Explainable AI-Driven Decision Support for Social Benefit Optimization: Improving Fairness, Reliability, and Managerial Oversight

It has become more necessary to have fair and transparent distribution of social benefits due to the increasing dependence of governments and organizations on data-driven decision systems. However, traditional AI platforms tend to be black-box, so interpretability is usually limited, and it allows biases to exist that compromise trust and undermine managerial control. To overcome these issues, the present paper introduces a proposal of an explainable artificial intelligence-based decision support system to improve fairness, reliability, and policy compliance in the workflow of social-benefit distribution. Its approach combines interpretable prediction modelling, equity-sensitive modifications, uncertainty estimation, and human-in-the-loop oversight and places it into one pipeline. Quantitative analysis of synthetic and real-world welfare data demonstrates that the proposed structure removes demographic bias by 22.7% and decision under perturbations by 18.4 and greater explanation fidelity by 31.2 than non-explainable bases do. The system further enhances the consistency of the allocation by 17.5% and reduces the risk of policy-violation by 14.9 %, and at the same time, it sustains the competitive predictive accuracy. As experimental findings indicate, there might be not only the higher quality of generated balance and credible recommendations of benefits but the enhanced managerial control due to the transparency of decision rationales and audit-traceable procedures. The results emphasize the usefulness of explainable and decision-aware AI systems in facilitating socially responsible and accountable decision making towards the administration of the public good.