The dual footprint of artificial intelligence: environmental and social impacts across the globe

TL;DR

This paper introduces the 'dual footprint' concept to analyze AI's interconnected environmental and social impacts, highlighting global inequalities and unsustainable practices in AI supply chains.

Contribution

It presents a novel framework linking AI's environmental and social impacts through case studies of international resource and data flows, emphasizing global inequalities.

Findings

AI industry spans multiple countries, creating unequal impacts.

Resource and data flows perpetuate global inequalities.

AI's impacts are rooted in socioeconomic and geographical factors.

Abstract

This article introduces the concept of the 'dual footprint' as a heuristic device to capture the commonalities and interdependencies between the different impacts of artificial intelligence (AI) on the natural and social surroundings that supply resources for its production and use. Two in-depth case studies, each illustrating international flows of raw materials and of data work services, portray 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 the dual footprint emanate from similar underlying socioeconomic processes and geographical trajectories.