Our Observatory of Planetary Justice Impacts of AI aims to make visible the impacts of the AI supply chain on human and more-than-human communities on the planet. In this blog, we’re rounding up the latest cases added to the Observatory, highlighting the ones where affected communities are pushing back against the development  of AI infrastructure along the supply chain.

The AI models that we use in our day-to-day life are the products of a vast and complex supply chain, the planetary impacts of which we aim to uncover and make visible. Our Observatory of Planetary Justice Impacts of AI aims to make visible the impacts of the AI supply chain on human and more-than-human communities on the planet. In this blog, we are putting the spotlight on a series of new data center projects in Canada and the grassroots movements mobilizing against them.

Our Observatory of Planetary Justice Impacts of AI aims to make visible the impacts of the AI supply chain on human and more-than-human communities on the planet. This month, instead of a roundup of the main cases we’ve found, we are putting the spotlight on one particular case: the 50 to 60 new data centres being built in and around London.

The AI models that we use in our day-to-day life are the products of a vast and complex supply chain, the planetary impacts of which we aim to uncover and make visible. In our Observatory of Planetary Justice Impacts of AI, we aim to bear witness to the impacts of the AI supply chain, so as to to spread information, share resources, and build a catalogue of real-world examples. In this blog, we highlight five of the ten latest additions to the Observatory, which highlight political and structural injustice throughout the AI supply chain.

The AI models that we use in our day-to-day life are the products of a vast and complex supply chain, the planetary impacts of which we aim to uncover and make visible. In our Observatory of Planetary Justice Impacts of AI, we aim to bear witness to the impacts of the AI supply chain, so as to to spread information, share resources, and build a catalogue of real-world examples.

In this blog, we highlight four of the ten latest additions to the Observatory, which highlight some power dynamics reminiscent of coloniality. In collecting these cases, we are looking at the bigger picture and how these cases might be understood through thinking about epistemic justice and neocolonial relations.

AI systems are built on global supply chains with deep planetary impacts that often remain hidden. In this update, we document new cases from around the world — from lithium and rare-earth extraction to energy- and water-intensive data centres — showing how narratives of progress in AI mask environmental harm, social inequality, and a lack of transparency.

From lithium mining in Zimbabwe and rare-earth extraction in Myanmar, to data centres in India, the United States, Mexico, the UK, and Germany, these cases challenge dominant narratives of technological progress.

AI has global reach, and global consequences. As we try to examine and map AI and its planetary justice impacts, we want to map and expose the testimonies of real-world impacts of the AI supply chain. In our Observatory of Planetary Justice Impacts of AI, we endeavour to bear witness to the impacts of AI, so as to to spread information, share resources, and build a catalogue of real-world examples. In this blog, we highlight some of the latest additions to the Observatory, which we believe are stark examples of ways in which power and resistance coexist and balance each other at every stage of the AI supply chain and in many locations across the world. 

In this reflection from the Data for Public Goals event at Politecnico di Milano, we call for a radical reframing of how we understand and govern data for AI. Moving beyond technical metrics and ethical tweaks, the piece introduces a planetary justice lens that traces AI's impacts across its full supply chain–from resource extraction to model deployment. Highlighting the ecological, labor, and epistemic costs embedded in AI systems, the post challenges the narrative of data neutrality and questions the assumption that automation is inherently beneficial. What if, instead of optimizing AI, we asked whether it is needed at all–and who gets to decide?