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This position paper proposes Planet-Centered AI (PCAI), a design philosophy that shifts focus from purely human-centric optimization to aligning with planetary-scale socio-ecological systems. It argues that current frameworks fail under conditions of systemic risk and non-stationarity, urging researchers to integrate trajectory-oriented evaluation into the full AI lifecycle. The core contribution is a falsifiable claim that neglecting these broader consequences will likely exacerbate global instability rather than mitigate it.
Position: AI Must Become Planet-Centered, Not Just Human-Centered
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 especially consequential under current planetary conditions characterized by systemic risk, non-stationarity, and deep uncertainty. We then articulate how PCAI reshapes the AI lifecycle, from problem formulation and model design to evaluation and deployment, by emphasizing alignment with global agendas, developing system-aware AI foundations, trajectory-oriented evaluation, and monitorability. Finally, we advance a falsifiable claim: AI systems optimized without explicit consideration of systemic consequences are more likely to exacerbate systemic instability than to mitigate it.
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 especially consequential under current planetary conditions characterized by systemic risk, non-stationarity, and deep uncertainty. We then articulate how PCAI reshapes the AI lifecycle, from problem formulation and model design to evaluation and deployment, by emphasizing alignment with global agendas, developing system-aware AI foundations, trajectory-oriented evaluation, and monitorability. Finally, we advance a falsifiable claim: AI systems optimized without explicit consideration of systemic consequences are more likely to exacerbate systemic instability than to mitigate it.
Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2606.13704 [cs.CY]
(or arXiv:2606.13704v1 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2606.13704 Focus to learn more arXiv-issued DOI via DataCite
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