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.
Gene Transfer Leads to Longer Life and Healthspan
In August, a study provided the first proof-of-principle that genetic material transferred from one species to another can increase both longevity and healthspan in the recipient animal.
The naked mole rat won’t win any beauty contests, but it could possibly win in the talent category. Its superpower: fighting the aging process to live several times longer than other animals its size, in a state of youthful vigor.
It’s believed that naked mole rats experience all the normal processes of wear and tear over their lifespan, but that they’re exceptionally good at repairing the damage from oxygen free radicals and the DNA errors that accumulate over time. Even though they possess genes that make them vulnerable to cancer, they rarely develop the disease, or any other age-related disease, for that matter. Naked mole rats are known to live for over 40 years without any signs of aging, whereas mice live on average about two years and are highly prone to cancer.
Now, these remarkable animals may be able to share their superpower with other species. In August, a study provided what may be the first proof-of-principle that genetic material transferred from one species can increase both longevity and healthspan in a recipient animal.
There are several theories to explain the naked mole rat’s longevity, but the one explored in the study, published in Nature, is based on the abundance of large-molecule high-molecular mass hyaluronic acid (HMM-HA).
A small molecule version of hyaluronic acid is commonly added to skin moisturizers and cosmetics that are marketed as ways to keep skin youthful, but this version, just applied to the skin, won’t have a dramatic anti-aging effect. The naked mole rat has an abundance of the much-larger molecule, HMM-HA, in the chemical-rich solution between cells throughout its body. But does the HMM-HA actually govern the extraordinary longevity and healthspan of the naked mole rat?
To answer this question, Dr. Vera Gorbunova, a professor of biology and oncology at the University of Rochester, and her team created a mouse model containing the naked mole rat gene hyaluronic acid synthase 2, or nmrHas2. It turned out that the mice receiving this gene during their early developmental stage also expressed HMM-HA.
The researchers found that the effects of the HMM-HA molecule in the mice were marked and diverse, exceeding the expectations of the study’s co-authors. High-molecular mass hyaluronic acid was more abundant in kidneys, muscles and other organs of the Has2 mice compared to control mice.
In addition, the altered mice had a much lower incidence of cancer. Seventy percent of the control mice eventually developed cancer, compared to only 57 percent of the altered mice, even after several techniques were used to induce the disease. The biggest difference occurred in the oldest mice, where the cancer incidence for the Has2 mice and the controls was 47 percent and 83 percent, respectively.
With regard to longevity, Has2 males increased their lifespan by more than 16 percent and the females added 9 percent. “Somehow the effect is much more pronounced in male mice, and we don’t have a perfect answer as to why,” says Dr. Gorbunova. Another improvement was in the healthspan of the altered mice: the number of years they spent in a state of relative youth. There’s a frailty index for mice, which includes body weight, mobility, grip strength, vision and hearing, in addition to overall conditions such as the health of the coat and body temperature. The Has2 mice scored lower in frailty than the controls by all measures. They also performed better in tests of locomotion and coordination, and in bone density.
Gorbunova’s results show that a gene artificially transferred from one species can have a beneficial effect on another species for longevity, something that had never been demonstrated before. This finding is “quite spectacular,” said Steven Austad, a biologist at the University of Alabama at Birmingham, who was not involved in the study.
Just as in lifespan, the effects in various organs and systems varied between the sexes, a common occurrence in longevity research, according to Austad, who authored the book Methuselah’s Zoo and specializes in the biological differences between species. “We have ten drugs that we can give to mice to make them live longer,” he says, “and all of them work better in one sex than in the other.” This suggests that more attention needs to be paid to the different effects of anti-aging strategies between the sexes, as well as gender differences in healthspan.
According to the study authors, the HMM-HA molecule delivered these benefits by reducing inflammation and senescence (cell dysfunction and death). The molecule also caused a variety of other benefits, including an upregulation of genes involved in the function of mitochondria, the powerhouses of the cells. These mechanisms are implicated in the aging process, and in human disease. In humans, virtually all noncommunicable diseases entail an acceleration of the aging process.
So, would the gene that creates HMM-HA have similar benefits for longevity in humans? “We think about these questions a lot,” Gorbunova says. “It’s been done by injections in certain patients, but it has a local effect in the treatment of organs affected by disease,” which could offer some benefits, she added.
“Mice are very short-lived and cancer-prone, and the effects are small,” says Steven Austad, a biologist at the University of Alabama at Birmingham. “But they did live longer and stay healthy longer, which is remarkable.”
As for a gene therapy to introduce the nmrHas2 gene into humans to obtain a global result, she’s skeptical because of the complexity involved. Gorbunova notes that there are potential dangers in introducing an animal gene into humans, such as immune responses or allergic reactions.
Austad is equally cautious about a gene therapy. “What this study says is that you can take something a species does well and transfer at least some of that into a new species. It opens up the way, but you may need to transfer six or eight or ten genes into a human” to get the large effect desired. Humans are much more complex and contain many more genes than mice, and all systems in a biological organism are intricately connected. One naked mole rat gene may not make a big difference when it interacts with human genes, metabolism and physiology.
Still, Austad thinks the possibilities are tantalizing. “Mice are very short-lived and cancer-prone, and the effects are small,” he says. “But they did live longer and stay healthy longer, which is remarkable.”
As for further research, says Austad, “The first place to look is the skin” to see if the nmrHas2 gene and the HMM-HA it produces can reduce the chance of cancer. Austad added that it would be straightforward to use the gene to try to prevent cancer in skin cells in a dish to see if it prevents cancer. It would not be hard to do. “We don’t know of any downsides to hyaluronic acid in skin, because it’s already used in skin products, and you could look at this fairly quickly.”
“Aging mechanisms evolved over a long time,” says Gorbunova, “so in aging there are multiple mechanisms working together that affect each other.” All of these processes could play a part and almost certainly differ from one species to the next.
“HMM-HA molecules are large, but we’re now looking for a small-molecule drug that would slow it’s breakdown,” she says. “And we’re looking for inhibitors, now being tested in mice, that would hinder the breakdown of hyaluronic acid.” Gorbunova has found a natural, plant-based product that acts as an inhibitor and could potentially be taken as a supplement. Ultimately, though, she thinks that drug development will be the safest and most effective approach to delivering HMM-HA for anti-aging.
A new study provides key insights in what causes Alzheimer's: a breakdown in the brain’s system for clearing waste.
In recent years, researchers of Alzheimer’s have made progress in figuring out the complex factors that lead to the disease. Yet, the root cause, or causes, of Alzheimer’s are still pretty much a mystery.
In fact, many people get Alzheimer’s even though they lack the gene variant we know can play a role in the disease. This is a critical knowledge gap for research to address because the vast majority of Alzheimer’s patients don’t have this variant.
A new study provides key insights into what’s causing the disease. The research, published in Nature Communications, points to a breakdown over time in the brain’s system for clearing waste, an issue that seems to happen in some people as they get older.
Michael Glickman, a biologist at Technion – Israel Institute of Technology, helped lead this research. I asked him to tell me about his approach to studying how this breakdown occurs in the brain, and how he tested a treatment that has potential to fix the problem at its earliest stages.
Dr. Michael Glickman is internationally renowned for his research on the ubiquitin-proteasome system (UPS), the brain's system for clearing the waste that is involved in diseases such as Huntington's, Alzheimer's, and Parkinson's. He is the head of the Lab for Protein Characterization in the Faculty of Biology at the Technion – Israel Institute of Technology. In the lab, Michael and his team focus on protein recycling and the ubiquitin-proteasome system, which protects against serious diseases like Alzheimer’s, Parkinson’s, cystic fibrosis, and diabetes. After earning his PhD at the University of California at Berkeley in 1994, Michael joined the Technion as a Senior Lecturer in 1998 and has served as a full professor since 2009.
Dr. Michael Glickman