A Transparent XAI-based Human-in-the- Loop Architecture for Responsible AI Systems
This has cast extreme doubt in relation to transparency, accountability, and human trust in machine learning applications despite the growing dependency on automated decision-making systems. Numerous high-performance models are black boxes, limiting human knowledge and supervision in high-stakes areas. This study comes up with a human-centered machine learning infrastructure that will underpin transparent automated decision-making by instantiating explainability infrastructure and human-in-the-loop architectures. The paper compares four machine learning models, namely, Logistic Regression, Decision Tree, Random Forest, and XGBoost with SHAP explanations, on a structured dataset (10,000 instances). As the experimental results indicate, XGBoost with SHAP model has best predictive performances with an accuracy of 89.2, an F1-score of 0.88 and an AUC of 0.92, with excellent explanation clarity. Random Forest had an accuracy of 86.9% and Decision Tree and Logistic Regression had accuracy of 82.4 and 79.6 respectively, but it had better interpretability. Human-in-the-loop assessment also revealed higher rates of 91.6 and lower rates of 8.4 in decision acceptance of explainable models and override, respectively. The proposed architecture has a better balance between the performance and transparency as compared to the existing related work. The results validate the claim that explainability and human supervision increase the levels of trust, usability, and ethical adherence in the automated decision-making systems