Deep Learning-Enabled Human Resource Analytics in Predicting Employee Performance

This study delves into the revolutionary potential of Deep Learning-enabled Human Resource Analytics (HRA) with regard to forecasting worker productivity. Our research illustrates the efficacy of Deep Learning models in generating correct predictions by using a thorough approach including data collection, preprocessing, feature engineering, model selection, ethical concerns, and interpretability. According to Human Capital Theory, one of the most important results is that one's level of experience matters when making forecasts about one's performance. Responsible AI adoption in HR practices is grounded on issues of ethics, such as the prevention of prejudice and the protection of individuals' personal information. We highlight the need of real-time feedback loops, transfer learning, effective human-AI cooperation, and improved explainability of models in future approaches. Collectively, these methods pave the way for HR decisions that are better for the company as a whole and its employees in terms of both ethics and flexibility.