  In recent years, the question of the reliability of Machine Learning (ML)
methods has acquired significant importance, and the analysis of the associated
uncertainties has motivated a growing amount of research. However, most of
these studies have applied standard error analysis to ML models, and in
particular Deep Neural Network (DNN) models, which represent a rather
significant departure from standard scientific modelling. It is therefore
necessary to integrate the standard error analysis with a deeper
epistemological analysis of the possible differences between DNN models and
standard scientific modelling and the possible implications of these
differences in the assessment of reliability. This article offers several
contributions. First, it emphasises the ubiquitous role of model assumptions
(both in ML and traditional Science) against the illusion of theory-free
science. Secondly, model assumptions are analysed from the point of view of
their (epistemic) complexity, which is shown to be language-independent. It is
argued that the high epistemic complexity of DNN models hinders the estimate of
their reliability and also their prospect of long-term progress. Some potential
ways forward are suggested. Thirdly, this article identifies the close relation
between a model's epistemic complexity and its interpretability, as introduced
in the context of responsible AI. This clarifies in which sense, and to what
extent, the lack of understanding of a model (black-box problem) impacts its
interpretability in a way that is independent of individual skills. It also
clarifies how interpretability is a precondition for assessing the reliability
of any model, which cannot be based on statistical analysis alone. This article
focuses on the comparison between traditional scientific models and DNN models.
But, Random Forest and Logistic Regression models are also briefly considered.
