Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions
Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions
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DOI：
10.3390/su14095172
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发表时间：
2022-05-01
期刊：
影响因子：
3.9
通讯作者：
Combaz, Jacques
中科院分区：
文献类型：
--
作者：
Ligozat, Anne-Laure;Lefevre, Julien;Combaz, Jacques
在过去的十年里，人工智能取得了巨大的进步，现在它被视为解决环境问题的首选工具，首先是温室气体排放（GHG）。与此同时，深度学习社区开始意识到，使用越来越多的参数训练模型需要大量的能源，因此会产生温室气体排放。据我们所知，质疑人工智能解决方案对环境（AI for绿色）而不仅仅是GHG的完整净环境影响从未得到直接解决。在这篇文章中，我们建议研究人工智能对绿色可能产生的负面影响。首先，我们回顾了不同类型的人工智能影响;然后，我们提出了用于评估这些影响的不同方法，并展示了如何将生命周期评估应用于人工智能服务。最后，我们讨论了如何评估一般人工智能服务的环境有用性，并指出现有的工作在人工智能的绿色的局限性。
In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and, in the first place, greenhouse gas emissions (GHG). At the same time, the deep learning community began to realize that training models with more and more parameters require a lot of energy and, as a consequence, GHG emissions. To our knowledge, questioning the complete net environmental impacts of AI solutions for the environment (AI for Green) and not only GHG, has never been addressed directly. In this article, we propose to study the possible negative impacts of AI for Green. First, we review the different types of AI impacts; then, we present the different methodologies used to assess those impacts and show how to apply life cycle assessment to AI services. Finally, we discuss how to assess the environmental usefulness of a general AI service and point out the limitations of existing work in AI for Green.