In order to support responsible and executable decisions about people resources, this paper creates a unified math system that mixes many tasks’ predictions, fair results, and costs into one decision‐making process. Instead of treating task losses and business restrictions as separate additions, the suggested formulation incorporates (i) task‐level empirical dangers for churn prediction, performance evaluation, and promotion suggestions, (ii) team‐fairness departure fines (such as limited subgroup AUC/positive‐rate variances), and (iii) spending/hazard limits on intervention strategies inside a solitary primary‐dual optimization objective. This coupling gives Pareto‐efficient solutions that cannot be reached by just using a set weight sum of task losses since Lagrange multipliers adjustively change objectives to meet feasibility and fairness needs. With IBM HR Analytics Public HR Benchmark (1470 records) as a reproducible testbed, we show better predictive performance compared to classical baselines and give SHAP‐based explanations along with subgroup diagnostics. In the whole document, “big data” means the scalable data processing and feature engineering pipeline (multisource integration, standardized coding, and governed feature store), not just the number of samples in a benchmark dataset. The proposed framework provides a reusable applied mathematics template for HR decision‐making where accuracy, cost, risk, and fairness have to be optimized simultaneously.
Guan et al. (Thu,) studied this question.