  Machine learning software, deep neural networks (DNN) software in particular,
discerns valuable information from a large dataset, a set of data. Outcomes of
such DNN programs are dependent on the quality of both learning programs and
datasets. Unfortunately, the quality of datasets is difficult to be defined,
because they are just samples. The quality assurance of DNN software is
difficult, because resultant trained machine learning models are unknown prior
to its development, and the validation is conducted indirectly in terms of
prediction performance. This paper introduces a hypothesis that faults in the
learning programs manifest themselves as distortions in trained machine
learning models. Relative distortion degrees measured with appropriate observer
functions may indicate that there are some hidden faults. The proposal is
demonstrated with example cases of the MNIST dataset.
