Algorithmic Bias in HR Management: A Critical Inquiry into the Automation of Inequity

This paper provides a comprehensive critical analysis of algorithmic bias in human resource management, examining how artificial intelligence (AI) systems used in hiring, promotions, and performance management can perpetuate and amplify societal biases. Moving beyond conventional discussions of bias, this research introduces a series of novel theoretical frameworks to uncover the deeper, systemic mechanisms through which algorithmic HR systems produce inequitable outcomes. These include the Sociotechnical Bias Amplification Model (SBAM), which explains how temporal bias, intersectional bias, and feedback loops interact to compound discrimination; the Algorithmic Opacity Paradox, which argues that the perceived objectivity of AI obscures its discriminatory potential; and the Fairness Impossibility Theorem in the HR context, which demonstrates the mathematical and normative trade-offs inherent in any algorithmic fairness definition. The paper further develops a critical perspective by introducing concepts such as data colonialism, the performance management panopticon, and the algorithmic accountability gap. Through a synthesis of technical literature, organizational theory, and critical algorithm studies, this research argues that the challenge of algorithmic bias is not merely a technical problem of “debiasing” but a fundamental crisis of legitimacy for automated decision-making in the workplace. The analysis is supported by clearly labelled conceptual diagrams and selected empirical findings drawn from published research. It concludes with a set of actionable recommendations for HR leaders, policymakers, and researchers, advocating for a shift from a technical-fix mindset to a more holistic, human-centered approach to governance that prioritizes justice, accountability, and equity.