Procedural Fairness in Algorithmically Mediated Workplace Decisions

Daniel Thompson

Procedural Fairness in Algorithmically Mediated Workplace Decisions

Author(s)	Dr. Daniel Thompson
Country	United States
Abstract	Algorithmically mediated workplace decision-making has expanded from recruitment screening into performance evaluation, task allocation, scheduling, promotion, disciplinary assessment, and employment termination. Although such systems can improve consistency, processing capacity, and managerial access to information, their legitimacy cannot be evaluated solely through predictive accuracy or statistical parity. Employment decisions are relational and consequential processes through which individuals obtain opportunities, income, professional status, and continued access to work. Procedural fairness therefore requires attention to how information is collected, how criteria are selected, whether affected workers can understand the basis of a decision, whether relevant contextual information can be introduced, whether meaningful human review exists, and whether adverse outcomes can be challenged. Contemporary regulation increasingly reflects these concerns. New York City's Automated Employment Decision Tools framework requires specified automated employment tools used for hiring or promotion to undergo a bias audit, publication of audit information, and advance notice requirements, while the European Union's AI Act treats significant employment-related AI applications within its risk-based regulatory architecture and prohibits workplace emotion-recognition systems except for narrowly specified circumstances. This study develops a procedural-fairness governance framework for algorithmically mediated workplace decisions through conceptual analysis, regulatory comparison, and a transparent simulation-based risk assessment. Six employment contexts are examined: recruitment screening, promotion, performance evaluation, scheduling and task allocation, disciplinary action, and termination. The proposed framework evaluates consistency, evidentiary accuracy, bias suppression, worker voice, explainability, correctability, and meaningful human accountability. The simulated analysis indicates that algorithmically mediated termination and disciplinary decisions generate the highest procedural-fairness risks because errors at these stages have immediate and substantial consequences and because retrospective explanation cannot substitute for genuine opportunities to contest evidence before an adverse decision becomes final. The study concludes that procedurally fair workplace AI requires more than a nominal human-in-the-loop mechanism. Organizations should establish advance notice, job-relevant criteria, auditable data provenance, understandable explanations, employee participation, independent review, accessible appeal procedures, and documented human authority capable of changing algorithmically recommended outcomes. The resulting framework offers a practical foundation for organizations seeking to integrate algorithmic management with organizational justice, responsible AI governance, and worker-centered accountability.
Keywords	procedural fairness, algorithmic management, workplace artificial intelligence, automated employment decisions, organizational justice, algorithmic accountability, employee voice, explainable AI, human oversight, employment governance
Field	Engineering
Published In	Volume 2, Issue 6, November-December 2021
Published On	2021-12-30

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American Journal of Advanced Multidisciplinary Innovation and Research

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Procedural Fairness in Algorithmically Mediated Workplace Decisions

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