Climate And Resource Awareness is Imperative to Achieving Sustainable AI (and Preventing a Global AI Arms Race)

2025-02-27

AI Summary

This paper addresses a structural imbalance in sustainable AI research—its overemphasis on environmental dimensions while neglecting economic and social sustainability—by advocating an integrated approach reconciling climate awareness with resource accessibility. Methodologically, it innovatively proposes the Climate- and Resource-Aware ML (CARAML) framework, grounded in base-superstructure theory to expose political-economic contradictions in AI development; it further integrates political economy analysis, cross-scale policy modeling, and sustainable systems thinking to transcend purely technical solutions. The study establishes a tripartite sustainability paradigm—environmental, economic, and social—and delivers a multi-level governance roadmap spanning individual, organizational, national, and global scales. Its contributions include theoretical originality in reframing AI sustainability through critical political economy, and actionable institutional designs—particularly offering governance warnings and policy mechanisms to curb the AI arms race and advance equitable, globally inclusive innovation.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Efficient ML / Green AI

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Sustainability encompasses three key facets: economic, environmental, and social. However, the nascent discourse that is emerging on sustainable artificial intelligence (AI) has predominantly focused on the environmental sustainability of AI, often neglecting the economic and social aspects. Achieving truly sustainable AI necessitates addressing the tension between its climate awareness and its social sustainability, which hinges on equitable access to AI development resources. The concept of resource awareness advocates for broader access to the infrastructure required to develop AI, fostering equity in AI innovation. Yet, this push for improving accessibility often overlooks the environmental costs of expanding such resource usage. In this position paper, we argue that reconciling climate and resource awareness is essential to realizing the full potential of sustainable AI. We use the framework of base-superstructure to analyze how the material conditions are influencing the current AI discourse. We also introduce the Climate and Resource Aware Machine Learning (CARAML) framework to address this conflict and propose actionable recommendations spanning individual, community, industry, government, and global levels to achieve sustainable AI.

Problem

• Reconcile climate and resource awareness in AI
• Address equitable access to AI resources
• Propose sustainable AI framework CARAML

Innovation

• CARAML framework addresses sustainability
• Base-superstructure analyzes AI discourse
• Proposes multi-level actionable recommendations