  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.
