A Framework for Fairness-Aware In System Base Data Configuration using Multi-Stream, Multi-Model Dueling Deep Q-Networks-Like Architecture

Q-Learning-like fairness-aware deep reinforcement learning framework based on a modified Dueling Deep Q-Learning-Like architecture. The proposed system introduces a complex approach to addressing fairness in decision-making processes while maintaining high performance in system base data configuration assessment tasks in different categories. The architecture implements a multi-model feature approach for fairness optimization, incorporating numerous data processing pipelines that handle multiple concurrent data streams. It includes a fairness-aware deep Q-learning-like architecture with a multi-state model, an integrated multi-stream processing system, and a weight-based reward mechanism balancing prediction and accuracy with fairness metrics. Experimental results have shown the effectiveness of our approach in maintaining fairness across different featured groups while achieving high performance in system base data configuration assessment tasks. Unlike traditional system base data configuration assessment methods that rely on subjective self-reporting, which are vulnerable to cultural biases, literacy barriers, and limited effectiveness in non-verbal patients (e.g., infants, critically ill, or cognitively impaired individuals)—our automated approach provides objective, continuous monitoring with consistent interpretation across diverse patient populations. This addresses critical clinical challenges, including disparities in system base data configuration management across demographic groups, clinician bias in system base data configuration assessment, and communication barriers in vulnerable populations. Furthermore, our fairness-aware framework specifically mitigates algorithmic biases that might otherwise perpetuate existing inequities in system base data configuration management.