Responsible AI & governance
omnibias is scientific-computing infrastructure, so "responsible AI" here means something concrete and technical: can you trust, reproduce, and bound the numbers it produces? This section is about scientific-model governance, not generic LLM content safety.
The five pillars
| Pillar | Page |
|---|---|
| Governance process for scientific models | Model governance |
| Where numbers come from, sealed | Provenance & certificates |
| Same inputs → same bits, anywhere | Reproducibility |
| What must pass before release | Evaluation gates |
| What we do and do not claim | Scope boundaries |
The core principle: honesty about exactness
Every result omnibias produces is labelled by how it is exact — closed-form, autodiff-exact, or numerically approximate. Conflating these is the central governance failure for scientific ML, and omnibias is designed to make it impossible to do so by accident. See Exactness & scope.
Who this section is for
- Researchers publishing results that depend on omnibias numbers.
- Reviewers / auditors who need to re-derive or bound a claim.
- Engineering leads putting a scientific model into a regulated or safety-relevant pipeline.