mlsanityData to 14 Oct 2026

Position: AI Must Become Planet-Centered, Not Just Human-Centered

arXiv 2606.13704 ↗First posted 9 Jun 2026cs.CY cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 85 references that are in the map. First posted on arXiv 9 Jun 2026.

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17 of 85 references.

References · 17

  1. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 183
  2. 2021The Values Encoded in Machine Learning Researchcited by 172
  3. 2019Principles alone cannot guarantee ethical AIcited by 135
  4. 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
  5. 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
  6. 2021Simulation Intelligence: Towards a New Generation of Scientific Methodscited by 68
  7. 2023Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Mediacited by 30
  8. 2021Climate-Invariant Machine Learningcited by 24
  9. 2022Current and Near-Term AI as a Potential Existential Risk Factorcited by 14
  10. 2022Metaethical Perspectives on 'Benchmarking' AI Ethicscited by 12
  11. 2023Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AIcited by 12
  12. 2022Collective wind farm operation based on a predictive model increases utility-scale energy productioncited by 6
5 more
  1. 2024Crafting Desirable Climate Trajectories with RL Explored Socio-Environmental Simulationscited by 4
  2. 2025Exploring Equity of Climate Policies using Multi-Agent Multi-Objective Reinforcement Learningcited by 2
  3. 2025From Prediction to Foresight: The Role of AI in Designing Responsible Futurescited by 2
  4. 2025Exploring the Viability of the Updated World3 Model for Examining the Impact of Computing on Planetary Boundariescited by 1
  5. 2025Expert Assessment: The Systemic Environmental Risks of Artficial Intelligencecited by 1

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