PROBAITI Core Methodology Specification - Version 1.0
PROBAITI Core Methodology Specification — Version 1.0 defines the normative methodological baseline of PROBAITI, an evidence-first AI governance and assurance methodology for determining what an organisation can responsibly substantiate about a defined AI activity within a stated boundary. The specification establishes the canonical PROBAITI methodological kernel, including its controlled definitions, Core objects, relationship semantics, methodological rules, assessment lifecycle, conclusion constraints, reliance states, conformance gates and change-control requirements. PROBAITI is based on the principle that governance conclusions should be bounded, attributable, reconstructable and no stronger than the material evidence supporting them. The methodology connects applicable authority, verified facts, assessable claims, risks and failure conditions, controls, evidence, testing, findings, contradictions, bounded conclusions, accountable decisions and continuing revalidation within a defined Assurance Envelope. The Core Methodology incorporates the principal PROBAITI mechanisms: the Assurance Envelope, Demonstrability Chain, Evidence Ceiling Rule, Contradiction Rule, Demonstrability Debt, dependency-based change invalidation and targeted revalidation, dual traceability, and multi-axis non-compensation. This specification is the controlling normative methodological source within the PROBAITI publication ecosystem. Explanatory, operational, control-framework and implementation materials may elaborate or implement the methodology within their delegated boundaries but may not create, alter, weaken or override Core requirements. Version 1.0 was approved as the PROBAITI normative methodology baseline on 14 September 2026. Approval of this specification does not constitute legal advice, certification, accreditation, regulatory endorsement, a statutory conformity assessment or independent empirical validation. Applicable external law and authoritative legal sources remain controlling within their respective scope.
Authors
- Cem Yılmaz
Institutions
- Centro Universitário Curitiba (BR)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-16
- DOI
- https://doi.org/10.5281/zenodo.22793261
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00