Dynamic Fairness in Workforce Allocation

Author(s)	Syed Arham Akheel
Country	United States
Abstract	This paper proposes a framework for fairness-aware workforce allocation by integrating dynamic feature weighting with hybrid AI models. The approach combines lexical and semantic retrieval techniques, Large Language Model (LLM)driven fairness labeling, and reinforcement learning for adaptive feature prioritization. By addressing scalability, bias amplification, and interpretability gaps in existing systems, this work bridges the divide between algorithmic precision and ethical accountability in HR decision-making. Evaluations on both synthetic benchmarks and real-world HR datasets demonstrate superior fairness-performance trade-offs compared to state-of-the-art baselines, achieving up to 91% bias reduction while maintaining 89% recommendation accuracy. Key contributions include a context-aware fairness metric, an LLM-guided reranking layer, and a stakeholder-in-the-loop weight adjustment mechanism.
Keywords	Fairness-aware AI, Workforce Allocation, Hybrid Retrieval, Dynamic Feature Weighting, LLM Reasoning, Reinforcement Learning
Field	Engineering
Published In	Volume 6, Issue 2, February 2025
Published On	2025-02-28
Cite This	Dynamic Fairness in Workforce Allocation - Syed Arham Akheel - IJLRP Volume 6, Issue 2, February 2025. DOI 10.5281/zenodo.15034532
DOI	https://doi.org/10.5281/zenodo.15034532
Short DOI	https://doi.org/g88j5p

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