Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
Generative AI has a large carbon footprint and other drawbacks. But AI can help mitigate its own harms—by plowing through mountains of data on extreme weather and human displacement.
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.
Living with someone changes your microbiome, new research shows
For the first time, research has shown that bacteria of the microbiome are transmitted between many individuals, not just infants and their mothers, in ways that can’t be explained by having the same diet or geography.
Some roommate frustration can be expected, whether it’s a sink piled high with crusty dishes or crumbs where a clean tabletop should be. Now, research suggests a less familiar issue: person-to-person transmission of shared bacterial strains in our gut and oral microbiomes. For the first time, the lab of Nicola Segata, a professor of genetics and computational biology at the University of Trento, located in Italy, has shown that bacteria of the microbiome are transmitted between many individuals, not just infants and their mothers, in ways that can’t be explained by their shared diet or geography.
It’s a finding with wide-ranging implications, yet frustratingly few predictable outcomes. Our microbiomes are an ever-growing and changing collection of helpful and harmful bacteria that we begin to accumulate the moment we’re born, but experts are still struggling to unravel why and how bacteria from one person’s gut or mouth become established in another person’s microbiome, as opposed to simply passing through.
“If we are looking at the overall species composition of the microbiome, then there is an effect of age of course, and many other factors,” Segata says. “But if we are looking at where our strains are coming from, 99 percent of them are only present in other people’s guts. They need to come from other guts.”
If we could better understand this process, we might be able to control and use it; perhaps hospital patients could avoid infections from other patients when their microbiome is depleted by antibiotics and their immune system is weakened, for example. But scientists are just beginning to link human microbiomes with various ailments. Growing evidence shows that our microbiomes steer our long-term health, impacting conditions like obesity, irritable bowel syndrome, type 2 diabetes, and cancer.
Previous work from Segata’s lab and others illuminated the ways bacteria are passed from mothers to infants during the first few months of life during vaginal birth, breastfeeding and other close contact. And scientists have long known that people in close proximity tend to share bacteria. But the factors related to that overlap, such as genetics and diet, were unclear, especially outside the mother-baby dyad.
“If we look at strain sharing between a mother and an infant at five years of age, for example, we cannot really tell which was due to transmission at birth and which is due to continued transmission because of contact,” Segata says. Experts hypothesized that they could be caused by bacterial similarities in the environment itself, genetics, or bacteria from shared foods that colonized the guts of people in close contact.
Strain sharing was highest in mother-child pairs, with 96 percent of them sharing strains, and only slightly lower in members of shared households, at 95 percent.
In Italy, researchers led by Mireia Valles-Colomer, including Segata, hoped to unravel this mystery. They compared data from 9,715 stool and saliva samples in 31 genomic datasets with existing metadata. Scientists zoomed in on variations in each bacterial strain down to the individual level. They examined not only mother-child pairs, but people living in the same household, adult twins, and people living in the same village in a level of detail that wasn’t possible before, due to its high cost and difficulties in retrieving data about interactions between individuals, Segata explained.
“This paper is, with high granularity, quantifying the percent sharing that you expect between different types of social interactions, controlling for things like genetics and diet,” Gibbons says. Strain sharing was highest in mother-child pairs, with 96 percent of them sharing strains, and only slightly lower in members of shared households, at 95 percent. And at least half of the mother-infant pairs shared 30 percent of their strains; the median was 12 percent among people in shared households. Yet, there was no sharing among eight percent of adult twins who lived separately, and 16 percent of people within villages who resided in different households. The results were published in Nature.
It’s not a regional phenomenon. Although the types of bacterial strains varied depending on whether people lived in western and eastern nations — datasets were drawn from 20 countries on five continents — the patterns of sharing were much the same. To establish these links, scientists focused on individual variations in shared bacterial strains, differences that create unique bacterial “fingerprints” in each person, while controlling for variables like diet, demonstrating that the bacteria had been transmitted between people and were not the result of environmental similarities.
The impact of this bacterial sharing isn’t clear, but shouldn’t be viewed with trepidation, according to Sean Gibbons, a microbiome scientist at the nonprofit Institute for Systems Biology.
“The vast majority of these bugs are actually either benign or beneficial to our health, and the fact that we're swapping and sharing them and that we can take someone else's strain and supplement or better diversify our own little garden is not necessarily a bad thing,” he says.
"There are hundreds of billions of dollars of investment capital moving into these microbiome therapeutic companies; bugs as drugs, so to speak,” says Sean Gibbons, a microbiome scientist at the Institute for Systems Biology.
Everyday habits like exercising and eating vegetables promote a healthy, balanced gut microbiome, which is linked to better metabolic and immune function, and fewer illnesses. While many people’s microbiomes contain bacteria like C. diff or E. coli, these bacteria don’t cause diseases in most cases because they’re present in low levels. But a microbiome that’s been wiped out by, say, antibiotics, may no longer keep these bacteria in check, allowing them to proliferate and make us sick.
“A big challenge in the microbiome field is being able to rationally predict whether, if you're exposed to a particular bug, it will stick in the context of your specific microbiome,” Gibbons says.
Gibbons predicts that explorations of microbe-based therapeutics will be “exploding” in the coming decades. “There are hundreds of billions of dollars of investment capital moving into these microbiome therapeutic companies; bugs as drugs, so to speak,” he says. Rather than taking a mass-marketed probiotic, a precise understanding of an individual’s microbiome could help target the introduction of just the right bacteria at just the right time to prevent or treat a particular illness.
Because the current study did not differentiate between different types of contact or relationships among household members sharing bacterial strains or determine the direction of transmission, Segata says his current project is examining children in daycare settings and tracking their microbiomes over time to understand the role genetics and everyday interactions play in the level of transmission that occurs.
This relatively newfound ability to trace bacterial variants to minute levels has unlocked the chance for scientists to untangle when and how bacteria leap from one microbiome to another. As researchers come to better understand the factors that permit a strain to establish itself within a microbiome, they could uncover new strategies to control these microbes, harnessing the makeup of each microbiome to help people to resist life-altering medical conditions.
Are the gains from gain-of-function research worth the risks?
Gain-of-function research can make pathogens more infectious and deadly. It also enables scientists to prepare remedies in advance.
Scientists have long argued that gain-of-function research, which can make viruses and other infectious agents more contagious or more deadly, was necessary to develop therapies and vaccines to counter the pathogens in case they were used for biological warfare. As the SARS-CoV-2 origins are being investigated, one prominent theory suggests it had leaked from a biolab that conducted gain-of-function research, causing a global pandemic that claimed nearly 6.9 million lives. Now some question the wisdom of engaging in this type of research, stating that the risks may far outweigh the benefits.
“Gain-of-function research means genetically changing a genome in a way that might enhance the biological function of its genes, such as its transmissibility or the range of hosts it can infect,” says George Church, professor of genetics at Harvard Medical School. This can occur through direct genetic manipulation as well as by encouraging mutations while growing successive generations of micro-organism in culture. “Some of these changes may impact pathogenesis in a way that is hard to anticipate in advance,” Church says.
In the wake of the global pandemic, the pros and cons of gain-of-function research are being fiercely debated. Some scientists say this type of research is vital for preventing future pandemics or for preparing for bioweapon attacks. Others consider it another disaster waiting to happen. The Government Accounting Office issued a report charging that a framework developed by the U.S. Department of Health & Human Services (HHS) provided inadequate oversight of this potentially deadly research. There’s a movement to stop it altogether. In January, the Viral Gain-of-Function Research Moratorium Act (S. 81) was introduced into the Senate to cease awarding federal research funding to institutions doing gain-of-function studies.
While testifying before the House COVID Origins Select Committee on March 8th, Robert Redfield, former director of the U.S. Centers for Disease Control and Prevention, said that COVID-19 may have resulted from an accidental lab leak involving gain-of-function research. Redfield said his conclusion is based upon the “rapid and high infectivity for human-to-human transmission, which then predicts the rapid evolution of new variants.”
“It is a very, very, very small subset of life science research that could potentially generate a potential pandemic pathogen,” said Gerald Parker, associate dean for Global One Health at Texas A&M University.
“In my opinion,” Redfield continues, “the COVID-19 pandemic presents a case study on the potential dangers of such research. While many believe that gain-of-function research is critical to get ahead of viruses by developing vaccines, in this case, I believe that was the exact opposite.” Consequently, Redfield called for a moratorium on gain-of-function research until there is consensus about the value of such risky science.
What constitutes risky?
The Federal Select Agent Program lists 68 specific infectious agents as risky because they are either very contagious or very deadly. In order to work with these 68 agents, scientists must register with the federal government. Meanwhile, research on deadly pathogens that aren’t easily transmitted, or pathogens that are quite contagious but not deadly, can be conducted without such oversight. “If you’re not working with select agents, you’re not required to register the research with the federal government,” says Gerald Parker, associate dean for Global One Health at Texas A&M University. But the 68-item list may not have everything that could possibly become dangerous or be engineered to be dangerous, thus escaping the government’s scrutiny—an issue that new regulations aim to address.
In January 2017, the White House Office of Science and Technology Policy (OSTP) issued additional guidance. It required federal departments and agencies to follow a series of steps when reviewing proposed research that could create, transfer, or use potential pandemic pathogens resulting from the enhancement of a pathogen’s transmissibility or virulence in humans.
In defining risky pathogens, OSTP included viruses that were likely to be highly transmissible and highly virulent, and thus very deadly. The Proposed Biosecurity Oversight Framework for the Future of Science, outlined in 2023, broadened the scope to require federal review of research “that is reasonably anticipated to enhance the transmissibility and/or virulence of any pathogen” likely to pose a threat to public health, health systems or national security. Those types of experiments also include the pathogens’ ability to evade vaccines or therapeutics, or diagnostic detection.
However, Parker says that dangers of generating a pandemic-level germ are tiny. “It is a very, very, very small subset of life science research that could potentially generate a potential pandemic pathogen.” Since gain-of-function guidelines were first issued in 2017, only three such research projects have met those requirements for HHS review. They aimed to study influenza and bird flu. Only two of those projects were funded, according to the NIH Office of Science Policy. For context, NIH funded approximately 11,000 of the 54,000 grant applications it received in 2022.
Guidelines governing gain-of-function research are being strengthened, but Church points out they aren’t ideal yet. “They need to be much clearer about penalties and avoiding positive uses before they would be enforceable.”
What do we gain from gain-of-function research?
The most commonly cited reason to conduct gain-of-function research is for biodefense—the government’s ability to deal with organisms that may pose threats to public health.
In the era of mRNA vaccines, the advance preparedness argument may be even less relevant.
“The need to work with potentially dangerous viruses is central to our preparedness,” Parker says. “It’s essential that we know and understand the basic biology, microbiology, etc. of some of these dangerous pathogens.” That includes increasing our knowledge of the molecular mechanisms by which a virus could become a sustained threat to humans. “Knowing that could help us detect [risks] earlier,” Parker says—and could make it possible to have medical countermeasures, like vaccines and therapeutics, ready.
Most vaccines, however, aren’t affected by this type of research. Essentially, scientists hope they will never need to use it. Moreover, Paul Mango, HSS former deputy chief of staff for policy, and author of the 2022 book Warp Speed, says he believes that in the era of mRNA vaccines, the advance preparedness argument may be even less relevant. “That’s because these vaccines can be developed and produced in less than 12 months, unlike traditional vaccines that require years of development,” he says.
Can better oversight guarantee safety?
Another situation, which Parker calls unnecessarily dangerous, is when regulatory bodies cannot verify that the appropriate biosafety and biosecurity controls are in place.
Gain-of-function studies, Parker points out, are conducted at the basic research level, and they’re performed in high-containment labs. “As long as all the processes, procedures and protocols are followed and there’s appropriate oversight at the institutional and scientific level, it can be conducted safely.”
Globally, there are 69 Biosafety Level 4 (BSL4) labs operating, under construction or being planned, according to recent research from King’s College London and George Mason University for Global BioLabs. Eleven of these 18 high-containment facilities that are planned or under construction are in Asia. Overall, three-quarters of the BSL4 labs are in cities, increasing public health risks if leaks occur.
Researchers say they are confident in the oversight system for BSL4 labs within the U.S. They are less confident in international labs. Global BioLabs’ report concurs. It gives the highest scores for biosafety to industrialized nations, led by France, Australia, Canada, the U.S. and Japan, and the lowest scores to Saudi Arabia, India and some developing African nations. Scores for biosecurity followed similar patterns.
“There are no harmonized international biosafety and biosecurity standards,” Parker notes. That issue has been discussed for at least a decade. Now, in the wake of SARS and the COVID-19 pandemic, scientists and regulators are likely to push for unified oversight standards. “It’s time we got serious about international harmonization of biosafety and biosecurity standards and guidelines,” Parker says. New guidelines are being worked on. The National Science Advisory Board for Biosecurity (NSABB) outlined its proposed recommendations in the document titled Proposed Biosecurity Oversight Framework for the Future of Science.
The debates about whether gain-of-function research is useful or poses unnecessary risks to humanity are likely to rage on for a while. The public too has a voice in this debate and should weigh in by communicating with their representatives in government, or by partaking in educational forums or initiatives offered by universities and other institutions. In the meantime, scientists should focus on improving the research regulations, Parker notes. “We need to continue to look for lessons learned and for gaps in our oversight system,” he says. “That’s what we need to do right now.”