management and organizational governance. While existing research has primarily focused on technical performance and bias mitigation in automated hiring systems, relatively limited attention has been devoted to the governance structures required to manage algorithmic decision-making within organizational recruitment processes. Addressing this gap, the paper develops the AI Recruitment Governance Framework (ARGF), a conceptual model that conceptualizes AI-driven recruitment as a form of algorithmic management and proposes a responsible AI governance architecture based on three core dimensions: transparency, accountability, and human oversight. The framework provides a theoretical foundation for future empirical research. The framework highlights governance mechanisms that enable organizations to maintain managerial responsibility and ethical oversight while leveraging the efficiency gains offered by AI technologies. This study contributes to the literature by conceptualizing AI-driven recruitment as a form of algorithmic management and proposing a governance framework for responsible AI deployment in hiring processes. The study contributes to the emerging literature on responsible AI in human resource management by integrating insights from algorithmic management theory, HR governance research, and AI ethics scholarship. The findings suggest that organizations should adopt hybrid recruitment models in which algorithmic screening is complemented by structured human oversight and clear governance mechanisms. Such approaches can enable organizations to benefit from AI-enabled recruitment while preserving fairness, transparency, and legitimacy in hiring decisions.

Item Type:	Article
Keywords:	Artificial Intelligence, Algorithmic Management, AI Recruitment Governance Framework (ARGF), Responsible AI Governance, Human Resource Management, Algorithmic Decision-Making
Divisions:	Applied Science, Computing and Engineering
Depositing User:	Hayley Dennis
Date Deposited:	11 May 2026 12:53
Last Modified:	11 May 2026 12:53
URI:	https://wrexham.repository.guildhe.ac.uk/id/eprint/18444

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