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

TL;DR

This paper critically examines how Machine Learning influences fundamental physics and society, highlighting issues of non-neutrality in science and algorithms, and questioning the paradigm shift towards data-driven science.

Contribution

It provides a comprehensive analysis of ML's role in science and society, emphasizing the non-neutrality of algorithms and the implications of a data-driven paradigm shift.

Findings

ML impacts fundamental physics and societal issues.

Algorithms contain non-neutral elements influenced by programmer choices.

The shift to data-driven science raises critical social and philosophical questions.

Abstract

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.