What question did this study set out to answer?

The aim is to develop a governance framework for traceable AI-assisted decisions that ensures accountability.

April 10, 2026Open Access

Institutional AI Accountability Fabric (IAAF): System and Method for Authority-Linked Decision Provenance, Human Override Capture, and Replayable Audit Reconstruction

Key Points

The aim is to develop a governance framework for traceable AI-assisted decisions that ensures accountability.
Establishment of authority-linked decision-making processes
Integration of human override mechanisms
Implementation of replayable audit strategies
Application in high-consequence sectors like finance and healthcare
Provides a systematic approach to linking AI recommendations with human and policy contexts
Facilitates better oversight and transparency of AI-driven decisions
Enhances reliability of audits in regulated environments

Abstract

Institutional AI Accountability Fabric (IAAF) presents a governance framework for preserving and reconstructing AI-assisted decisions in institutional settings. The framework links machine-generated recommendations to policy context, delegated authority, supervisory intervention, and later audit or review. Designed for regulated and high-consequence environments, IAAF is relevant to financial services, healthcare, cybersecurity, infrastructure, and public administration. U.S. Patent Application No. 19/639,270 – Patent Pending DOI: 10.5281/zenodo.19446234

Institutional AI Accountability Fabric (IAAF): System and Method for Authority-Linked Decision Provenance, Human Override Capture, and Replayable Audit Reconstruction

Key Points

Abstract

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