# AuditMAI: Towards An Infrastructure for Continuous AI Auditing

arXiv: https://arxiv.org/abs/2406.14243 · HTML: https://mlsanity.com/paper/2406.14243/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2024.

## Built on by (most cited first, 2 of 2)

- [Provenance Tracking in Large-Scale Machine Learning Systems](https://mlsanity.com/paper/2507.01075/) (2025)
- [AUDITA: certified auditing and causal attribution of adverse outcomes in autonomous multi-agent systems](https://mlsanity.com/paper/2608.22160/) (2026)

## Abstract (arXiv)

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.