Zimeng Wang, J. C. Yang, Wenbin Shang, Jianbo Ding
Talent promotion decisions are critical for organizational success, yet traditional prediction models often suffer from opacity and inherited biases embedded in historical data. These black-box systems not only lack transparency but also perpetuate discriminatory patterns related to sensitive attributes such as gender and geographic location, undermining organizational fairness and employee trust. Moreover, existing evaluation metrics struggle to quantify the unstructured contributions of senior management, leading to poor generalization in complex personnel decisions. To address these challenges, we proposeFairPromote, a novel framework that integrates SHAP (SHapley Additive exPlanations) values with adversarial debiasing techniques within an ensemble learning architecture. Our approach simultaneously achieves high prediction accuracy while automatically identifying and mitigating the negative impact of sensitive attributes on promotion decisions. The framework employs a dual-objective optimization strategy: a primary predictor maximizes promotion prediction performance, while an adversarial discriminator minimizes the ability to infer sensitive attributes from learned representations. Through SHAP-based local explanation plots, FairPromote provides instance-level attribution analysis for each personnel decision, addressing the critical trust deficit in algorithmic decision-making systems. We evaluate our framework on the IBM HR Analytics dataset, demonstrating superior performance across multiple dimensions. Experimental results show that FairPromote achieves 91.3% prediction accuracy while reducing demographic parity difference by 68% and equalized odds disparity by 72% compared to baseline models. The SHAP-based interpretations reveal that job involvement, years at company, and performance ratings are the primary drivers of promotion decisions, while successfully neutralizing the influence of gender and marital status.