From Explainable AI to Auditable AI: Developing an Integrated Artificial Intelligence Auditability Framework (AIAF) for Assuring Artificial Intelligence Systems

Abstract

Artificial Intelligence (AI) is transforming organisational decision-making across both the public and private sectors. Although significant progress has been made in AI governance through Responsible AI, Trustworthy AI, Explainable Artificial Intelligence (XAI), AI assurance, and AI auditing, comparatively little attention has been devoted to the organisational capability required to independently verify that AI systems operate in accordance with governance, regulatory, and assurance requirements throughout their lifecycle. This conceptual paper introduces AI Auditability as a distinct governance construct and proposes the Artificial Intelligence Auditability Framework (AIAF), an integrated governance framework designed to support continuous, evidence-based independent assurance of AI systems. Drawing upon governance theory, assurance theory, internal auditing, and socio-technical systems theory, the paper argues that the next evolution of AI governance is not simply more transparent or more responsible AI, but AI systems that are inherently auditable. The proposed framework integrates eight governance dimensions: Governance and Accountability Transparency and Explainability Traceability and Provenance Data Integrity Security and Privacy Risk and Compliance Continuous Monitoring Independent Assurance The paper further introduces several original theoretical contributions, including: AI Auditability as a distinct governance construct The Artificial Intelligence Auditability Framework (AIAF) The Principle of AI Auditability Evidence by Design Secure Auditability A conceptual distinction between Explainable AI and AI Auditability The Artificial Intelligence Auditability Assessment Matrix (AIAM) Collectively, these contributions establish a coherent theory of AI Auditability that extends existing AI governance literature beyond transparency, ethics, and risk management towards continuous, evidence-based independent assurance. The framework provides practical guidance for internal auditors, external auditors, AI developers, Boards of Directors, executive management, regulators, certification bodies, and public-sector organisations seeking to strengthen AI governance, regulatory compliance, organisational accountability, and stakeholder trust. This work is intended to serve as a foundation for future empirical validation, AI governance maturity assessment, AI assurance methodologies, and the development of international standards for AI Auditability.