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Armstrong’s necessitarian view of legal guidelines of nature – a non-Humean conception – is invulnerable to those explanatory circularity problems. At the end, I will analyse a semantic round condition for unsuccessful explanations, recently proposed by Shumener , concerning this discussion. In the literature, the terminology “explanatory circularity drawback” has been used to designate two barely different circularities. A first circularity is a full explanatory circularity, hereafter the problem of circularity C. Synthetically, a regulation of nature is inferred from an observed phenomenon and, thereafter, it’s used to clarify that very same noticed phenomena. Thus, an observed phenomenon explains itself. The different circularity is a problem of self-rationalization, hereafter the problem of circularity SE. The drawback of circularity SE is a sub-problem of the problem of circularity C. A regulation of nature explains an observed phenomenon, however the legislation consists of that very same phenomenon in its content material. In the case of ML procedures, we show that the properties of compositionality and elementarity correspond to properties of the segmentation of the information house by the execution of the algorithm. Compositionality corresponds to the existence of nicely-outlined segmentations, and elementarity corresponds to the definition of those segmentations by explicit, easy variables. But ML algorithms can loose both of these properties. Such is the case of opaque ML, as illustrated by deep studying neural networks, the place both properties are actually lost.This entails an enhanced dependance of a given determination to the

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