A Degrowth Perspective on Artificial Intelligence - Analysing the Appropriateness of Machine Learning to a Degrowth Context

Abstract

Green growth has emerged as a dominant narrative within environmental policy, holding out the prospect that technological progress will enable an absolute decoupling between economic growth and natural resources usage. To achieve such decoupling, digital technologies are considered particularly promising. The degrowth movement criticizes the green growth narrative and argues based on an increasing amount of evidence that we need an urgent break out of the growth paradigm. However, the movement’s vision on technology remains undefined, meanwhile influential actors such as industrialised governments dedicate great efforts to push their own imaginary of technological developments forward. This thesis therefore aims at contributing to the degrowth discourse on technology by outlining a degrowth perspective on Artificial Intelligence (AI), specifically machine learning (ML), a digital technology at the center of promises of environmental benefits, strong political support and extremely high investments and research interests. The thesis focuses on the question: could AI be appropriate to a degrowth context? If so, how? The first step of this thesis (the growth perspective) shows that (1) AI has the capacity to accelerate economic growth and that (2) governments and large consultancy firms show high interests in AI actually driving growth. This thesis therefore argues that a degrowth perspective on AI should oppose this technology as long as it acts as a growth-accelerator, while recognizing that most probably only a change in economic paradigm could divert AI from its growth-acceleration effects. In the second step (the conviviality perspective), Vetter’s Matrix of Convivial Technologies is applied to machine learning, which leads to the identification of three aspects of ML which strongly limit its conviviality: (1) its high complexity, (2) its environmental impacts and (3) the size of the infrastructure it needs. This thesis argues that if despite the above limitations to conviviality, machine learning were to still be considered by the degrowth movement, then it should at least satisfy the following two conditions to be considered appropriate: (1) it should have no global destructive consequences, and (2) it should be carefully assessed in its local context of application by the affected people, while striving for conviviality.