# Ensuring Dataset Quality for Machine Learning Certification

arXiv: https://arxiv.org/abs/2011.01799 · HTML: https://mlsanity.com/paper/2011.01799/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification](https://mlsanity.com/paper/2307.02192/) (2023)
- [Certification of embedded systems based on Machine Learning: A survey](https://mlsanity.com/paper/2106.07221/) (2021)
- [Dataset Definition Standard (DDS)](https://mlsanity.com/paper/2101.03020/) (2021)
- [The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis](https://mlsanity.com/paper/2408.02379/) (2024)
- [From Reflection to Repair: A Scoping Review of Dataset Documentation Tools](https://mlsanity.com/paper/2602.15968/) (2026)
- [Standardness Clouds Meaning: A Position Regarding the Informed Usage of Standard Datasets](https://mlsanity.com/paper/2406.13552/) (2024)
- [LabelBuddy: An Open Source Music and Audio Language Annotation Tagging Tool Using AI Assistance](https://mlsanity.com/paper/2603.04293/) (2026)

## Abstract (arXiv)

In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the specificities of the ML context are neither properly captured nor taken into ac-count. As a first answer to this concerning situation, we propose a dataset specification and verification process, and apply it on a signal recognition system from the railway domain. In addi-tion, we also give a list of recommendations for the collection and management of datasets. This work is one step towards the dataset engineering process that will be required for ML to be used on safety critical systems.