Artificial Intelligence in a degrowth context: A conviviality perspective on machine learning

Abstract

Degrowth has emerged as a strong voice against the green growth narrative. However, it has so far left largely unshaped its vision for technology, thereby overlooking a pivotal element of the green growth narrative. This article contributes to filling this gap by analyzing the appropriateness of a digital technology, Artificial Intelligence, to a degrowth context. It does so through the angle of conviviality, a concept introduced by Ivan Illich and frequently used by degrowth scholars, which states that convivial tools should foster autonomy, creativity, and relationships among humans and with nature. This paper specifically applies Vetter's Matrix of Convivial Technology to an application of machine learning with potential environmental benefits: predictive maintenance - a proactive maintenance technique based on real-time sensor monitoring. Three key limitations to its conviviality are identified: 1. the high complexity of machine learning, 2. its environmental impacts, and 3. the size of the infrastructure it relies on. These limitations prompt critical reflections on the appropriateness of machine learning (as a part of Artificial Intelligence) to degrowth but also act as inspirations for reshaping the technology towards more conviviality.