摘要: AI-enabled technologies are often presented as tools to address global societal challenges, including the climate crisis. Machine learning enhances the capacity to discover patterns and predict outcomes that can be used to optimize supply chains or circular economies or to make renewable energy more efficient (Herweijer and Waughray, 2018). What gets lost in these visions of “AI for Good” and “AI for Sustainability” is that AI is not simply a technical solution, but is also part of the problem: storing large amounts of data, training models, running complex software, manufacturing and disposing of hardware all have significant environmental and social costs. This dual aspect of AI, as solution as well as problem for environmental sustainability, is nearly absent in documents on AI policy and regulation. As this chapter will argue, the scarce engagement with the environmental sustainability issue in the policy and …
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