Environmental impacts – Data Big and Small

The impacts of artificial intelligence (AI) on the natural and social surroundings that supply resources for its production and use have been studied separately so far. In a new article, part of a forthcoming special issue of the journal Globalizations, 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 in my analysis the total impacts on the natural and social surroundings that supply the resources necessary for 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 social systems, economic structures, 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 primarily serves as a mapping tool, linking impacts to specific locations and to the people and groups that inhabit them.

Gloria Mendoza / ‘The Environmental Impact of Data Centers in Vulnerable Ecosystems’ / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/

My analysis draws on recent research that challenges idealized narratives of AI as the sole result of mathematics and code, or as the fancied machinic replacement of human brains. The production of AI relies on global value chains which, like those of textiles and electronics, take shape within the broader context of globalization, its long-standing trends of outsourcing and offshoring, and the cross-country disparities on which it thrives.

The argument is based on two case studies, each illustrating AI-induced cross-country flows of natural resources and data labour. The first involves Argentina as a supplier to the United States, while the second includes Madagascar and its primary export destinations: Japan and South Korea for raw materials, France for data work. These two cases portray the AI landscape as an asymmetric structure, where the countries that lead the tech race generate a massive demand for imports of raw materials, components, and intermediate goods and services. Core AI producers trigger the footprint and therefore should bear responsibility for it, but the pressure on (natural and social) resources and the ensuing impacts occur predominantly elsewhere. Cross-country value chains shift the burden toward more peripheral players, obscuring the extent to which AI is material- and labour-intensive.

Flows of raw materials (mainly nickel and cobalt from the Ambatovy mining project) from Madagascar to East Asia and, to a lesser extent, Europe and North America (top); flows of data work services from Madagascar to France, followed by North America and to a lesser extent, East Easia (bottom). Madagascar, one of the poorest countries in the world, contributes to state-of-the-art AI production without managing to move up the value chain.

This drain of resources toward AI engenders adverse effects in peripheral countries. Mining notoriously generates conflicts, and data work conditions are so poor that other segments of society – from local employers to workers’ families and even informal-economy actors – must step in to cover part of the costs. The current arrangements thus fail to ensure their own sustainability over time. Additionally, the aspirations of these countries to leverage their participation to the AI value chain as a development opportunity, and to transition toward leading positions, remain unfulfilled.

The dual footprint can fruitfully dialogue with the critical literature that leverages the concepts of extractivism (for example, Cecilia Rikap‘s concept of “twin” extractivism) and dependency (as theorised for example by Jonas Valente and Rafael Grohmann). Its contribution lies mainly in the effort to operationalise the ideas of more abstract social theories, while also facilitating mutual enrichment between different literatures.

Read the full paper: subscription-protected or open-access preprint.

The paper was developed as part of an initiative on ‘The Political Economy of Green-Digital Transition‘, organised by Edemilson Paraná in 2024 at LUT University in Finland. Further, the idea that the environmental and social dimensions of AI production emanate from similar underlying socio-economic processes and geographical trajectories constitutes the foundation of SEED – Social and Environmental Effects of Data Connectivity, a new DiPLab project that investigates how data extraction and material extraction are deeply interconnected. It stems from a collaboration with Núcleo Milenio FAIR at the Pontificia Universidad Católica de Chile and compares data and material infrastructures in Europe and South America.

Last week in Santiago, Chile, I had the tremendous opportunity to give a keynote speech at the 4th annual workshop of the Millennium Nucleus on the Evolution of Work (M-NEW), of which I am also a senior international member. This interdisciplinary workshop brought together labour scholars from various parts of Latin America and beyond. I really liked the inspiring talks and the friendly and stimulating interactions with colleagues.

My own talk drew on my multi-year research programme on the crucial yet invisible human labor behind the global production of artificial intelligence. I first examined the evolution of this form of work over the last two decades, demonstrating that while its core functions in the development of smart systems have remained consistent, the scope and volume of such tasks have expanded significantly. I then analyzed the organization of this labour at the intersection of three trends in recent globalization: outsourcing, offshoring, and digitalization. These dynamics account for the marginalization of these workers within the tech industry and the relocation of their labor to lower-wage countries. Based on these insights, I described four cases—Venezuela, Argentina, Brazil, and Chile—highlighting the diverse effects of local conditions. I concluded by identifying emerging scientific and policy challenges, particularly concerning the recognition of skills, and the place of the informal economy.

The following week, still in Santiago, I was excited to participate in the kick-off meeting of the new research project SEED (“Social and Environmental Effects of Data connectivity: Hybrid ecologies of transoceanic cables and data centers in Chile and France”), a collaboration between my research group DiPLab and another Millennium Nucleus, FAIR (“Futures of Artificial Intelligence Research”). SEED received joint funding from the ECOS-SUD programme (France) and ANID (Chile) to analyse the AI value chain, from its production and development to its impact on employment, use and environmental consequences, by studying the Valparaíso-Santiago de Chile and Marseille-Paris axes.

My presentation introduced the concept of the ‘dual footprint’ as a heuristic device to capture the commonalities and interdependencies between the different impacts of AI on the natural and social surroundings that supply resources for its production and use. I framed the AI industry as a value chain that spans national boundaries and perpetuates inherited global inequalities. The countries that drive AI development generate a massive demand for inputs and trigger social costs that, through the value chain, largely fall on more peripheral actors. The arrangements in place distribute the costs and benefits of AI unequally, resulting in unsustainable practices and preventing the upward mobility of more disadvantaged countries. The dual footprint grasps how the environmental and social dimensions of AI emanate from similar underlying socio-economic processes and geographical trajectories.

Tag Archives: Environmental impacts

The dual footprint of AI

AI, labour and natural resources in Santiago