Can AI Be Auditable?

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Authors: Himanshu Verma, Kirtan Padh, Eva Thelisson
Cited by: 2
References: 119

Abstract

Auditability is defined as the capacity of AI systems to be independently assessed for compliance with ethical, legal, and technical standards throughout their lifecycle. The chapter explores how auditability is being formalized through emerging regulatory frameworks, such as the EU AI Act, which mandate documentation, risk assessments, and governance structures. It analyzes the diverse challenges facing AI auditability, including technical opacity, inconsistent documentation practices, lack of standardized audit tools and metrics, and conflicting principles within existing responsible AI frameworks. The discussion highlights the need for clear guidelines, harmonized international regulations, and robust socio-technical methodologies to operationalize auditability at scale. The chapter concludes by emphasizing the importance of multi-stakeholder collaboration and auditor empowerment in building an effective AI audit ecosystem. It argues that auditability must be embedded in AI development practices and governance infrastructures to ensure that AI systems are not only functional but also ethically and legally aligned.

Fields of Study & Subjects

Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI)

Publication details

Journal / source
Cambridge University Press eBooks
Publisher
Cambridge University Press
Pages
130–162
Publication date
24 July 2026
Language
EN
Document type
Book chapter