I am delighted to be part of a broad reflection, initiated and promoted by Edemilson Paraná, that critically interrogates the convergence of environmental sustainability and digitalization, in a special issue just published in the journal Globalizations. Governments and multi-lateral institutions frame the “twin transition” as a strategy to foster economic growth and durable social prosperity at the same time. However, Paraná argues in his introductory article (co-authored with Rodrigo Santaella-Gonçalves), this agenda is shaped by market logics and geopolitical competition, in ways that ultimately contradict the social and ecological goals it supposedly supports. The special issue has been designed to offer a systemic political-economy perspective on this inconsistency, grounded in critical social sciences and heterodox economics. Overall, it shows how the structural drivers of digitalisation can constrain its green potential and identifies pathways for redirecting the transition towards socially equitable and ecologically sustainable transformations.
My own article within this special issue aims to reconcile two separate trends in the study of the impacts of artificial intelligence (AI), one focused on the natural and the other on the social surroundings that supply resources for its production and use. I introduce the concept of the ‘dual footprint’ as a heuristic device to capture the commonalities and interdependencies between them. Originally borrowed from ecology, the concept denotes here the total impacts on the environment and society generated by AI’s production and use. It is an indicator of sustainability insofar as it grasps the degree to which the AI industry is failing to ensure the maintenance of the socio-economic systems and environmental conditions necessary to its production. To develop the concept in this way, it is necessary to (provisionally) renounce some of the accounting flavour of extant footprint measures, allowing for a more descriptive interpretation. In my article, the dual footprint serves as a mapping tool, linking impacts to specific locations and to the people and groups that inhabit them.
I use two in-depth case studies, each illustrating international flows of raw materials (nature) and of labour services (society). Case studies are a preliminary illustration of a broader idea, that will need to be developed further: that the AI industry is a value chain that spans national boundaries and perpetuates inherited global inequalities. Specifically, I use the case of Argentina as a provider to the United States and Madagascar as a provider to France, Japan and South Korea. It appears that the countries that drive AI development (here, the United States, France, Japan and South Korea) generate a massive demand for inputs and trigger social costs that, through the value chain, largely fall on more peripheral actors (here, Argentina and Madagascar). While Argentina and Madagascar differ under many respects, in both cases initial hopes of jobs and prosperity have failed to materialise as predominant outsourcing arrangements reinforce informality and precariousness without preventing environmental damage. Put differently, the arrangements in place distribute the costs and benefits of AI unequally, resulting in unsustainable practices and preventing the upward mobility of relatively more disadvantaged countries. The concept of the dual footprint is useful because it grasps how the environmental and social dimensions of the dual footprint emanate from similar underlying socio-economic processes and geographical trajectories.
A prior presentation of this idea can be found here.
A pre-print version of the article (green open access) can be found here.
Data Big and Small 