arXiv:2502.20016v2 Announce Type: replace Abstract: Sustainability encompasses three key facets: economic, environmental, and social. However, the nascent discourse on sustainable artificial intelligence (AI) predominantly focuses on the environmental sustainability of AI, neglecting the economic and social aspects. Achieving truly sustainable AI necessitates addressing the tension between its environmental sustainability, which emphasises mitigating AI's climate impact, and its social sustainability, hinging on equitable access to AI development resources. This push for increased accessibilit
The increasing scale and resource demands of AI development are forcing a re-evaluation of its broader societal impacts beyond just environmental concerns, especially as global competition intensifies.
This highlights that sustainability in AI is not a singular issue but a complex interplay of environmental, economic, and social factors that will profoundly shape policy, investment, and geopolitical dynamics.
The scope of the AI sustainability discussion is expanding beyond solely environmental metrics to include critical dimensions like economic accessibility and social equity, demanding more holistic solutions.
- · Open-source AI initiatives
- · Developers of energy-efficient AI hardware
- · Governments with sustainable AI policies
- · AI developers with high environmental footprints
- · Regions lacking equitable access to AI resources
- · Proprietary AI models with high TCO
Increased pressure on AI developers to demonstrate comprehensive sustainability, including social and economic equity.
Potential for regulatory frameworks that mandate broader sustainability criteria for AI development and deployment.
Shift towards federated or distributed AI models to democratize access and reduce concentrated environmental impact, potentially reshaping the global AI landscape.
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