Improving Human and Machine Intelligence for Climate Action

Tennessee Warbler

17-09-2025

Recently, the intelligence of Kingfisher has blossomed remarkably. Yet, near his dwelling, across two ponds, lived a sage known as Monk Bird. After just a few conversations, Kingfisher realized he still had much to learn.

In “Meditation Master”; Wild Wise Weird [1]

© Wix AI

Artificial intelligence (AI) is rapidly transforming societies, economies, and governance systems. While it holds great promise for tackling planetary crises, it also brings risks of bias, inequity, and unintended consequences. A recent perspective by Debnath, Creutzig, Sovacool, and Shuckburgh [2] argues that aligning AI with climate action requires designing systems that are less biased, more accountable, and firmly integrated with human knowledge networks.

The study identifies three key pathways for AI to advance climate solutions. First, AI can help build a planetary “epistemic web”—a knowledge system that links data, models, and human perspectives across disciplines and regions [3]. Second, it can strengthen mitigation and adaptation by detecting social tipping elements, such as shifts in public opinion or policy, that accelerate decarbonization [4]. Third, AI can reduce data injustices by ensuring diverse, representative datasets that reflect the realities of marginalized communities, particularly in the Global South [5].

Yet, significant challenges remain. Many AI systems are trained on biased or incomplete data, producing distorted forecasts and inequitable outcomes [2]. They may also deepen polarization by amplifying misinformation through digital networks [6]. To address these risks, the authors highlight the importance of human-in-the-loop AI, where humans remain embedded in design and evaluation. This collaboration improves interpretability, accountability, and public trust [7].

The broader lesson is clear: AI is not a silver bullet. Progress depends on pluralism—integrating insights from the social sciences, humanities, and technical fields to co-create fairer and more effective climate intelligence. Such an approach builds resilience not only to environmental risks but also to societal tipping points such as inequality and democratic erosion [2].

Digital technologies, like ecosystems, generate feedback loops that can either stabilize or destabilize societies. Embedding Nature Quotient (NQ) into AI design will help make digital systems as part of Earth’s life-support infrastructure, ensuring they serve both humanity and planetary health [8,9].

References

[1] Vuong QH. (2024). Wild Wise Weird. https://books.google.com/books?id=N10jEQAAQBAJ

[2] Debnath R, Creutzig F, Sovacool BK, Shuckburgh E. (2023). Harnessing human and machine intelligence for planetary-level climate action. npj Climate Action, 2, 20. https://doi.org/10.1038/s44168-023-00056-3

[3] Renn J. (2020). The evolution of knowledge. Princeton University Press.

[4] Otto IM, et al. (2020). Social tipping dynamics for stabilizing Earth’s climate by 2050. PNAS, 117, 2354-2365. https://doi.org/10.1073/pnas.1900577117

[5] Taylor L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4. https://doi.org/10.1177/2053951717736335

[6] Hornsey MJ, Lewandowsky S. (2022). A toolkit for understanding and addressing climate scepticism. Nature Human Behaviour, 6, 1454-1464. https://doi.org/10.1038/s41562-022-01463-y

[7] Mosqueira-Rey E, et al. (2022). Human-in-the-loop machine learning: a state of the art. Artificial Intelligence Review, 56, 3005-3054. https://doi.org/10.1007/s10462-022-10246-w

[8] Vuong QH, Nguyen MH. (2025). On Nature Quotient. Pacific Conservation Biology, 31, PC25028. https://doi.org/10.1071/PC25028 

[9] Nguyen MH. (2024). How can satirical fables offer us a vision for sustainability? Visions for Sustainability, 23(11267), 323-328. https://doi.org/10.13135/2384-8677/11267