Designing reflective AI agents to continuously audit public policy algorithms for hidden biases in social welfare decision-making systems

Public policy algorithms increasingly shape access to social welfare benefits, healthcare subsidies, housing assistance, and poverty-alleviation programs. While these systems promise efficiency and evidence-based decision-making at scale, they also introduce new risks related to hidden biases, opaque rule structures, and unintended discrimination against vulnerable populations. As governments adopt machine-learning models to allocate resources, detect eligibility, and forecast risk, the challenge shifts from building accurate algorithms to ensuring that they remain fair, accountable, and aligned with fundamental public-interest principles. Traditional audit mechanisms periodic manual reviews, rule-checking, and post-hoc statistical fairness tests lack the agility and continuity needed to monitor modern adaptive systems. This paper proposes a framework for reflective AI agents capable of continuously auditing public policy algorithms for embedded biases, structural inequities, and harmful drift in high-stakes social welfare decision-making. These agents operate as autonomous overseers that interrogate model behaviors, evaluate disparities across demographic subgroups, and detect evolving patterns of exclusion as policy environments and population data shift over time. The approach integrates three core components: (1) a multi-layer bias-detection engine combining counterfactual simulations, causal diagnostics, and distribution-shift monitoring; (2) a reflective reasoning layer enabling agents to critique their own assumptions, retrace audit paths, and generate interpretable explanations; and (3) a policy-aware governance layer that aligns audit findings with statutory mandates, equity standards, and human-oversight requirements. By embedding reflective AI agents within the lifecycle of public policy algorithms, governments can move toward proactive, self-correcting governance infrastructures that strengthen transparency and safeguard citizens from algorithmic harm. The proposed framework illustrates a pathway toward more equitable, accountable, and resilient social welfare decision-making systems.