# Audit, Don't Explain -- Recommendations Based on a Socio-Technical Understanding of ML-Based Systems

arXiv: https://arxiv.org/abs/2107.09917 · HTML: https://mlsanity.com/paper/2107.09917/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil society](https://mlsanity.com/paper/2403.07904/) (2024)

## Abstract (arXiv)

In this position paper, I provide a socio-technical perspective on machine learning-based systems. I also explain why systematic audits may be preferable to explainable AI systems. I make concrete recommendations for how institutions governed by public law akin to the German T"UV and Stiftung Warentest can ensure that ML systems operate in the interest of the public.