Ensuring Algorithmic Equity via Distributional Alignment Techniques for Reducing Disparate Impact in Automated Hiring Systems - Victor Donovan; Samuel Carmichael (Computational Intelligence Systems, 2026)
The rapid integration of automated hiring systems into the global labor market has promised unparalleled efficiency in talent acquisition, yet it has simultaneously introduced profound challenges regarding algorithmic bias and systemic inequity. This research explores the technical and socio-technical dimensions of ensuring algorithmic equity through the application of distributional alignment techniques. By focusing on the mitigation of disparate impact, the study investigates how alignment strategies can recalibrate the statistical distribution of outcomes across protected demographic groups without compromising the predictive utility of recruitment models. We analyze the architectural constraints of large-scale automated screening tools and the governance frameworks necessary to maintain robustness during deployment. The discussion emphasizes the structural trade-offs between mathematical fairness and organizational performance, arguing that equity must be treated as a core architectural requirement rather than a post-processing adjustment. Through a detailed examination of infrastructure requirements and policy implications, this paper provides a comprehensive roadmap for developing sustainable, equitable, and transparent hiring infrastructures. The findings suggest that while distributional alignment offers a powerful mechanism for reducing historical bias, its success remains inextricably linked to the broader socio-technical context and the rigor of institutional oversight.