There is a growing urgency to ensure that data justice efforts are more closely aligned with the critical need for global sustainability. As part of this initiative, we are pleased to announce the publication of a new article by EDI Director of Research, Professor Sabina Leonelli, in the respected Harvard Data Science Review (HDSR).

Leonelli’s contribution, “Environmental Intelligence: Redefining the Philosophical Premises of AI,” presents a philosophical and practical framework for how the data community can actively engage in environmental preservation. She challenges us to consider whether computing is intended to transcend nature or support it.

Thirteen discussants from diverse backgrounds and perspectives were invited to offer critical insights and constructive feedback. The full collection – including RDA Secretary General Hilary Hanahoe’s reflections on achieving Environmental Intelligence buy-in and implementation through learning from the Research Data Alliance in Trust, Openness, and Transparency—key elements of the Environmental Intelligence Framework – is available at: https://hdsr.mitpress.mit.edu/volume7issue4

Environmental Intelligence (EI) is a concept that prompts us to step back and reflect carefully on the purpose of data-driven computing in an era marked by climate challenges. It signifies a necessary shift away from the traditional concept of Artificial Intelligence, which often concentrates on a universal, abstract notion of intelligence. Instead, EI advocates that advanced data systems should primarily aim to sustain and enhance life on Earth.

The EI framework perceives intelligence as inherently distributed, embodied, and context-dependent. This means:

• Intelligence is not limited to a single centralised model; it emerges from the complex interactions between organisms, technology, and their specific ecosystems.
• Data systems should be built upon and responsive to local, environmental, and social conditions, rather than aiming for a uniform, abstract solution.

The core principles and frameworks of Environmental Intelligence align directly with EDI’s central priorities: Data Justice, Accountability, and Governance.

Ethical data practices must extend beyond human equity to encompass planetary health. When data systems exacerbate environmental crises, they ultimately breach the standards of justice by disproportionately harming vulnerable communities and future generations. Therefore, data justice must inherently consider the environmental externalities of technology. We require systems that measure and enforce transparency regarding environmental impacts, fostering accountability.

Environmental Intelligence offers a philosophical foundation for moving beyond a focus on social biases and advocating for transparency metrics that reveal the ecological costs of AI—such as the resource demands of infrastructure—alongside more traditional social risks and systemic impacts. EI underscores that diversity is vital not only for social fairness but also for epistemic resilience. It warns against algorithmic monoculture—standardised systems that are too fragile to cope with the complex variation in the real world.