The paper defines algorithmic contestability as identifying evidence to overturn potentially incorrect decisions and identifies three types of such evidence that make decisions normatively indefensible under the decision maker's standards.
arXiv preprint arXiv:2504.10708 , year=
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A framework combining legal ontology, rule extraction, and solver reasoning verifies whether AI explanations for CalFresh eligibility align with statutory constraints.
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Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
The paper defines algorithmic contestability as identifying evidence to overturn potentially incorrect decisions and identifies three types of such evidence that make decisions normatively indefensible under the decision maker's standards.
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A Neuro-Symbolic Framework for Accountability in Public-Sector AI
A framework combining legal ontology, rule extraction, and solver reasoning verifies whether AI explanations for CalFresh eligibility align with statutory constraints.