{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T19:31:35Z","timestamp":1784662295342,"version":"3.55.0"},"reference-count":55,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The aim of this research is to present a risk-based AI assurance framework that produces quantifiable metrics for auditors and stakeholders to make deployment decisions with evidence-driven assurance of traceability, explainability, accountability, and reproducibility. Our proposed framework incorporates risk severity core with additional modifiers to accommodate the context, governance obligations, technical and environmental exposure, and residual risk relevant to the AI model. This multi-tiered technique enables stakeholders and governance teams to operationalize the safe deployment assurance. The final Assurance Adequacy Score (AAS) comprises a Governance Readiness Score (GRS) along with two additional indices to quantify the traceability and explainability of the AI model. The Traceability Adequacy Index (TAI) is calculated by evaluating the attributes such as the dataset and model versioning, pipeline logging, model audit completeness, and reproducibility. And an Explainability Adequacy Index (EAI) is calculated by evaluating the attributes such as the fidelity for local and global explanations, stability, faithfulness of the explanation provided, robustness, coverage, and human comprehension. This architecture enables integration of risk assessment and enables continued AI assurance by deploying a bottleneck principle where the readiness of the AI model is confined by the weaker of the indices. Finally, a tiered gate mechanism is applied on the Assurance Adequacy Score to enforce minimum assurance floors for high-risk AI systems. The evaluation conducted on multi-domain AI models demonstrates the Risk-Based AI Assurance Framework\u2019s (RBAAF) ability to yield stable and consistent readiness decisions with sensitivity analysis and re-scoring. The use cases demonstrate that even comparable risk levels can lead to significantly different deployment outcomes depending on assurance maturity, and design-specific improvements in traceable or explainable domains have the ability to shift gate outcomes. Combining governance regulations with a standardized and quantifiable traceability and explainability score enables the stakeholders to evaluate the AI system for an accountable and regulation-compliant deployment.<\/jats:p>","DOI":"10.3390\/info17030263","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:29:53Z","timestamp":1772724593000},"page":"263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Risk-Based AI Assurance Framework"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4127-1260","authenticated-orcid":false,"given":"Aoun E.","family":"Muhammad","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8610-661X","authenticated-orcid":false,"given":"Kin-Choong","family":"Yow","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"ref_1","unstructured":"(2026, February 17). 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