AI for climate: towards environmentally responsible research and innovation

In contrast to the technologies of the industrial revolutions, which caused unprecedent levels of air and water pollution and darkened the sky of many cities, digital technologies seem clean and environmentally friendly.

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But during the last years we have started to realize that these technologies are less innocent than they seem.

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Consider climate change and artificial intelligence (AI). AI provides a lot of opportunities. Data analysis by AI can help scientists study climate change and AI can create smarter and more economic ways of dealing with energy – something very important in the current energy crisis. But there are also problems. Data centers use energy, and it is known for example that large language models use a lot of energy.

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Moreover, even if the software were clean, software is always connected with hardware and infrastructure. And that hardware and infrastructure needs to be produced and maintained, requiring natural resources and thereby impacting the environment. As Kate Crawford has shown in her book The Atlas of AI, for example, the political ecology of AI is dirtier than we think.

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If we want to work towards a more sustainable future for people and planet, therefore, we not only need to use the available digital technologies to help us to mitigate climate change and reduce energy consumption; we also need to change the technologies themselves in a more sustainable and climate-friendly direction. We need clever ways to develop and use what I have called AI for climate.

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Top-down regulation and government incentives is one way to achieve this; AI policy needs to include an environmental and climate component. But we cannot and should not wait until politicians take action. Environmentally responsible research and innovation is something developers, universities, and companies can do already now. People should be stimulated to use their creativity to make better technology, with “better” meaning: better for people and better for climate and environment.

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Next to integrating environment and climate concerns in our thinking about the ethics and politics of AI, which I have tried to do in my work on AI ethics and the political philosophy of AI, we need concrete proposals that can be integrated in development and management practice. An example is “sustainability budgets’: a practical solution that gamifies the problem, respects the autonomy of developers, and can be integrated in software design and governance of AI.

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AI can have a bright future if we make sure it’s a green one.

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