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.

翻译：人工智能（AI）可审计性是实现负责任AI系统设计的核心要求。然而，在现有应用中，它尚未成为显著的设计特征。当前的人工智能审计工具通常缺乏集成功能，仍以孤立的方式存在。这导致人工智能审计工作多为手动、高成本且通常为一次性操作，亟需替代性方法。受金融等其他领域启发，持续人工智能审计是对人工智能系统进行定期评估的一个有前景的方向。然而，问题依然存在，因为目前持续人工智能审计的方法尚未成熟。为弥补这一差距，我们提出了人工智能可审计性方法（AuditMAI），旨在作为构建持续人工智能审计基础设施的蓝图。为此，我们首先基于文献明确了人工智能可审计性的定义。其次，我们从两个工业用例中推导出对持续人工智能审计工具支持的需求。最后，我们开发了AuditMAI，并对其作为持续人工智能可审计性基础设施蓝图的构成要素进行了讨论。