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However, most of these studies have applied standard error analysis to ML models\u2014and in particular Deep Neural Network (DNN) models\u2014which 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\u2019s epistemic complexity and its interpretability, as introduced in the context of responsible AI. This clarifies in which sense\u2014and to what extent\u2014the 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 a plausible assessment of 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. However, Random Forest (RF) and Logistic Regression (LR) models are also briefly considered.<\/jats:p>","DOI":"10.1007\/s11023-024-09682-0","type":"journal-article","created":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T09:01:54Z","timestamp":1719306114000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Reliability and Interpretability in Science and Deep Learning"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3682-7187","authenticated-orcid":false,"given":"Luigi","family":"Scorzato","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,25]]},"reference":[{"key":"9682_CR1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","volume":"76","author":"M Abdar","year":"2021","unstructured":"Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. 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