ICLR 2026 Paper

AI governance for healthcare ML. Bias detection, fairness metrics, FDA-ready model cards, responsible AI.

Three-plane ML governance (Training → Approval → Serving) with Ed25519 cryptographic signatures, time-bounded approvals, and drift detection for NANDA-compatible agent registries

Risk ranking under operational constraints with explainability and governance support.

Reproducible verification ledger — AI safety audits, mathematical proof reproduction, and biomedical AI pipeline governance. Includes failures and honest abstentions.

Defensible risk evidence for deployed machine learning models

MLOps (Machine Learning Operations) is an engineering discipline that unifies machine learning development with operations to deploy, monitor, govern, and maintain machine learning models in production.