A Neuro-Symbolic Framework for Legal Accountability in Public-Sector AI

Automated decision-making systems are increasingly used by public agencies to govern access to welfare. Accountability in these settings is not achieved through access to model internals, but by the explanations provided to applicants i.e. documents that function as legal justifications and sites of contestation. Little work has been done to examine whether such explanations are legally valid. This paper examines explanation-level accountability in public-sector AI systems by designing and implementing a neuro-symbolic framework to assess automated welfare eligibility determinations. We deploy a Large Language Model (LLM) to encode statutory eligibility rules into a formal ontology and use satisfiability-based verification to assess whether benefits explanations comply with the governing law. Applying this approach to California's CalFresh program, we analyze explanations and observe if our system detects legal mismatches. Our findings show that formal verification reveals violations of statutory requirements even when explanations appear reasonable or complete to human readers. We argue that explanation-level verification offers a distinct and necessary complement to existing approaches of algorithmic auditing and accountability, shifting attention from model behavior to the legal integrity of justificatory artifacts. We call for a shift from interpretability to auditability of public algorithms, as government explanations must do more than reveal statistical associations; they should articulate the legal basis for a decision so that affected individuals, oversight bodies, and administrative reviewers can assess whether the justification satisfies statutory criteria and procedural norms.