Environmental justice aspects of the (un)intended consequences of AI
What is the socio-ecological impact of AI-enabled systems?
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AI has consequences much more far-reaching and multi-dimensional than many other technologies. How do we even start to think about the impact of AI on the Environment?
Beyond the carbon footprint of training a large machine learning model, it is helpful to consider how a model is utilized as part of a social context. From a systems thinking perspective, it will inevitably impact all other human and non-human actors already participating in that context.
The Environmental Justice movement emerged as a response to the disproportionate exposure to pollutants and environmental hazards borne by marginalized and racialized communities. The US Environmental Protection Agency defines environmental justice as "the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income, with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies." [1]
Around the world, we've seen the Rights of Nature movement - "the recognition and honoring that Nature has rights. It is the recognition that our ecosystems – including trees, oceans, animals, mountains – have rights just as human beings have rights." [2]
Puzzling through this myself, I think that any investigation of the socio-ecological impacts of AI needs to consider the following:
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Data ownership and governance frameworks
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The similarities and differences between data governance and climate governance models
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Could new business models enable equitable futures for people and the Planet?
What do you think?
References:
[1] https://www.epa.gov/environmentaljustice
[2] https://www.therightsofnature.org/what-is-rights-of-nature/
欢迎，[bobi.rakova]，很高兴您能来到这里，感谢您分享质疑 AI 与环境交集的问题！
// 与许多其他技术相比，人工智能的影响更为深远和多维。我们如何开始考虑人工智能对环境的影响？
事实上，简单地搜索结果的排名将对我们的日常决策产生非线性影响：决策者，企业，由于这些决策，他们将做出不同的选择。行车路线导航器选择了哪些路径，或者返回了哪些医疗程序，可能会带来截然不同的结果。想法：因此，也许搜索引擎应该开发一个可选的“安全搜索”结果，这些结果与财务或医疗建议具有相同的责任。
您正确地提到了训练模型的碳足迹，但我不确定它与开采加密货币的碳足迹有多大不同。建议：最好添加一些数字来证实这一说法。
然而，人工智能与环境正义运动和自然权利运动有什么关系？它们与你提到的三个问题有什么联系？问题：您能否详细说明数据所有权和数据治理如何影响气候治理？现状是什么，应该如何？
附注由于您在选择“静默”或“草稿”之前保存了主题，因此，我们收到了有关它的通知，虽然它目前无法在索引中发现，但仍可通过直接 URL 访问。因此，希望我们的评论有助于改进您的写作。
Welcome, [bobi.rakova], amazing to have you here, and thank you for sharing the issue questioning the intersection of AI and environment!
// AI has consequences much more far-reaching and multi-dimensional than many other technologies. How do we even start to think about the impact of AI on the Environment?
Indeed, simply the ranking of search results will have non-linear effects to our daily decisions: of policy makers, of businesses, that, because of those decisions shall make different choices. What path driving directions navigator chooses, or what medical procedure search engine returns may entail very different outcomes. Idea: So, perhaps search engines should develop an optional "safe search" feature to display only results that are treated with the same responsibility as financial or medical advice.
You are correctly mentioning carbon footprint of training models, though, I'm not sure how much it is different from carbon footprint of say, mining crypto-currencies. Suggestion: it would be good to add some numbers to substantiate the claim.
However, what does AI have to do with Environmental Justice movement, and Rights of Nature movement? How are they connected with the three issues you mentioned? Question: Could you elaborate on how, say data ownership and data governance affects climate governance? What's the status quo, and how it should be?
P.S. As you've saved the topic before choosing "silently" or "draft", therefore, we got the notification about it, while it is currently not discover-able in the index, it is still accessible via direct URL. So, hope our comments are useful in improving your write up.
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感谢您的周到回复[Mindey]！
与 AI 的碳足迹相关，我所知道的最佳参考资料是：Strubell, E.、Ganesh, A. 和 McCallum, A.（2019 年）。 NLP 深度学习的能源和政策考虑。 https://arxiv.org/abs/1906.02243
例如，训练一个支持该公司搜索引擎的谷歌语言模型 (BERT) 的版本，产生了 1,438 磅的二氧化碳当量，据 Strubell 估计，与纽约市和旧金山之间的往返航班几乎相同。在实践中，单个模型在投入生产之前要经过大量训练。
我应该澄清一下，我指的不是一般意义上的 AI，而是指在开发环境解决方案时使用机器学习。与此相关的一篇很棒的概述论文是：Rolnick, D., et al。 (2019)。通过机器学习应对气候变化。 https://arxiv.org/pdf/1906.05433.pdf 在此处查看它的交互式版本：https://www.climatechange.ai/summaries
例如，人工智能系统已被用于通过使用卫星和无人机图像数据来预测森林固碳潜力；计算机视觉算法用于确定合适的种植地点、监测植物健康和分析趋势；人工智能还用于识别可能非法砍伐森林的地方，以及评估因火灾、疾病、昆虫或其他原因造成的风险。 Rolnick 等人的论文中提供了所有这些用途的参考资料。
我对环境 AI 模型中的数据治理模型感兴趣。例如，在用于森林恢复项目的人工智能的情况下：
哪些数据所有权和数据治理框架可以在使用技术的方式上增强土著世界观的公平性和包容性？
此类 AI 模型对生态系统服务和生态系统服务概念的潜在影响是什么？ Comberti 等人。 (2015)。生态系统服务还是生态系统服务？重视人类与生态系统之间的耕作和相互关系。全球环境变化，34, 247-262。 https://www.sciencedirect.com/science/article/pii/S0959378015300145
我通过选中“发布”选项编辑了帖子，我希望我现在可以发现它，但是，我仍然没有在主拼图页面上看到它。
Thank you for your thoughtful reply [Mindey]!
Related to AI's carbon footprint, the best reference I know about is: Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. https://arxiv.org/abs/1906.02243
For example, training a version of Google’s language model (BERT) which underpins the company’s search engine, produced 1,438 pounds of CO2 equivalent in Strubell’s estimate to nearly the same as a round-trip flight between New York City and San Francisco. In practice, a single model is trained a large number of times before it's put in production.
I should have clariefied that I don't mean AI in general but rather the use of Machine Learning in the development of environmental solutions. A great overview paper related to that is: Rolnick, D., et al. (2019). Tackling climate change with machine learning. https://arxiv.org/pdf/1906.05433.pdf See an interactive version of it here: https://www.climatechange.ai/summaries
For example, AI systems have been used to predict forest carbon sequestration potential through the use of satellite and drone image data; computer vision algorithms are used in identifying appropriate planting sites, monitoring plant health, and analyzing trends; AI is also used to identify where deforestation may have been conducted illegally as well as assess risks due to fire, disease, insects, or other causes. References on all of these uses are available with the paper by Rolnick et al.
I'm interested in data governance models in the case of environmental AI models. For example, in the case of AI used for forest restoration projects:
what data ownership and data governance frameworks could empower equity and inclusion of indigenous worldviews in the way the technology is used?
what might be the potential impact of such AI models on the concepts of Ecosystem Services and Services To Ecosystems? Comberti et al. (2015). Ecosystem services or services to ecosystems? Valuing cultivation and reciprocal relationships between humans and ecosystems. Global Environmental Change, 34, 247-262. https://www.sciencedirect.com/science/article/pii/S0959378015300145
I edited the post by checking the 'publish' option and I hope I've made it discoverable now, however, I still don't see it on the main Puzzles page.
// AI has consequences much more far-reaching and multi-dimensional than many other technologies. How do we even start to think about the impact of AI on the Environment?
-Hmm, to me it seems we're talking about the same Human impact on the Environment (because Humans create AI to improve lives for themselves). So from here, I invite to zoom out, and add a puzzle on top of this about questioning Human perspective on life in general. When humans see themselves as the centre of the Earth (and probably, the Universe), we create technologies to improve life for ourselves only, e.g. we design say AI to have all knowledge at the click of a button without realising that training AI creates pollution. And then in the design process we further zoom in and divide humans into different stakeholders (like, users, funders, doers, etc) instead of zooming out and having the planet Earth as a stakeholder for example.
So, zooming out here as a thought exercise:
How would the Internet (and any web tech) be designed if we see humans only a specie as part of the living network on Earth and in the Universe? Could we imagine that the planet Earth itself has needs and not only humans do? Could we map out species from the environment as stakeholders in the design process of technologies?
What we could do even here on o2oo: when a new idea is described (before it goes into production), it could share an estimate of the pollution it will create and estimate the impact/value it will produce.
Let's take o2oo system as an example. So by running our website we probably pollute too ([Mindey], maybe you could help me think through this? :), and then we promise to get the humanity connected, so that all new ideas are discussed before they go into production. A promised value (with all stakeholders in mind) would be - that if an idea/project is discussed early, then it can be adjusted, while existing industries are so complex, and are not flexible anymore..
So I wonder how about estimating waste and value/impact for ideas and projects?
Another question is about ethics: Some ideas will promise to create a greater knowledge, even with an expense of creating pollution. What then? How could pollution transform into no-waste (and have a circular model of zero waste)? Say, perhaps when in the ideation process a CO2 emission is projected, then a part of that idea should involve solving that problem in general.
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Thanks for your response [Ruta]! I agree, I think this idea of enironmental justice pushes us to imagine a future where Nature is a stakeholder.
I think it is important to differentiate between AI and technology in general. In my experience, often times, the engineering of an AI model start from the idea of full automation because an AI system can do what people do - faster, cheaper, and better than humans. I think this is why it becomes important to consider the cnsequences as automating away the human introduces new kinds of challenges.
I think it'd be easier to start with the impact assessment of AI models which directly interact with environmental systems as then we could more directly measure the potential impact. Does that make sense?
// For example, training a version of Google’s language model (BERT) which underpins the company’s search engine, produced 1,438 pounds of CO2 equivalent in Strubell’s estimate to nearly the same as a round-trip flight between New York City and San Francisco. In practice, a single model is trained a large number of times before it's put in production.
[bobi.rakova], a few more thoughts:
1,438 pounds (=652 kg) of CO2 for Google language model? Training AI models from big data is expensive, but trained once, a model can be reused very cheaply large number of times. Google may re-train the model frequently, so it depends on the frequency, which your comment doesn't mention. Also, it doesn't mention how they are training this model -- if they have something pre-trained already, and training only the top layer, the statistic of expensive entire training would misrepresent the reality of relatively cheap top layer training (transfer learning) on top of the base model. Suggestion: It would be good to mention the total amounts.
In general, because it's expensive to train, but cheap to use, it would be great to have large public model zoo, or model market, so as to prevent people re-training expensive models. I wonder, what's the largest public model zoo that humanity has. Having something like "GitHub" for AI models would also be very liberating to people who have no hardware on which to train large models, but still want to benefit from AI models. I think I had this as a separate idea somewhere.