🤖 AI Summary
Automated welfare eligibility systems frequently produce explanations misaligned with statutory requirements, undermining administrative legitimacy. This paper introduces an accountable neuro-symbolic framework for public-sector AI—first achieving semantic alignment and formal verifiability between algorithmic explanations and California’s CalFresh statutory law. Methodologically, the framework integrates structured legal ontology modeling, a rule extraction pipeline, formal representation in Prolog and Answer Set Programming (ASP), and solver-driven compliance reasoning. It precisely detects unlawful explanations, pinpoints violated statutory provisions, and enables explanation provenance tracking and contestation—thereby substantially enhancing decision transparency and statutory consistency. The core contribution is a legally traceable explanatory infrastructure that bridges AI interpretability with statutory compliance in public welfare administration.
📝 Abstract
Automated eligibility systems increasingly determine access to essential public benefits, but the explanations they generate often fail to reflect the legal rules that authorize those decisions. This thesis develops a legally grounded explainability framework that links system-generated decision justifications to the statutory constraints of CalFresh, California's Supplemental Nutrition Assistance Program. The framework combines a structured ontology of eligibility requirements derived from the state's Manual of Policies and Procedures (MPP), a rule extraction pipeline that expresses statutory logic in a verifiable formal representation, and a solver-based reasoning layer to evaluate whether the explanation aligns with governing law. Case evaluations demonstrate the framework's ability to detect legally inconsistent explanations, highlight violated eligibility rules, and support procedural accountability by making the basis of automated determinations traceable and contestable.