Creutzig, F., Denton, F., Hine, E. et al. (6 more authors) (2026) Governing artificial intelligence for planetary health. The Lancet Planetary Health, 10 (3). 101408. ISSN: 2542-5196
Abstract
Establishing global governance of artificial intelligence (AI) is becoming an increasingly pressing challenge to ensure the provision of global public goods and to mitigate harmful effects on societies and the planet. Current debates around AI take various forms, follow diverse narratives, and centre variously on economic, social, environmental, or safety aspects. Here, we make three contributions. First, we classify risks and challenges of AI across the social, planetary, and safety domains. Second, we show that AI should be governed as a global commons, requiring coordinated interventions across all three domains, reflecting relevant inter-domain feedback loops, and root drivers, such as the pursuit of monopolistic AI power and the AI-infused media environment. Third, we identify data, energy, and compute as relevant regulatory dimensions across social, planetary, and safety domains. We conclude by emphasising the importance of limiting agentic AI, incentivising depolarising algorithms on social media, and setting AI dynamics within the context of global equity.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Dates: |
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| Institution: | The University of Leeds |
| Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) |
| Date Deposited: | 12 May 2026 15:07 |
| Last Modified: | 12 May 2026 15:07 |
| Status: | Published |
| Publisher: | Elsevier |
| Identification Number: | 10.1016/j.lanplh.2025.101408 |
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| Sustainable Development Goals: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:240747 |


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