Unveiling Equity: Exploring Feature Dependency using Complex-Valued Neural Networks and Attention Mechanism for Fair Data Analysis

With the increasing use of big data, cloud computing, and machine learning in high-stake domains such as justice systems, financial institutions, and healthcare, concerns about fairness have become more prominent. This paper presents a novel approach to foster fair decision-making by tackling social bias and enhancing transparency in machine learning models. The proposed framework leverages quantum-inspired complex-valued neural networks and attention-based networks, offering improved transparency in modeling the decision process for interpreting feature importance and dependency. Furthermore, our approach tackles the challenges posed by imbalanced data through the incorporation of focal loss and oversampling techniques, resulting in reduced prediction errors. Through extensive experiments conducted on real-life datasets encompassing criminal charge prediction, financial fraud detection, and credit card default payment prediction, our approach consistently demonstrates reliable prediction precision and recall. Notably, our analysis of feature significance highlights the statistical importance of task-related features such as historical records of bank transactions or criminal charge history, while socially biased identifiers like race, gender, and age exhibit minimal significance. By excluding these biased features, our approach enhances fairness without compromising prediction accuracy, thereby contributing to the advancement of fair decision-making in big data and cloud computing across various high-stake domains.