Artificial Intelligence Techniques for Convolutional Autoencoder with Dual-Key Transformer Network-Based Causality Analysis of Human Resource Practices on Firm Performance: Trends and Challenges

Abstract

The rapid evolution of artificial intelligence has significantly transformed organizational decision-making processes, particularly in human resource management and firm performance evaluation. This study explores advanced artificial intelligence techniques integrating convolutional autoencoders with dual-key transformer networks for causality analysis in human resource practices. The proposed framework leverages deep learning-based feature extraction and secure attention mechanisms to uncover complex causal relationships between HR practices and organizational performance outcomes. Convolutional autoencoders effectively capture latent representations from high-dimensional HR datasets, while dual-key transformer architectures ensure robust modeling of temporal dependencies and data privacy. This paper presents a comprehensive review of recent trends, methodologies, and applications in this domain, emphasizing the role of hybrid architectures in enhancing interpretability and predictive accuracy. Furthermore, the study identifies key challenges, including data heterogeneity, model explainability, computational complexity, and ethical concerns related to AI-driven decision systems. By synthesizing current research findings, the paper provides insights into emerging opportunities for integrating AI-driven causality analysis in HR analytics. The outcomes highlight the potential of advanced deep learning frameworks to support strategic HR decisions, optimize workforce productivity, and improve firm performance in dynamic business environments.