A Policy Profile for Croissant: Refusal as a Property of the Dataset
Researchers supply executable evaluation semantics for the Croissant ML dataset descriptor, enabling auditable refusal decisions with 11.7 microsecond added cost.
The paper defines an additive policy profile for Croissant, the JSON-LD machine-readable descriptor for ML datasets, letting datasets declare admitted operations and conditions over a closed set of five operators with a fully specified decision procedure. Evaluation on two corpora, including three descriptors gating a real nf-core pipeline, showed 552 complete decision records agreeing three ways with native descriptor and ODRL usageInfo terms. Added cost was 11.7 microseconds against a 119 microsecond decision, and the layer strips out leaving a valid Croissant document.
- Adds decision procedure to Croissant data-use conditions (DUO/ODRL)
- Closed set of five operators with full decision semantics
- Validated on three real nf-core pipeline descriptors
- 552 decision records agree across three evaluation methods
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Croissant is the de facto machine-readable descriptor for ML datasets: JSON-LD over schema.org. Since version 1.1 it also carries data use conditions, recommending DUO and ODRL for them. What no version specifies is how any of them is evaluated: no decision procedure, no bound on evaluation cost, no outcome for a condition an implementation cannot evaluate, no record of what was checked, and nothing on composition with caller-side authority. We supply that half. An additive profile lets a dataset declare the operations it admits and the conditions under which it admits them, over a closed set of five operators whose decision procedure is given in full, so a gate decides from the descriptor alone and records what it checked. Two corpora evaluate it and their evidence is kept apart. Three descriptors that gated a real nf-core pipeline give the deployment result: decisions from a profile document match the gate's native descriptor record for record, stripping the layer leaves a valid Croissant document, and the added cost is 11.7 $μ$s against a 119 $μ$s decision. A corpus generated from the profile's grammar gives the breadth, covering every operator, refusal class and conformance clause. Across its valid cases, 552 complete decision records agree three ways -- native descriptor, profile terms, and the same policy as ODRL in usageInfo. The carrier is therefore not the contribution; the evaluation semantics is. Finally, caller-bound and data-bound policies range over non-overlapping state spaces, so neither permit set contains the other.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.19640