A Neuro-Symbolic Framework for Legal Accountability in Public-Sector AI — Sunny & Sivan-Sevilla (2026)
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…
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