Explainable AI-Driven Employee Performance Prediction

Abstract

In order to tackle both of the described issues, the following research introduces a unique multi-stage solution, which includes a synthetic data generator based on a Conditional Tabular Generative Adversarial Network, used to balance the data, and a Transformer-based network for predicting the desired outcome. A CTGAN will synthesize a sufficient amount of high-fidelity examples for a minor class to ensure proper learning process while a modified transformer will recognize relevant relations between various professional features through its multi-head attention mechanism. For “Human-in-the-Loop” decision making, a SHAP layer is included. The Explainable Artificial Intelligence (XAI) module breaks down the intricate output generated by the Transformer model into detailed contribution scores, providing not only local reasoning for each evaluation but also global understanding of organizational productivity drivers. It has been empirically established that the GAN-Transformer-SHAP approach outperforms all baseline ensembles (random forest, XGBoost) and Bayesian techniques [10, 14] in terms of F1-score and AUC-ROC. At the end of the day, this study offers a reliable, interpretable, and unbiased path to adopting advanced AI into talent management and performance appraisal processes.