# Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detection

arXiv: https://arxiv.org/abs/2406.15268 · HTML: https://mlsanity.com/paper/2406.15268/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 95 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 28 of 28)

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- [IEEE 7010: A New Standard for Assessing the Well-being Implications of Artificial Intelligence](https://mlsanity.com/paper/2005.06620/) (2020)
- [Combinatorial Testing for Deep Learning Systems](https://mlsanity.com/paper/1806.07723/) (2018)
- [Understanding Bias in Machine Learning](https://mlsanity.com/paper/1909.01866/) (2019)
- [Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction](https://mlsanity.com/paper/1905.11474/) (2019)
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## Abstract (arXiv)

Countless domains rely on Machine Learning (ML) models, including safety-critical domains, such as autonomous driving, which this paper focuses on. While the black box nature of ML is simply a nuisance in some domains, in safety-critical domains, this makes ML models difficult to trust. To fully utilize ML models in safety-critical domains, it would be beneficial to have a method to improve trust in model robustness and accuracy without human experts checking each decision. This research proposes a method to increase trust in ML models used in safety-critical domains by ensuring the robustness and completeness of the model's training dataset. Because ML models embody what they are trained with, ensuring the completeness of training datasets can help to increase the trust in the training of ML models. To this end, this paper proposes the use of a domain ontology and an image quality characteristic ontology to validate the domain completeness and image quality robustness of a training dataset. This research also presents an experiment as a proof of concept for this method, where ontologies are built for the emergency road vehicle domain.