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
- Organizations
- nf-core
From the original article
1 min read at arxiv.orgCroissant is the de facto machine-readable descriptor for ML datasets: JSON-LD over schema. org. Since version 1.
By Alexander Chernov · arxiv.org
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