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.

A new batch of case studies has been added to our Observatory of Planetary Justice Impacts of AI! This is one of our core initiatives, where we map real-world examples of the planetary justice impacts of the AI supply chain. It is a continuous, adaptive effort to collect data, to track cases and investigations, and see what patterns emerge.

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?

The AI + Planetary Justice Alliance joined researchers, organizers, and practitioners in Bengaluru for the Mapping Data Work workshop — a gathering that foregrounded the invisible labor behind AI systems. In our blog, we report key takeaways, reflect on what it means to build “ethical datasets,” question the assumptions behind datafication, and share insights from our own research on epistemic justice in agricultural AI in India. What if the push to represent is just another form of surveillance? And why are we building AI in the first place? Read the full piece to explore the planetary justice implications of some of AI’s quietest supply chains: data work.