Operationalizing Accountable AI Through Traceable Governance Architecture for Institutional Decision Support

Abstract

Institutional artificial intelligence (AI) decision-support systems progressively evaluate cases, determine eligibility, and allocate resources; yet, predicted efficacy alone does not guarantee equity, contestability, or responsible utilization. Current research frequently considers fairness measures, explainability, human oversight, and organizational governance as rather distinct issues. This paper presents a traceable bias-auditing framework that amalgamates prediction, explanation, selective human review, and structured recording into a cohesive operational decision pathway. Through design science research, the artifact was exhibited in a controlled proof-of-concept utilizing 8000 synthetic institutional situations and historically biased data labels. The foundational classifier was a logistic regression model. Selective escalation is initiated by the proximity of boundaries, tension in explanation patterns, and the rules governing review priorities. Three situations were evaluated: baseline prediction, prediction with explanation alone, and comprehensive architecture with review and audit recording. Explanations enhanced reviewability but did not significantly alter fairness outcomes. The proposed architecture improved F1 from 0.781 to 0.795, reduced the demographic parity gap from 0.070 to 0.010, decreased the equal opportunity gap from 0.116 to 0.036, and improved audit completeness from 0.33 to 1.00, while escalating only 4.8% of cases for human review. The results indicate that explanations attain institutional significance solely when linked to procedural regulations and enduring records. The evaluation was simulation-based; thus, the results should be interpreted as proof-of-concept evidence rather than direct field validation.