Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
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.
A new injection is helping stave off RSV this season
In November 2021, Mickayla Wininger’s then one-month-old son, Malcolm, endured a terrifying bout with RSV, the respiratory syncytial (sin-SISH-uhl) virus—a common ailment that affects all age groups. Most people recover from mild, cold-like symptoms in a week or two, but RSV can be life-threatening in others, particularly infants.
Wininger, who lives in southern Illinois, was dressing Malcolm for bed when she noticed what seemed to be a minor irregularity with this breathing. She and her fiancé, Gavin McCullough, planned to take him to the hospital the next day. The matter became urgent when, in the morning, the boy’s breathing appeared to have stopped.
After they dialed 911, Malcolm started breathing again, but he ended up being hospitalized three times for RSV and defects in his heart. Eventually, he recovered fully from RSV, but “it was our worst nightmare coming to life,” Wininger recalled.
It’s a scenario that the federal government is taking steps to prevent. In July, the Food and Drug Administration approved a single-dose, long-acting injection to protect babies and toddlers. The injection, called Beyfortus, or nirsevimab, became available this October. It reduces the incidence of RSV in pre-term babies and other infants for their first RSV season. Children at highest risk for severe RSV are those who were born prematurely and have either chronic lung disease of prematurity or congenital heart disease. In those cases, RSV can progress to lower respiratory tract diseases such as pneumonia and bronchiolitis, or swelling of the lung’s small airway passages.
Each year, RSV is responsible for 2.1 million outpatient visits among children younger than five-years-old, 58,000 to 80,000 hospitalizations in this age group, and between 100 and 300 deaths, according to the Centers for Disease Control and Prevention. Transmitted through close contact with an infected person, the virus circulates on a seasonal basis in most regions of the country, typically emerging in the fall and peaking in the winter.
In August, however, the CDC issued a health advisory on a late-summer surge in severe cases of RSV among young children in Florida and Georgia. The agency predicts "increased RSV activity spreading north and west over the following two to three months.”
Infants are generally more susceptible to RSV than older people because their airways are very small, and their mechanisms to clear these passages are underdeveloped. RSV also causes mucus production and inflammation, which is more of a problem when the airway is smaller, said Jennifer Duchon, an associate professor of newborn medicine and pediatrics in the Icahn School of Medicine at Mount Sinai in New York.
In 2021 and 2022, RSV cases spiked, sending many to emergency departments. “RSV can cause serious disease in infants and some children and results in a large number of emergency department and physician office visits each year,” John Farley, director of the Office of Infectious Diseases in the FDA’s Center for Drug Evaluation and Research, said in a news release announcing the approval of the RSV drug. The decision “addresses the great need for products to help reduce the impact of RSV disease on children, families and the health care system.”
Sean O’Leary, chair of the committee on infectious diseases for the American Academy of Pediatrics, says that “we’ve never had a product like this for routine use in children, so this is very exciting news.” It is recommended for all kids under eight months old for their first RSV season. “I would encourage nirsevimab for all eligible children when it becomes available,” O’Leary said.
For those children at elevated risk of severe RSV and between the ages of 8 and 19 months, the CDC recommends one dose in their second RSV season.
The drug will be “really helpful to keep babies healthy and out of the hospital,” said O’Leary, a professor of pediatrics at the University of Colorado Anschutz Medical Campus/Children’s Hospital Colorado in Denver.
An antiviral drug called Synagis (palivizumab) has been an option to prevent serious RSV illness in high-risk infants since it was approved by the FDA in 1998. The injection must be given monthly during RSV season. However, its use is limited to “certain children considered at high risk for complications, does not help cure or treat children already suffering from serious RSV disease, and cannot prevent RSV infection,” according to the National Foundation for Infectious Diseases.
Until the approval this summer of the new monoclonal antibody, nirsevimab, there wasn’t a reliable method to prevent infection in most healthy infants.
Both nirsevimab and palivizumab are monoclonal antibodies that act against RSV. Monoclonal antibodies are lab-made proteins that mimic the immune system’s ability to fight off harmful pathogens such as viruses. A single intramuscular injection of nirsevimab preceding or during RSV season may provide protection.
The strategy with the new monoclonal antibody is “to extend protection to healthy infants who nonetheless are at risk because of their age, as well as infants with additional medical risk factors,” said Philippa Gordon, a pediatrician and infectious disease specialist in Brooklyn, New York, and medical adviser to Park Slope Parents, an online community support group.
No specific preventive measure is needed for older and healthier kids because they will develop active immunity, which is more durable. Meanwhile, older adults, who are also vulnerable to RSV, can receive one of two new vaccines. So can pregnant women, who pass on immunity to the fetus, Gordon said.
Until the approval this summer of the new monoclonal antibody, nirsevimab, there wasn’t a reliable method to prevent infection in most healthy infants, “nor is there any treatment other than giving oxygen or supportive care,” said Stanley Spinner, chief medical officer and vice president of Texas Children’s Pediatrics and Texas Children’s Urgent Care.
As with any virus, washing hands frequently and keeping infants and children away from sick people are the best defenses, Duchon said. This approach isn’t foolproof because viruses can run rampant in daycare centers, schools and parents’ workplaces, she added.
Mickayla Wininger, Malcolm’s mother, insists that family and friends wear masks, wash their hands and use hand sanitizer when they’re around her daughter and two sons. She doesn’t allow them to kiss or touch the children. Some people take it personally, but she would rather be safe than sorry.
Wininger recalls the severe anxiety caused by Malcolm's ordeal with RSV. After returning with her infant from his hospital stays, she was terrified to go to sleep. “My fiancé and I would trade shifts, so that someone was watching over our son 24 hours a day,” she said. “I was doing a night shift, so I would take caffeine pills to try and keep myself awake and would end up crashing early hours in the morning and wake up frantically thinking something happened to my son.”
Two years later, her anxiety has become more manageable, and Malcolm is doing well. “He is thriving now,” Wininger said. He recently had his second birthday and "is just the spunkiest boy you will ever meet. He looked death straight in the eyes and fought to be here today.”
Story by Big Think
For most of history, artificial intelligence (AI) has been relegated almost entirely to the realm of science fiction. Then, in late 2022, it burst into reality — seemingly out of nowhere — with the popular launch of ChatGPT, the generative AI chatbot that solves tricky problems, designs rockets, has deep conversations with users, and even aces the Bar exam.
But the truth is that before ChatGPT nabbed the public’s attention, AI was already here, and it was doing more important things than writing essays for lazy college students. Case in point: It was key to saving the lives of tens of millions of people.
AI-designed mRNA vaccines
As Dave Johnson, chief data and AI officer at Moderna, told MIT Technology Review‘s In Machines We Trust podcast in 2022, AI was integral to creating the company’s highly effective mRNA vaccine against COVID. Moderna and Pfizer/BioNTech’s mRNA vaccines collectively saved between 15 and 20 million lives, according to one estimate from 2022.
Johnson described how AI was hard at work at Moderna, well before COVID arose to infect billions. The pharmaceutical company focuses on finding mRNA therapies to fight off infectious disease, treat cancer, or thwart genetic illness, among other medical applications. Messenger RNA molecules are essentially molecular instructions for cells that tell them how to create specific proteins, which do everything from fighting infection, to catalyzing reactions, to relaying cellular messages.
Johnson and his team put AI and automated robots to work making lots of different mRNAs for scientists to experiment with. Moderna quickly went from making about 30 per month to more than one thousand. They then created AI algorithms to optimize mRNA to maximize protein production in the body — more bang for the biological buck.
For Johnson and his team’s next trick, they used AI to automate science, itself. Once Moderna’s scientists have an mRNA to experiment with, they do pre-clinical tests in the lab. They then pore over reams of data to see which mRNAs could progress to the next stage: animal trials. This process is long, repetitive, and soul-sucking — ill-suited to a creative scientist but great for a mindless AI algorithm. With scientists’ input, models were made to automate this tedious process.
“We don’t think about AI in the context of replacing humans,” says Dave Johnson, chief data and AI officer at Moderna. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
All these AI systems were in put in place over the past decade. Then COVID showed up. So when the genome sequence of the coronavirus was made public in January 2020, Moderna was off to the races pumping out and testing mRNAs that would tell cells how to manufacture the coronavirus’s spike protein so that the body’s immune system would recognize and destroy it. Within 42 days, the company had an mRNA vaccine ready to be tested in humans. It eventually went into hundreds of millions of arms.
Biotech harnesses the power of AI
Moderna is now turning its attention to other ailments that could be solved with mRNA, and the company is continuing to lean on AI. Scientists are still coming to Johnson with automation requests, which he happily obliges.
“We don’t think about AI in the context of replacing humans,” he told the Me, Myself, and AI podcast. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
Moderna, which was founded as a “digital biotech,” is undoubtedly the poster child of AI use in mRNA vaccines. Moderna recently signed a deal with IBM to use the company’s quantum computers as well as its proprietary generative AI, MoLFormer.
Moderna’s success is encouraging other companies to follow its example. In January, BioNTech, which partnered with Pfizer to make the other highly effective mRNA vaccine against COVID, acquired the company InstaDeep for $440 million to implement its machine learning AI across its mRNA medicine platform. And in May, Chinese technology giant Baidu announced an AI tool that designs super-optimized mRNA sequences in minutes. A nearly countless number of mRNA molecules can code for the same protein, but some are more stable and result in the production of more proteins. Baidu’s AI, called “LinearDesign,” finds these mRNAs. The company licensed the tool to French pharmaceutical company Sanofi.
Writing in the journal Accounts of Chemical Research in late 2021, Sebastian M. Castillo-Hair and Georg Seelig, computer engineers who focus on synthetic biology at the University of Washington, forecast that AI machine learning models will further accelerate the biotechnology research process, putting mRNA medicine into overdrive to the benefit of all.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.