Risk-Based AI Assurance Framework

The aim of this research is to present a risk-based AI assurance framework that producesquantifiable metrics for auditors and stakeholders to make deployment decisions withevidence-driven assurance of traceability, explainability, accountability, and reproducibility. Our proposed framework incorporates risk severity core with additional modifiers toaccommodate the context, governance obligations, technical and environmental exposure,and residual risk relevant to the AI model. This multi-tiered technique enables stakeholdersand governance teams to operationalize the safe deployment assurance. The final AssuranceAdequacy Score (AAS) comprises a Governance Readiness Score (GRS) along withtwo additional indices to quantify the traceability and explainability of the AI model. TheTraceability Adequacy Index (TAI) is calculated by evaluating the attributes such as thedataset and model versioning, pipeline logging, model audit completeness, and reproducibility. And an Explainability Adequacy Index (EAI) is calculated by evaluating theattributes such as the fidelity for local and global explanations, stability, faithfulness of theexplanation provided, robustness, coverage, and human comprehension. This architectureenables integration of risk assessment and enables continued AI assurance by deployinga bottleneck principle where the readiness of the AI model is confined by the weaker ofthe indices. Finally, a tiered gate mechanism is applied on the Assurance Adequacy Scoreto enforce minimum assurance floors for high-risk AI systems. The evaluation conductedon multi-domain AI models demonstrates the Risk-Based AI Assurance Framework’s(RBAAF) ability to yield stable and consistent readiness decisions with sensitivity analysisand re-scoring. The use cases demonstrate that even comparable risk levels can leadto significantly different deployment outcomes depending on assurance maturity, anddesign-specific improvements in traceable or explainable domains have the ability to shiftgate outcomes. Combining governance regulations with a standardized and quantifiabletraceability and explainability score enables the stakeholders to evaluate the AI system foran accountable and regulation-compliant deployment.