A Digital and Green Transition Series: Will Artificial Intelligence Foster or Hamper the Green New Deal?

By Dr. Theodora Dryer

Dr. Theodora Dryer, testified before the European Parliament on February 3rd, 2021, covering the complex relationship between AI, climate policy, and possibilities for a just and liveable future. This is in response to two of the European Commission’s key priorities for the upcoming years to “accelerate innovation and digitalisation” while at the same time “reaching climate neutrality and high environmental standards.”

The European Commission posed the overarching question: How can AI be deployed to benefit society, advance research, and accelerate our climate transition without its impact hampering our environmental goals? I argue that before exploring the possibilities of AI and the environment, we should clarify what is meant by AI, which frameworks are used to assess AI, and who primarily experience the harms of AI and climate change. Here I share the key takeaways and open questions that emerged from my testimony:

We must ask: Who is developing this technology, for whom and for what, and with what data? Who profits from this technology? And then as a corollary: Who holds sovereignty and ownership rights to the data, and what is at stake, and for whom, in the resulting applications?
Artificial intelligence developed in the name of benefiting the environment is not the same thing as establishing environmentally and socially conscious AI systems. It is therefore imperative to center Justice and sovereignty frameworks, rather than economic growth frameworks, in assessments of AI and environmental policy.
By clarifying the terms — of what, exactly, is meant by artificial intelligence — it is evident that promises made that AI will ‘solve’ the climate crisis are in direct opposition to the role AI plays in perpetuating climate injustice.
Artificial Intelligence or AI systems are inherently extractive from and detrimental to the environment. The popular notion of cloud computing as existing in virtual space beyond lived environmental and labor conditions is false. AI is a technological enterprise that is totally energy dependent, and that requires vast amounts of natural resources.
While some environmental benchmarks (e.g., for carbon emissions) can serve as meaningful guidelines, they are often vaguely stated and abstracted from real political and environmental contexts. Companies often use these benchmarks to pose themselves as thriving in one singular domain (e.g., carbon), while continuing with economic growth agendas that harm the environment in many other ways (e.g., pursuing lucrative contracts with oil and gas companies).
A necessary condition for AI to bring more environmental and social benefits than harm would be centering a Climate and Data Justice framework in assessments that are led by the interests and needs of Black, Indigenous, and Peoples of Color and those generally at the frontlines of the climate crisis. As discussed in my remarks below, there are large networks, collectives, coalitions, and movements working at this nexus whose work and knowledge is critical to these aims

Bio: Dr. Theodora Dryer is a historian of computing and technology and STS scholar. She is a research assistant professor at New York University and leads Climate + Water research at the AI Now Institute. She has worked for the past decade on information and algorithmic decision systems as they relate to environmental and economic power. She specializes in weather and climate modeling, water policy, and agriculture.

The original questions offered by the European Commission appear as section headers, organizing my extended written testimony printed in full below.³

{European Parliament Question 1: What are the Possibilities that AI offers for the Environment?}

Before interrogating the relationship between artificial intelligence (AI) and the environment, we should clarify what is meant by AI {The Terms}, which frameworks are used to assess AI {Frameworks}, and who primarily experiences the harms of AI and climate change {For Whom}.

{The Terms}

Artificial intelligence developed in the name of benefiting the environment is not the same thing as establishing environmentally and socially conscious AI systems. It is therefore imperative to center Justice and sovereignty frameworks, rather than economic growth frameworks, in assessments of AI and environmental policy.

Artificial intelligence or AI is a powerful terminology advanced by technological developers that can refer to a broad suite of technologies, data collection and storage infrastructures, and automated decision systems. It often describes predictive statistics-based digital systems such as machine learning. Ascertaining whether or not these technological systems can improve on the climate crisis foremost requires asking precise and situated questions. Assessing the appropriateness of applying AI solutions to environmental issues would involve identifying locally situated technologies and data, examining the environments they extract data from, and understanding AI-backed decision-making procedures. It is crucial to understand how data practices and decision-making procedures impact environmental policy and resource access.

Situating AI in local contexts involves framing questions in the vein of: Who is developing this technology, for whom and for what, and with what data? Who profits from this technology? And then as a corollary: Who holds sovereignty and ownership rights to the data, and what is at stake, and for whom, in the resulting applications? Broadly stated: does this technology reinforce an extractive economic system or do these systems contribute to possibilities for environmental and climatic justice?

Activists and researchers have shown time and again that centering ‘economic growth,’ ‘technological progress,’ ‘optimization,’ and ‘efficiency’ in the development and applications of technological systems is incommensurable with possibilities for climatic and digital justice.⁴ Many have called for postcolonial, decolonial, Indigenous, and anti-capitalist approaches to environmental tech policy as these frameworks provide realistic and justice-centered alternatives to the aforementioned frameworks and cultural commitments — e.g. optimization⁵ — of extractive economic systems.⁶

By clarifying the terms⁷ — of what, exactly, is meant by artificial intelligence — it is evident that promises made that AI will ‘solve’ the climate crisis are in direct opposition to the role AI plays in perpetuating climate injustice. However, hopeful marketing and positivist policy proposals continue to promise AI as a solution in the domains of carbon tracking, agricultural production and control, weather modification, and much more.⁸ For example, Microsoft recently launched a “$1 billion climate innovation fund” to achieve carbon-negativity by 2030.⁹ Concurrent with the promise of carbon-negativity, Microsoft also recently received a $22 billion contract to supply the military with virtual reality (VR) technology.¹⁰ Military production has always been intrinsically murderous to the environment¹¹ and human life¹², and provides the human and technological infrastructures that police access to resources and safety for the populations at the frontline of the climate crisis.¹³

While some environmental benchmarks can serve as meaningful guidelines for assessment during ecological transition, they are often vaguely stated and abstracted from real political and environmental contexts. Companies — like Microsoft — often use these benchmarks to pose themselves as thriving in one singular domain (e.g., carbon), while continuing with economic growth agendas that harm the environment in many other ways (e.g., contracts with oil and gas companies, as well as the military). These statistical benchmarks alone are not adequate for determining a sound environmental justice agenda. And the reality under climate change is that we don’t have 30 years to experiment with the efficacy of these benchmarks.

{Frameworks}

I warn against utilizing frameworks of ‘progress and profit,’ ‘technological solutionism,’ and ‘sustainable development’ that have historically been used by technological developers and extractive economic interests to profit off land and people, and under the premises of climate crisis and resource scarcity.

Assessments of the potential uses of digital technology to foster Green New Deal policy should center frameworks of climate and environmental justice and digital and data justice.¹⁴ These two domains — environmental justice and digital justice — constitute decades long political and social movements and fields of research study. Taken together, they provide us with operable frameworks with which to assess the known impacts of AI technologies as they intersect with environmental and climate policy. Consideration of digital technology as it connects with environmental justice is not an arbitrary conjunction, these two domains are deeply historically linked.¹⁵

Representatives, policy makers, and researchers who are interested in understanding the nexus between digital technology and the environment are therefore tasked with learning the intersections of these two movements and engaging their locally-situated contexts.¹⁶ In this vein, I propose to reframe this question as: Is it possible for the amorphous technological powers of artificial intelligence (AI) to help realize climatic and environmental justice? And: How should we engage the nexus between climate and environmental justice and digital and data justice?

{For Whom?}

In recent decades, activists, lawyers, scholars, and advocacy groups have proven, with consensus, that the same communities at the frontlines of the climate crisis are the same communities most adversely impacted by the development of predictive technologies.¹⁷ In elucidating these realities, a coalition working on global climate justice recently described climate change as an unequally mitigated human-caused crisis:¹⁸

Climate justice perspectives center the fact that the brunt of climate change falls hardest on the most poor and marginal peoples — peoples often trampled by the twin ravages of colonialism and capitalism, who demonstrate resilience despite these depredations. The rampant extraction of resources by imperial powers in colonized lands — and subsequently by local predator elites — left the lands in a state of continuing impoverishment, and with depleted levels of physical and economic resources that make it daunting, if not almost impossible, to withstand the humanitarian and environmental crises caused by climate change.

Given the realities of the climate crisis, the frameworks guiding the terms of engagement and assessment should come from the work and voices of the people most adversely impacted and who are most knowledgeable about digital and environmental injustice.¹⁹

{European Parliament Question 2: What is the Impact AI has on the Environment?}

Artificial Intelligence or AI systems are inherently extractive from and detrimental to the environment. The popular notion of cloud computing as existing in virtual space beyond lived environmental and labor conditions is false. AI is a technological enterprise that is totally energy dependent, and that requires vast amounts of natural resources.²⁰ AI infrastructures utilize vast quantities of anthracite coal and oil materials used to fuel computing and transportation infrastructures.²¹

Digital systems also require vast amounts of water for cooling. In 2019, Google requested more than 2.3 billion gallons of water for data centers across three different U.S. states.²² After cooling usage, water becomes a repository for electronic waste and derivative toxins, making toxicity a permanent feature of surrounding systems and ecologies.²³ Google and other companies are continually expanding, meaning that their consumption of local groundwater will continue to expand with devastating and obfuscated impacts on local communities and ecosystems.

The broader systems of artificial intelligence have generated waste and toxins that have permanently altered the global landscape. These include “silicon” development in the U.S. California region²⁴ — a chemical element used in machine hardware, and namesake to dominant conceptions of Silicon Valley — as well as electronic waste (‘e-waste’) landfills like the one that surrounds Korle Lagoon, in Agbogbloshie Ghana.²⁵ Waste generated by big tech has and will continue to alter ecological and economic systems around the world, entrenching environmental racism and toxic colonialism.²⁶

As emblemized by the e-waste landfills in Agbogbloshie, the global South is carrying the brunt of the environmental impacts of AI. A recent study on the Gulf of Guinea highlights how Western waste-brokers continue to unsafely dispose of toxic e-waste in global South countries despite the existence of prohibitive laws.²⁷ Western waste-brokers’ economic and financial powers override local regulations and enforcement apparatus in places like Côte d’Ivoire, Nigeria and Ghana.

AI systems alter economic and ecological contexts in ways that directly impact the health of local communities and hinder workers’ rights.²⁸ In the greater Silicon Valley region, big tech companies including Google, Facebook, Apple, and Amazon have significantly transformed the urban and human environments of Santa Clara, San Jose, Alameda, and San Mateo counties all of which are on Ohlone land. Many activists, scholars, and lawyers have shown that the presence of these companies has had deleterious and deadly impacts on people from local communities’ health and their access to needed resources including healthcare, equitable pay, housing,²⁹ and other survival resources.³⁰

Finally, AI — which is energy dependent itself — is increasingly being used in natural resource extraction and energy production (e.g., for coal, oil, and uranium). With increasing fervor, predictive statistical systems and computer vision technologies are being developed to expand energy mining and oil drilling.³¹ This creates a destructive self-reinforcing feedback loop: mining itself has proved, across centuries, to directly contribute to toxic environments for local communities and produce inequitable impacts.³² Yet this is rarely discussed, even as my colleagues Meredith Whittaker and Roel Dobbe point out that “big AI companies are aggressively marketing their (carbon intensive) AI services to oil & gas companies, offering to help optimize and accelerate oil production and resource extraction.”³³

{European Parliament Question 3: Data is often described as key in the “AI race” and is currently fragmented, missing, and not openly available. Could you elaborate on the data governance and access conditions necessary for a sustainable AI to emerge?}

Data is never apolitical: the availability of and access to data are linked to larger systems of governance and power, and the production of data itself is often linked to extractive environmental policies.

More than a singular technology, we can think of AI as a data-driven and natural resource dependent technological infrastructure that people in power use to govern resource allocation. As such, AI depends on the mass production, extraction, and processing of environmental, climatic, and natural resource information. This data is not magically generated in a cloud. Rather, this data has a history that inextricably encodes the interests and perspectives of the actors extracting the data, and there are significant political and policy contexts behind its production and uses, especially when dealing with environmental and natural resource information.

{Data + Water Rights}

A critically important component of the Indigenous Environmental Justice movement pertains to the right to information and data that belongs to Indigenous communities, data that is critical for decision making and for informing these communities’ water and land ownership rights.³⁴ It matters who creates the data, who owns the data, and who holds power in designing data analysis systems.

In my research, especially on the uses of digital automated decision systems in water allocation and distribution policies in the U.S. southwest, it is clear that data cannot be separated from the natural resource policies and legal apparatus that it functions within. Algorithmic systems function within larger ‘computing landscapes’ that encompass particular power structures, environmental conditions, and policy contexts.³⁵ Often, these political and environmental contexts are hidden by the technical function and opacity of digital systems.

For example, I have traced algorithmic-driven water development projects in the U.S. southwest dating back a century and have uncovered the explicit ways in which algorithmic frameworks contribute to the settler colonial function and environmental racism of water policy in the region. This is to say that the disavowal of Native American water rights is literally encoded in the technical function of U.S. state-run automated decision systems, many of which grew out of resource capture and allocation projects.

Unsurprisingly, in the U.S. southwest, water and climatic data is produced and monopolized by the neocolonial state and federal institutions, especially the Department of Interior. And many of these institutions first ballooned in power during the original New Deal era. Policy and technology function together in shaping the landscapes of data availability, and shaping what questions data answers, and whose interests are served in the course of its being processed through automated decision systems.

{Data + Carbon Tracing}

Questions regarding carbon tracing data are complex and variegated. In terms of the core benchmarks of the Green New Deal — to achieve carbon neutrality and cut down on emissions — this requires a particular ambition to track and map the relevant information. Decisions as to whether to use AI in this endeavor should follow after a process of reflexivity on the political and technical frameworks that define the concept of carbon neutrality, ensuring that these concepts are anchored in the lived reality of the populations most at risk of harm from climate crisis.

Historically, emissions data has been collected for assessing shared and individual responsibility for climate impact across nation states following major establishments such as the Intergovernmental Panel on Climate Change (IPCC) in 1989 and the Kyoto Protocol following that. The mapping, tracking, and data production involved in producing these emissions numbers and information is complicated by much deeper geopolitical issues, and cannot be accepted at face value as an indicator of climate impact or accountability for that impact.

For example, the United States has produced and continues to produce more than its fair share of carbon emissions. And significantly, the U.S. has monopolized the policy frameworks for international agreement and indeed the benchmarks by which climate impact is assessed. This is to say that the data production required for emissions and carbon tracking, and to track the progress of these benchmarks, is itself not a politically neutral endeavor.

{European Parliament Question 4: Relation between AI and “Sustainability”: Why should we not only focus on climate change, but also on the broader ecosystem, such as water and agriculture?}

I have two thoughts on this, the first pertaining to sustainability and the second to water and agriculture. With each of these, I believe it is imperative to center Justice frameworks in considering assessment and impact statements pertaining to the role of digital technology in the Green New Deal.

“Sustainability” is a term with a long history and in the current policy discourses: it holds statistical meanings and functions as a benchmark in data analysis. In recent years, there have been a number of studies conducted on the “role of artificial intelligence in achieving Sustainable Development Goals.³⁶ There is danger in these types of usages that abstract sustainability — as a statistical measure — from consideration of the historical and environmental contexts behind its development and use. This use of ‘sustainability’ is similar to the use of ‘statistical bias’ in algorithmic impact statements that neglect consideration of larger political and social contexts of automated decision systems and the social beliefs baked into these measures.³⁷

Water and agriculture have been central to my work over the past decade, and my research shows just how intricately and historically linked climate injustice and tech development are. Since the eighteenth century, water management and agricultural development have been the central laboratories for the development of data-driven governance on the global stage. Technological advancements in computing and data management have always directly impacted local and Indigenous communities, immigration and agricultural labor, possibilities for food justice, and access to clean water and natural resources.

Over the last decade alone there has been rapid growth in the amorphously defined field of artificial intelligence in these two domains, employing variegated technologies in computer vision, machine learning, and automated decisions systems. While AI proponents in these domains claim that the technology aids in fulfilling sustainability goals, their systems remain unexamined in their impact on food and energy access and rights, ecological systems, and agricultural workers.³⁸

{European Parliament Question 5: How can AI help accelerate the ecological transition and help achieve our green objectives? What is the condition for AI to bring more environmental and social benefits than harm?}

The Green New Deal benchmarks and ambitions are not negotiable, they are a matter of survival. However, it is important to be cautious in how we frame ecological transitions and climate futures in shaping this policy.³⁹ Climate Justice groups are currently confronting “transition” itself as the site for engaging necessary change. The London Mining Network and War on Want coalition, for example, make a compelling and necessary argument that:⁴⁰

Mining corporations are aggressively and cynically marketing their destructive activity as a solution to the climate emergency. It’s critical that we stop extractive industries from greenwashing their crimes and capturing the narrative around the transition to renewable technologies. The climate movement must listen to and learn from frontline communities pushing back the expansion of the extractive economy: communities who are simultaneously advancing solutions that embody social, ecological and climate justice.

Furthermore, the notion of “acceleration” used in the ecological transition to achieve Green New Deal benchmarks through the development and expansion of digital technology is a dangerous framing.⁴¹ Under the urgency of acceleration and rapid economic growth, there is deep historical precedent of deferring to profiteers, technological developers, and private capitalists, and making decisions without equitable and considered consultation. This has supported these actors in going after weather, climate, and natural resources as a site for development in ways that are ultimately destructive to the environment and that perpetuate environmental racism and social inequities.

A necessary condition for AI to bring more environmental and social benefits than harm would be centering a Climate and Data Justice framework in assessments that are led by the interests and needs of Black, Indigenous, and Peoples of Color and those generally at the frontlines of the climate crisis. As discussed in these remarks, there are large networks, collectives, coalitions, and movements working at this nexus whose work and knowledge is critical to these aims.

Endnotes:

Toni Morrison, Charlotte Mecklenburg Library Novello Festival, November 20, 1996: “You know, they straightened out the Mississippi River in places, to make room for houses and livable acreage. Occasionally the river floods these places. “Floods” is the word they use, but in fact it is not flooding; it is remembering. Remembering where it used to be. All water has a perfect memory and is forever trying to get back to where it was.”
Cite as: Dryer, Theodora. February 3rd, 2021. Testimony before the European Parliament Greens/EFA Group. A Digital and Green Transition Series: Will Artificial Intelligence Foster or Hamper the Green New Deal? Republished on Medium by the AI Now Institute at New York University.
Thank you to MEP Kim Van Sparrentak and MEP David Cormand for their thoughtful interventions. Thank you to Fieke Jansen and Chris Adams for their engagement. And thank you Meredith Whittaker, Alejandro Calcaño, and Luke Strathmann for their editorial insight.
In April 2021, The Red Nation will be releasing a powerful new book on Indigneous action to save the earth. See: The Red Nation, The Red Deal: Indigenous Action to Save Our Earth (Common Notions, 2021); Indigenous Environmental Network, Just Transition, https://www.ienearth.org/justtransition/, “New forms of federal energy legislation with false incentives are designed to encourage the expansion of extraction and industrial-scale development on and near our Indigenous lands and territories by outside corporate interests. Once again, the dominant system is putting economics first, over our indigenous values, duties and responsibilities to protect the environment, ecosystems, and sacred and historical and cultural areas and the water of life.”
On optimization, see: Theodora Dryer, Settler Computing, “I thread together a genealogy of western water policy and Native American water rights in the Colorado River Basin region as they relate to the growing power of optimization algorithms that galvanized in the 1950s through 1980s period. By linking the algorithmic process in water management to the place of the U.S. southwest, we see that algorithmic computing is not confined to computer science laboratories. Rather, it is a system of economic and environmental resource governance that upholds settler power structures through its adhesions to legal frameworks.”
See, for example: Lina Alvarez & Brendan Coolsaet, “Decolonizing Environmental Justice Studies: A Latin American Perspective,” Capitalism Nature Socialism 31, no. 2 (2020): 50–69.
Audre Lorde summoned in her 1985 work, A Burst of Light: “We must observe the implications of our lives […] the personal is political and we can subject everything in our lives to scrutiny. We have been nurtured in a sick, abnormal society, and we should be about the process of reclaiming ourselves as well as the terms of that society,” Audre Lorde, A Burst of Light and Other Essays (Ixia Press, 1988).
Jonathan Shieber, “CarbonChain is using AI to determine the emissions profile of the world’s biggest polluters,” Tech Crunh, August 21, 2020, https://techcrunch.com/2020/08/21/carbonchain-is-using-ai-to-determine-the-emissions-profile-of-the-worlds-biggest-polluters/’; Louis Columbus, “10 Ways AI Has The Potential To Improve Agriculture In 2021,” Forbes February 17, 2021, https://www.forbes.com/sites/louiscolumbus/2021/02/17/10-ways-ai-has-the-potential-to-improve-agriculture-in-2021/?sh=6b92e987f3b1.
Brad Smith, “Microsoft will be carbon negative by 2030,” Microsoft, Jan. 16, 2020, https://blogs.microsoft.com/blog/2020/01/16/microsoft-will-be-carbon-negative-by-2030/.
Lucas Matney, “Microsoft gets contract worth up to $22 billion to outfit U.S. Army with 120,000 AR headsets,” https://techcrunch.com/2021/03/31/microsoft-wins-contract-worth-up-to-22-billion-to-outfit-u-s-army-with-120000-ar-headsets/.
See, for example: Susan D. Lanier-Graham, The Ecology of War: Environmental Impacts of Weaponry and Warfare; Davorn Sisavath, “The US Secret War in Laos: Constructing an Archive from Military Waste,” Radical History Review 133 (2019). Beyond the environmental impacts of military bombing and destruction, resource extraction for military development has created technological systems that perpetuate environmental racism and genocide. See, for example: Valerie Rangel, Environmental Justice in New Mexico: Counting Coup (Charleston: The History Press, 2019), “Sometime in our recent history, the decision was made to compromise human lives and the environment; to make ecological sacrifices for a more industrialized, capitalistic world. Population growth and new technological advancements shifted the dominant society to one that is powered by an unquenched thirst for fuel, food, and material possessions. Suddenly, land use planning and resource management were at the forefront of decision making and key to political control.” See also: Dana E. Powell, Landscapes of Power: Politics of Energy in the Navajo Nation (Durham and London: Duke University Press, 2018).
I define military “destruction data” as information generated from 10,000 feet above the earth through processes of mass destruction and land alternation that coincides with the visualization of bombing waste and destruction as predictable, controllable, and calculable sites of statistical study. Destruction data is a major historical feature of predictive statistics. See: Theodora Dryer, Designing Certainty: The Rise of Algorithmic Computing in an Age of Anxiety, 1920–1970 (PhD Diss., University of California, San Diego, 2019).
Naomi Klein, “How Big Tech Helps India Target Climate Activists,” The Guardian March 4, 2021, https://www.theguardian.com/news/2021/mar/04/how-big-tech-helps-india-target-climate-activists-naomi-klein.
Much of the current policy and regulatory discourse on the impact and uses of artificial intelligence in the climate crisis does not include or center a justice framework. This is alarming given the realities and abundant information pertaining to both the climatic and environmental justice movements and digital and data justice work. The environmental justice movement is a decades-long global policy, science and data, and justice initiative. It is widely understood to originate in the U.S. context popularized by scholar Robert D. Bullard who investigated environmental racism in the segregated U.S. south. The environmental justice and climate justice movements, as framed in dominant government description and western contexts, have since been rigorously revised and extended to account for the contexts of the global South, coloniality, and Indigenous sovereignty. See for example: Robert D. Bullard, Dumping in Dixie: Race, Class, and Environmental Quality (New York: Routledge, 1990); Lexington Wilks, “The Global South and the Burden of Environmental Racism’s Past and Future,” ColorBloq https://www.colorbloq.org/the-global-south-and-the-burden-of-environmental-racisms-past-and-future; Indigenous Environmental Justice, eds. Karen Jarratt-Snider and Marianne O. Nielsen (Tucson: The University of Arizona Press, 2020). The historic and present-day digital and data justice movements hold a similarly rich corpus of information and policy frameworks produced by activists and researchers who have for decades interrogated the way digital technologies, data systems, and their related policies and regulatory frameworks uphold social inequality and deny people’s access to natural resources and freedom from oppression. In the U.S. context, organizations are leading work on this including Data 4 Black Lives, who commit to “using the datafication of our society to make bold demands for racial justice.” See: Yeshimabeit Milner, “For Black people, Minneapolis is a metaphor for our world,” Medium May 29. Additional organizations include the Indigenous Data Sovereignty Network, IDA B. WELLS Just Data Lab, the Algorithmic Justice League, Our Data Bodies, The Anti-Eviction Mapping Project, and more.
In my historical research on automated water management in the U.S. southwest, it is evident that there is a deep entrenchment between data extraction, water policy, and technological development that reaches back centuries. See: Theodora Dryer, “Settler Computing: Water Algorithms and the Doctrine of Prior Appropriation in the U.S. Southwest,” Preprints. Environmental injustice and data injustice are deeply historically linked and require assessment as two sides of the same historical programs of settler colonialism and natural resource dispossession.
Research and work at the nexus between these two domains are currently gaining momentum. The Environmental Data & Governance Initiative has defined Environmental Data Justice (EDJ), https://envirodatagov.org/environmental-data-justice/. The Civic Laboratory for Environmental Action Research (CLEAR) interrogates these intersections in marine science, https://civiclaboratory. The Indigenous Data Sovereignty Network is addressing the need for tribes to drive their data agendas. https://nni.arizona.edu/programs-projects/policy-analysis-research/indigenous-data-sovereignty-and-governance.
E.g., see: Tech Workers Coalition, https://techworkerscoalition.org/climate-strike/
Climate Futures: Reimagining Global Climate Justice, Eds. Kum-Kum Bhavani, John Foran, Priya A. Kurian, and Debashish Munshi (Zed Books, 2019): 3.
See: Hi’ilei Julia Hobart, “Atomic Histories and Elemental Futures across Indigenous Waters,” Disaster Media, April 08, 2021, https://mediaenviron.org/article/21536-atomic-histories-and-elemental-futures-across-indigenous-waters.
Christine Hall, “Cooling the World’s Largest ARM Supercomputer,” Data Knowledge Center, June 25th, 2018, https://www.datacenterknowledge.com/supercomputers/cooling-worlds-largest-arm-supercomputer. Resource extraction and water monopolization contribute to situational processes of environmental racism, settler colonialism, and coloniality. See: Nick Estes, “Water is Life: Nick Estes on Indigenous Technologies,” Logic: NATURE, 9 (December 07, 2019) https://logicmag.io/nature/water-is-life-nick-estes-on-indigenous-technologies/.
Rachel Bergmann and Sonja Solomun. 2021. “Diesel Death Zones in the Amazon Empire: Environmental Justice in Algorithmically Mediate Work.” Forthcoming in This Seems to Work: Designing Technological Systems with The Algorithmic Imaginations of Those Who Labor. CHI ’21: May 8–13, 2021, Yokohama, Japan. ACM. New York, NY, USA.
Nikitha Sattiraju, “Google Data Centers’ Secret Cost: Billions of Gallons of Water,” April 1, 2020 Bloomberg Green. https://www.bloomberg.com/news/features/2020-04-01/how-much-water-do-google-data-centers-use-billions-of-gallons.
Max Liberion, M. Tironi, and N. Calvillo, “Toxic Politics: Acting in a Permanently Polluted World,” Social Studies of Science 48, no. 3 (2018): 331–349.
David Naguib Pellow and Lisa Sun-Hee Park, The Silicon Valley of Dreams: Environmental Injustice, Immigrant Workers, and the High-Tech Global Economy (New York: New York University Press, 2020).
Samuel Armoo, “Korle Lagoon: a “regatta” of dead computers,” Environment Care, October 24, 2019, https://www.environmentcareconsortium.org/blog/2019/10/24/korle-lagoon-a-regatta-ground-for-dead-computers-and-electronic-waste-from-the-agbobloshie-e-waste-site.
Grace A. Akese and Peter C. Little, “Electronic Waste and the Environmental Justice Challenge in Agbogbloshie,” Environmental Justice 11, no. 2 (2018).
Ifesinachi Okafor-Yarwood and Ibukun Jacob Adewumi, “Toxic waste dumping in the Global South as a form of environmental racism: Evidence from the Gulf of Guinea,” African Studies 79, no.3 (2020): 285–304.
Nantina Vgontzas, “A New Industrial Working Class? Challenges in Disrupting Amazon’s Fulfillment Process in Germany,” in The Cost of Free Shipping: Amazon in the Global Economy (Pluto Press, 2020); Nantina Vgontzas, “Amazon after Bessemer,” Boston Review, April 21, 2021, http://bostonreview.net/class-inequality/nantina-vgontzas-amazon-after-bessemer.
Erin McElroy, “Data, dispossession, and Facebook: techno-imperialism and toponymy in gentrifying San Francisco,” Urban Geography (2019).
On the term, “survival resources,” see: Kimberlé Crenshaw, “Under the Blacklight: The Intersectional Failures that CoVid Lays Bare,” on “the dramatic maldistribution of survival resources,” https://www.youtube.com/watch?v=OsBstnmBTaI.
For Example: Jon Walker, “AI in Mining — Mineral Exploration, Autonomous Drills, and More,” EMERGJ: The AI Research and Advisory Company February 2, 2019, https://emerj.com/ai-sector-overviews/ai-in-mining-mineral-exploration-autonomous-drills/.
Joan Martinez-Alier, “Mining conflicts, environmental justice, and valuation,” Journal of Hazardous Materials 86, no. 1–13 (2001): 153–170.
Roel Dobbe and Meredith Whittaker, “AI and Climate Change: How they’re connected, and what we can do about it,” AI Now Institute, (2019): Retrieved from https://medium.com/@AINowInstitute/ai-and-climate-change-how-theyre-connected-and-what-we-can-do-about-it-6aa8d0f5b32c
Indigenous Data Sovereignty: Toward an Agenda, eds. Tahu Kukutai and John Taylor (ANU Press, 2016).
I define “computing landscape” as the geographic area of land and the historical and political contexts impacted by computing work. See: Designing Certainty.
For example: Ricardo Vinuesa et. al., “The Role of Artificial Intelligence in Achieving the Sustainable Development Goals,” Nature Communications 11, no. 233 (2020).
Joy Buolamwini, “Unmasking Bias,” Medium, December 14, 2016; “Our individual encounters with bias embedded into coded systems — a phenomenon I call the ‘coded gaze’ — are only shadows of persistent problems with inclusion in tech and machine learning.”; Ruha Benjamin, “Catching our Breath: Critical Race STS and the Carceral Imagination,” Engaging Science, Technology, and Society 2 (2016): 145–156.
Aditi Surie and Lakshmee V. Sharma, “Climate Change, Agrarian Distress, and the Role of Digital Labour Markets: Evidence from Bengaluru, Karnataka,” Decision 46 (2019): 127–138.
Cathy Gere, “The Drama of the Commons: A new script for the Green New Deal,” The Point 22, June 12, 2020, https://thepointmag.com/politics/the-drama-of-the-commons/.
War on Want and London Mining Network, “A Just(ice) Transition is a Post-Extractivist Transition: Centering the Extractive Frontier in Climate Justice,” (2019).
Scholar Kyle Whyte describes how dominant scientific systems that track climate change assume a linear framework of time. And “linear measures of time have the capacity to generate a sense of imperilment and urgency” that leads to “governments and corporations [taking] swift actions without genuine benefit and sharing with and consent by Indigenous peoples.” See: Kyle Whyte, “Time as Kinship,” forthcoming in The Cambridge Companion to Environmental Humanities, eds. Jeffrey Cohen and Stephanie Foote (Cambridge University Press, 2021).

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A Digital and Green Transition Series: Will Artificial Intelligence Foster or Hamper the Green New Deal?