Crawford, Kate, Roel Dobbe, Theodora Dryer, Genevieve Fried, Ben Green, Elizabeth Kaziunas, Amba Kak, Varoon Mathur, Erin McElroy, Andrea Nill Sánchez, Deborah Raji, Joy Lisi Rankin, Rashida Richardson, Jason Schultz, Sarah Myers West, and Meredith Whittaker. AI Now 2019 Report . New York: AI Now Institute, 2019, .


2.4 AI and the Climate Crisis
On September 20, workers from 12 tech companies joined the global climate strike.369 They highlighted tech’s role in climate change and demanded “zero carbon emissions by 2030, zero contracts with fossil fuel companies, zero funding of climate denial lobbying or other efforts, and zero harm to climate refugees and frontline communities.”370

This might have surprised some people, as tech’s contribution to the climate crisis is rarely acknowledged. Indeed, industry marketing often highlights green policies, sustainability initiatives, and futures in which AI and other advanced technologies provide solutions to climate problems.

But the tech sector is a significant contributor to climate change and environmental harms.371

AI Makes Tech Dirtier
The tech industry faces criticism for the significant energy used to power its computing infrastructure. As a whole, the industry’s energy dependence is on an exponential trajectory, with best estimates showing that its 2020 global footprint amounts to 3.0–3.6 percent of global greenhouse emissions, more than double what the sector produced in 2007.372

This is comparable to that of the aviation industry,373 and larger than that of Japan, which is the fifth
biggest polluter in the world.374 In the worst-case scenario, this footprint could increase to 14 percent of global emissions by 2040.

In response, the major tech companies have made data centers more efficient, and have worked to ensure they’re powered at least in part by renewable energy—changes they’re not shy about, announcing them with marketing blasts and much public fanfare.375 These changes are a step in the right direction, but don’t come close to tackling the problem. Most large tech companies continue to rely heavily on fossil fuels, and when they do commit to efficiency goals, these are most often not open to public scrutiny and validation.376,377

The AI industry is a significant source of further growth in greenhouse emissions. With the emergence of 5G networks aiming to realize the “internet of things,” the increased acceleration of data collection and traffic is already underway.378 In addition to 5G antennas consuming far more energy than their 4G predecessors,379 the introduction of 5G is poised to fuel a proliferation of carbon-intensive AI technologies, including autonomous driving 380 and telerobotic surgery.381

A core contributor to the AI field’s growing carbon footprint is a dominant belief that “bigger is better.” In other words, AI models that leverage massive computational resources to consume larger training datasets are assumed to be inherently “better” and more accurate.382 While this narrative is inherently flawed,383 its assumptions drive the use of increased computation in the development of AI models across the industry.
Last year, researchers Dario Amodei and Danny Hernandez at OpenAI reported that “[s]ince 2012, the amount of [computation] used in the largest AI training runs has been increasing exponentially with a 3.4 month doubling time (by comparison, Moore’s Law had an 18 month doubling period).”384 Their observations show developers “repeatedly finding ways to use more chips in parallel, and . . . willing to pay the economic cost of doing so”.

As AI relies on more computers, its carbon footprint increases, with significant consequences. A recent study from the University of Massachusetts, Amherst estimated the carbon footprint of training a large natural-language processing model. Emma Strubell and her coauthors reported that training just one AI model produced 300,000 kilograms of carbon dioxide emissions.385 That’s roughly the equivalent of 125 round-trip flights from New York to Beijing.

AI and the Fossil Fuel Industry
Adding to their already sizeable climate impact, big AI companies are aggressively marketing their (carbon intensive) AI services to oil and gas companies, offering to help optimize and accelerate oil production and resource extraction. Amazon is luring potential customers in the oil and gas industry 386 with programs like “Predicting the Next Oil Field in Seconds with Machine Learning.”387 Microsoft held an event called “Empowering Oil & Gas with AI,”388 and Google Cloud has its own energy vertical dedicated to working with fossil fuel companies.389 And C3 IoT, an AI company originally created to facilitate the transition to a society fueled by renewable energy, now helps large oil and gas companies, including Royal Dutch Shell, Baker Hughes, and Engie, to expedite their extraction of fossil fuel.390 A recent article in Logic points out that oil and gas account for 30 percent of the total addressable market, making “the success of Big Oil, and the production of fossil fuels . . . key to winning the cloud race.”391 Recently, the Guardian examined the role of Big Tech in sustaining the market for fossil fuel, illuminating the massive amounts of money tech companies invest in organizations that actively campaign against climate legislation, and promote climate change denial.

Opacity and Obfuscation
When researchers and policymakers attempt to account for tech’s climate footprint, it is immediately clear how little information is available. They are left to rely on voluntary company disclosures, without access to the information they would need to make a thorough accounting of tech’s true energy use.

There is very little public data available, and few incentives for tech companies to release it. Without the information necessary to reach robust conclusions, researchers Lotfi Belkhir andAhmed Elmeligi had to estimate 2018 data-center energy consumption using data from 2008.393 It was all they had to work with, even though, over the past ten years, both the scale of computation and the technologies powering it have changed radically.The authors of Greenpeace’s report make similar observations, stating that while efficiency metrics have been eagerly adopted by the industry, “very few companies report under newer metrics . . . that could shed any light on the basic question: how much dirty energy is being used, and which companies are choosing clean energy to power the cloud?”394 More frustratingly, the unwillingness of cloud providers to provide customers with insight into the energy use of procured services forms a critical barrier to meaningful carbon accounting across all sectors and organizations that rely on digital technology.

For the full report please see the pdf below:


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