LQS v3.1: A Procurement-Grade Quality Standard for AI Training Data with Cryptographically Verifiable Certificates

Procurement of training data for AI systems in regulated industries (financial services, healthcare, legal) currently lacks an independent quality measurement that satisfies model-risk audit requirements such as SR 11-7, EU AI Act Article 10, FDA 21 CFR 11.10(e), and HHS §1557. We introduce LQS v3.1, a 19-dimension quality standard for tabular, text, and image datasets that addresses three documented weaknesses of existing single-model quality scores: (1) reference-model bias via a 7-oracle consensus across 5 algorithm families with cross-validated agreement reporting (Cohen and Fleiss κ); (2) brittleness of metadata-derived task inference via a data-driven task detection layer with explicit ambiguity flagging; (3) over-confidence of point estimates via Wilson binomial intervals on rate-based dimensions, pooled-fold standard deviation on oracle-derived dimensions, and bootstrap-derived intervals on the composite. We add inductive split-conformal prediction (Vovk 2005, Romano 2019) producing 90% prediction intervals on downstream macro-F1 with provable coverage guarantees, and a graded benchmark-contamination dimension covering 40+ public evaluation suites (MMLU, HumanEval, GSM8K, SQuAD, etc.). Every score is bound to a canonical-JSON-serialized payload and signed with an Ed25519 keypair, producing a cryptographically verifiable certificate auditable offline against a published public key. We provide a reference implementation, a public verification API, and an SDK with no-auth verification helpers. The full LQS v3.1 specification is presented as a candidate reference methodology for ongoing standards work in IEEE P2841, NIST AI RMF, and ISO/IEC JTC 1 SC 42.