  Artificial Intelligence (AI) Auditability is a core requirement for achieving
responsible AI system design. However, it is not yet a prominent design feature
in current applications. Existing AI auditing tools typically lack integration
features and remain as isolated approaches. This results in manual,
high-effort, and mostly one-off AI audits, necessitating alternative methods.
Inspired by other domains such as finance, continuous AI auditing is a
promising direction to conduct regular assessments of AI systems. The issue
remains, however, since the methods for continuous AI auditing are not mature
yet at the moment. To address this gap, we propose the Auditability Method for
AI (AuditMAI), which is intended as a blueprint for an infrastructure towards
continuous AI auditing. For this purpose, we first clarified the definition of
AI auditability based on literature. Secondly, we derived requirements from two
industrial use cases for continuous AI auditing tool support. Finally, we
developed AuditMAI and discussed its elements as a blueprint for a continuous
AI auditability infrastructure.
