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This paper proposes an additive profile for Croissant to evaluate data use conditions, enabling automated compliance checking for ML datasets with a decision procedure that records what was checked.
This paper presents a formally grounded ODRL evaluator with transparent semantics, supporting all rule types, and compares its performance with existing evaluators, addressing interoperability issues in policy modelling for data access and AI governance.