arXiv:2606.13704v1 Announce Type: cross Abstract: This position paper argues that contemporary AI paradigms are insufficient for supporting complex global goals and introduces Planet-Centered AI (PCAI) as a design philosophy and research agenda that reorients AI toward planetary-scale socio-ecological systems and their long-term trajectories. A planet-centered approach is grounded in systems thinking, treating Earth as an interconnected whole of which humans are part. We diagnose recurring limitations across AI frameworks, many of which remain human-centered, and show why these become especial
Growing awareness of AI's environmental impact and its potential to exacerbate global challenges, coupled with increasing urgency around large-scale socio-ecological crises, necessitates a re-evaluation of AI's fundamental design principles.
This paper proposes a foundational paradigm shift for AI development, moving beyond a purely human-centric view to explicitly integrate planetary well-being, which will influence future research, investment, and regulatory frameworks.
AI's design philosophy would expand from optimizing for human needs to optimizing for the health of planetary socio-ecological systems, leading to different problem formulations, ethical considerations, and deployment strategies.
- · Environmental scientists
- · Sustainable technology developers
- · Global governance bodies
- · Earth system modelers
- · Purely profit-driven AI developers
- · AI frameworks detached from ecological impact
- · Industries with high environmental footprints relying on current AI approaches
Increased funding and research into AI applications focused on climate modeling, biodiversity preservation, and sustainable resource management.
New AI ethics guidelines and regulatory frameworks that incorporate planetary boundaries and ecological impact assessments alongside human-centric considerations.
The emergence of 'planetary health' as a key performance indicator for AI systems, influencing AI hardware design, energy consumption, and data center locations.
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Read at arXiv cs.AI