论文标题
（非）科学和算法的中立：基本物理与社会之间的机器学习
(Non)-neutrality of science and algorithms: Machine Learning between fundamental physics and society
论文作者
论文摘要
机器学习（ML）算法在大数据和平台资本主义时代的影响尚未保留学术界的科学研究。在这项工作中，我们将分析ML在基本物理学中的使用及其与直接影响社会的其他情况的关系。我们将处理该问题的各个方面，从对出版物的书目分析到对文献的详细讨论，再到有关学术界内外的生产性和工作环境的概述。该分析将基于三个关键要素进行：科学的非中立性，被理解为其与历史和社会的内在关系；算法的非中等性，在取决于程序员选择的元素的意义上，无论技术进步是什么，这些算法都无法消除。范式转变的问题性质，支持数据驱动的科学（和社会）。从内部进行的科学思想普遍性的解构也成为了任何社会和政治讨论的必要第一步。这是ML案例研究中这项工作的主题。
The impact of Machine Learning (ML) algorithms in the age of big data and platform capitalism has not spared scientific research in academia. In this work, we will analyse the use of ML in fundamental physics and its relationship to other cases that directly affect society. We will deal with different aspects of the issue, from a bibliometric analysis of the publications, to a detailed discussion of the literature, to an overview on the productive and working context inside and outside academia. The analysis will be conducted on the basis of three key elements: the non-neutrality of science, understood as its intrinsic relationship with history and society; the non-neutrality of the algorithms, in the sense of the presence of elements that depend on the choices of the programmer, which cannot be eliminated whatever the technological progress is; the problematic nature of a paradigm shift in favour of a data-driven science (and society). The deconstruction of the presumed universality of scientific thought from the inside becomes in this perspective a necessary first step also for any social and political discussion. This is the subject of this work in the case study of ML.
