The dual footprint of artificial intelligence: environmental and social impacts across the globe
- URL: http://arxiv.org/abs/2512.01456v1
- Date: Mon, 01 Dec 2025 09:43:50 GMT
- Title: The dual footprint of artificial intelligence: environmental and social impacts across the globe
- Authors: Paola Tubaro,
- Abstract summary: Two in-depth case studies portray the AI industry as a value chain that spans national boundaries.<n>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.<n>The dual footprint grasps how the environmental and social dimensions of the dual footprint from similar underlying socioeconomic processes and geographical trajectories.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- The California Report on Frontier AI Policy [110.35302787349856]
Continued progress in frontier AI carries the potential for profound advances in scientific discovery, economic productivity, and broader social well-being.<n>As the epicenter of global AI innovation, California has a unique opportunity to continue supporting developments in frontier AI.<n>Report derives policy principles that can inform how California approaches the use, assessment, and governance of frontier AI.
arXiv Detail & Related papers (2025-06-17T23:33:21Z) - Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2) [49.142289900583705]
We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability.<n>We discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI.<n>Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI.
arXiv Detail & Related papers (2025-05-22T12:52:34Z) - Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment.<n>We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
arXiv Detail & Related papers (2025-04-01T13:53:11Z) - Climate And Resource Awareness is Imperative to Achieving Sustainable AI (and Preventing a Global AI Arms Race) [6.570828098873743]
We argue that reconciling climate and resource awareness is essential to realizing the full potential of sustainable AI.<n>We introduce the Climate and Resource Aware Machine Learning (CARAML) framework to address this conflict.
arXiv Detail & Related papers (2025-02-27T11:54:10Z) - Towards Environmentally Equitable AI [23.332350246411124]
We advocate environmental equity as a priority for the management of future AI systems.<n>We uncover the potential of equity-aware geographical load balancing to fairly re-distribute the environmental cost across different regions.<n>We conclude by discussing a few future directions to exploit the full potential of system management approaches to mitigate AI's environmental inequity.
arXiv Detail & Related papers (2024-12-21T08:46:19Z) - Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI [67.58673784790375]
We argue that the 'bigger is better' AI paradigm is not only fragile scientifically, but comes with undesirable consequences.<n>First, it is not sustainable, as, despite efficiency improvements, its compute demands increase faster than model performance.<n>Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate.
arXiv Detail & Related papers (2024-09-21T14:43:54Z) - Towards Socially and Environmentally Responsible AI [33.398841227207264]
In this paper, we propose equitable geographical load balancing (GLB) to fairly balance AI's regional social and environmental costs.
Our empirical results demonstrate that while the existing GLB algorithms result in disproportionately large social and environmental costs in certain regions, our proposed equitable GLB can fairly balance AI's negative social and environmental costs across all the regions.
arXiv Detail & Related papers (2024-04-23T00:41:41Z) - On the Emergence of Symmetrical Reality [51.21203247240322]
We introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations.
We propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality.
arXiv Detail & Related papers (2024-01-26T16:09:39Z) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z) - Artificial Intelligence in Sustainable Vertical Farming [0.0]
The paper provides a comprehensive exploration of the role of AI in sustainable vertical farming.
The review synthesizes the current state of AI applications, encompassing machine learning, computer vision, the Internet of Things (IoT), and robotics.
The implications extend beyond efficiency gains, considering economic viability, reduced environmental impact, and increased food security.
arXiv Detail & Related papers (2023-11-17T22:15:41Z) - Applications and Societal Implications of Artificial Intelligence in
Manufacturing: A Systematic Review [0.3867363075280544]
The study finds that there is a predominantly optimistic outlook in prior literature regarding AI's impact on firms.
The paper draws analogies to historical cases and other examples to provide a contextual perspective on potential societal effects of industrial AI.
arXiv Detail & Related papers (2023-07-25T07:17:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.