A call to design AI that serves the whole system, not just people
In recent years, Canada has seen unprecedented wildfire seasons that burned massive tracts of forest, persistent long-term drinking-water advisories in First Nations communities, biodiversity decline and growing flood risk across multiple provinces. Our interconnected systems are under stress. Human health, biodiversity, infrastructure and cultural life are all impacted simultaneously.
Artificial Intelligence (AI) is entering this picture as a new tool. AI is becoming central to how Canada confronts its climate and environmental crises. Machine-learning AI models have already been used in Canada to help predict wildfire risk, map flood hazards, identify habitat loss trends and even monitor water quality. From wildfire prediction to water system monitoring, AI tools are becoming powerful.
But most of these systems are designed to optimize human-centred metrics like saving money, preventing property loss or reducing response time.
Ecocentric ethics flips the AI for-human-profit script. Ecocentrism holds that all living and non-living components of ecosystems have intrinsic value – a perspective that might be used to offer a framework to guide AI toward more just, sustainable outcomes. Whereas anthropocentric ethics asks, “What’s good for people?” Ecocentrism asks, “What’s good for the whole system?”
Expanding moral consideration to soil, water, species interactions and the atmosphere, ecocentrism challenges both the notion that humans are the measure of worth and the idea that technology exists primarily to serve human ends.
Why is this relevant to AI? Because AI systems encode the values and metrics we give them. If the metrics are only economic or human-centred, we risk building powerful tools that continue extracting from ecosystems rather than regenerating them.
So, what would happen if we applied an ecocentric ethic, one that values the integrity of entire ecosystems (living and non-living), as the guiding principle for AI development?
Applying such a framework to AI would change its objectives. Models would weigh ecological integrity, biodiversity and long-term planetary stability as core metrics. Under an ecocentric approach, AI would be evaluated not only on accuracy, speed or economic return, but also on indicators such as biodiversity, carbon sequestration or soil health and inter-generational justice.
Ecocentrism offers AI a lens to set broader goals like carbon storage in forests, watershed health, species diversity and cultural stewardship of land. It provides a practical alternative for expanding our definition of progress to include the health of the living and non-living systems that sustain us.
Additionally, ecocentrism resonates with many Indigenous worldviews in Canada, which see humans as part of an interconnected whole rather than as managers of nature. The Assembly of First Nations has repeatedly emphasized including Indigenous data sovereignty and knowledge in climate planning. Without such integration, AI risks reproducing colonial patterns, extracting data from Indigenous lands without consent and ignoring culturally significant indicators.
Some practical steps toward ecocentric AI would include: mandating ecological metrics in all federal AI climate tools, auditing AI’s footprint, creating public transparency about algorithms and data sets, providing equal access to AI deployment and last but not least, requiring Indigenous co-governance for AI projects on or about their territories.
This ethical and practical reorientation of AI demands slowing down some aspects of the AI race, investing in local and community-led systems and making ecological impact a first-order metric of success. These initiatives should not be undertaken just for the sake of the ecosystem’s usefulness; rather, they should be undertaken because of the ecosystem’s intrinsic value and to protect its rights.
Institutional and policy changes are equally important. Canada’s AI strategy and climate plans will need to evolve beyond innovation and short-term efficiency. Governments could set clearer standards for measuring ecological impact and encourage AI projects that restore rather than extract from ecosystems. Without that broader shift in policy, even the best AI tools risk being bolted onto an old, extractive model rather than transforming it.
This is not about adding more gadgets to climate response. It’s about a mindset shift. In an age of mega-fires, water insecurity and biodiversity loss, the question is not just whether AI can help Canada’s climate future but whether we have the mindset to build AI systems that respect the whole of nature, not only the human part.
If Canada applies ecocentric principles to its AI strategy now during a period of historic wildfires, unsafe water and rising floods, it could build tools that restore ecosystems rather than merely manage disasters.
Such a pivot could make Canada a global leader in “green AI,” where technology serves entire living systems, not just human interests. And it would send a signal that in an age of automation, ethical frameworks still matter.
