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/
歡迎，[bobi.rakova]，很高興您能來到這裏，感謝您分享質疑 AI 與環境交集的問題！
// 與許多其他技術相比，人工智能的影響更爲深遠和多維。我們如何開始考慮人工智能對環境的影響？
事實上，簡單地搜索結果的排名將對我們的日常決策產生非線性影響：決策者，企業，由於這些決策，他們將做出不同的選擇。行車路線導航器選擇了哪些路徑，或者返回了哪些醫療程序，可能會帶來截然不同的結果。想法：因此，也許搜索引擎應該開發一個可選的“安全搜索”結果，這些結果與財務或醫療建議具有相同的責任。
您正確地提到了訓練模型的碳足跡，但我不確定它與開採加密貨幣的碳足跡有多大不同。建議：最好添加一些數字來證實這一說法。
然而，人工智能與環境正義運動和自然權利運動有什麼關係？它們與你提到的三個問題有什麼聯繫？問題：您能否詳細說明數據所有權和數據治理如何影響氣候治理？現狀是什麼，應該如何？
附註由於您在選擇“靜默”或“草稿”之前保存了主題，因此，我們收到了有關它的通知，雖然它目前無法在索引中發現，但仍可通過直接 URL 訪問。因此，希望我們的評論有助於改進您的寫作。
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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感謝您的周到回覆[Mindey]！
與 AI 的碳足跡相關，我所知道的最佳參考資料是：Strubell, E.、Ganesh, A. 和 McCallum, A.（2019 年）。 NLP 深度學習的能源和政策考慮。 https://arxiv.org/abs/1906.02243
例如，訓練一個支持該公司搜索引擎的谷歌語言模型 (BERT) 的版本，產生了 1,438 磅的二氧化碳當量，據 Strubell 估計，與紐約市和舊金山之間的往返航班幾乎相同。在實踐中，單個模型在投入生產之前要經過大量訓練。
我應該澄清一下，我指的不是一般意義上的 AI，而是指在開發環境解決方案時使用機器學習。與此相關的一篇很棒的概述論文是：Rolnick, D., et al。 (2019)。通過機器學習應對氣候變化。 https://arxiv.org/pdf/1906.05433.pdf 在此處查看它的交互式版本：https://www.climatechange.ai/summaries
例如，人工智能系統已被用於通過使用衛星和無人機圖像數據來預測森林固碳潛力；計算機視覺算法用於確定合適的種植地點、監測植物健康和分析趨勢；人工智能還用於識別可能非法砍伐森林的地方，以及評估因火災、疾病、昆蟲或其他原因造成的風險。 Rolnick 等人的論文中提供了所有這些用途的參考資料。
我對環境 AI 模型中的數據治理模型感興趣。例如，在用於森林恢復項目的人工智能的情況下：
哪些數據所有權和數據治理框架可以在使用技術的方式上增強土著世界觀的公平性和包容性？
此類 AI 模型對生態系統服務和生態系統服務概念的潛在影響是什麼？ Comberti 等人。 (2015)。生態系統服務還是生態系統服務？重視人類與生態系統之間的耕作和相互關係。全球環境變化，34, 247-262。 https://www.sciencedirect.com/science/article/pii/S0959378015300145
我通過選中“發佈”選項編輯了帖子，我希望我現在可以發現它，但是，我仍然沒有在主拼圖頁面上看到它。
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.