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The impact of Machine Learning (ML) algorithms in the age of big data andplatform capitalism has not spared scientific research in academia. In thiswork, we will analyse the use of ML in fundamental physics and its relationshipto other cases that directly affect society. We will deal with differentaspects of the issue, from a bibliometric analysis of the publications, to adetailed discussion of the literature, to an overview on the productive andworking context inside and outside academia. The analysis will be conducted onthe basis of three key elements: the non-neutrality of science, understood asits intrinsic relationship with history and society; the non-neutrality of thealgorithms, in the sense of the presence of elements that depend on the choicesof the programmer, which cannot be eliminated whatever the technologicalprogress is; the problematic nature of a paradigm shift in favour of adata-driven science (and society). The deconstruction of the presumeduniversality of scientific thought from the inside becomes in this perspectivea necessary first step also for any social and political discussion. This isthe subject of this work in the case study of ML.

(Non)-neutrality of science and algorithms: Machine Learning between fundamental physics and society