Auditability refers to the extent to which an AI system can be independently inspected and its decision-making processes, data flows, and performance metrics reviewed and verified. In the context of AI assurance, auditability is a foundational property that enables stakeholders — such as internal governance teams, external auditors, and regulators — to evaluate whether an AI system has been developed and deployed in accordance with defined standards, legal requirements, and ethical principles.

An auditable AI system is one that maintains detailed logs of its operations, retains documentation across the model development lifecycle, and enables reviewers to trace decisions back to underlying data and parameters. This includes version control for training datasets and models, configuration settings, user access records, and testing results. Auditability helps establish accountability and supports transparency — two key pillars of trustworthy AI.

Auditability is particularly critical in sectors where decisions made by AI have significant consequences — such as public safety, criminal justice, defence logistics, or critical infrastructure management. In these contexts, the inability to audit an AI system can hinder root cause analysis when failures occur, obstruct redress for affected individuals, and undermine institutional credibility.

The assurance process evaluates auditability by assessing whether sufficient traceability exists across the system’s lifecycle. This includes evaluating whether logs are accessible and interpretable, whether testing records are complete, and whether third parties can validate outcomes. It also involves checking whether the system supports “explainability on demand” — the ability to generate a coherent explanation when a decision is challenged.

Designing for auditability requires careful planning from the outset. Developers must anticipate the information that auditors will need and build in logging, documentation, and traceability features accordingly. Post-deployment, auditability must be maintained as models evolve or are retrained, ensuring that historic decisions can still be reviewed.

In regulated domains, lack of auditability may result in non-compliance with oversight requirements, particularly where auditing is mandated for certification, accreditation, or liability determination. International standards such as ISO/IEC 24029-1 and guidelines from NIST and the EU AI Act emphasise auditability as a core requirement for high-risk AI systems.

In sum, auditability is a critical enabler of trust and oversight. It empowers organisations to demonstrate due diligence, supports independent review, and ensures AI systems remain accountable and transparent throughout their operational life.