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.
Every year, around two million people worldwide die of liver disease. While some people inherit the disease, it’s most commonly caused by hepatitis, obesity and alcoholism. These underlying conditions kill liver cells, causing scar tissue to form until eventually the liver cannot function properly. Since 1979, deaths due to liver disease have increased by 400 percent.
The sooner the disease is detected, the more effective treatment can be. But once symptoms appear, the liver is already damaged. Around 50 percent of cases are diagnosed only after the disease has reached the final stages, when treatment is largely ineffective.
To address this problem, Owlstone Medical, a biotech company in England, has developed a breath test that can detect liver disease earlier than conventional approaches. Human breath contains volatile organic compounds (VOCs) that change in the first stages of liver disease. Owlstone’s breath test can reliably collect, store and detect VOCs, while picking out the specific compounds that reveal liver disease.
“There’s a need to screen more broadly for people with early-stage liver disease,” says Owlstone’s CEO Billy Boyle. “Equally important is having a test that's non-invasive, cost effective and can be deployed in a primary care setting.”
The standard tool for detection is a biopsy. It is invasive and expensive, making it impractical to use for people who aren't yet symptomatic. Meanwhile, blood tests are less invasive, but they can be inaccurate and can’t discriminate between different stages of the disease.
In the past, breath tests have not been widely used because of the difficulties of reliably collecting and storing breath. But Owlstone’s technology could help change that.
The team is testing patients in the early stages of advanced liver disease, or cirrhosis, to identify and detect these biomarkers. In an initial study, Owlstone’s breathalyzer was able to pick out patients who had early cirrhosis with 83 percent sensitivity.
Boyle’s work is personally motivated. His wife died of colorectal cancer after she was diagnosed with a progressed form of the disease. “That was a big impetus for me to see if this technology could work in early detection,” he says. “As a company, Owlstone is interested in early detection across a range of diseases because we think that's a way to save lives and a way to save costs.”
How it works
In the past, breath tests have not been widely used because of the difficulties of reliably collecting and storing breath. But Owlstone’s technology could help change that.
Study participants breathe into a mouthpiece attached to a breath sampler developed by Owlstone. It has cartridges are designed and optimized to collect gases. The sampler specifically targets VOCs, extracting them from atmospheric gases in breath, to ensure that even low levels of these compounds are captured.
The sampler can store compounds stably before they are assessed through a method called mass spectrometry, in which compounds are converted into charged atoms, before electromagnetic fields filter and identify even the tiniest amounts of charged atoms according to their weight and charge.
The top four compounds in our breath
In an initial study, Owlstone captured VOCs in breath to see which ones could help them tell the difference between people with and without liver disease. They tested the breath of 46 patients with liver disease - most of them in the earlier stages of cirrhosis - and 42 healthy people. Using this data, they were able to create a diagnostic model. Individually, compounds like 2-Pentanone and limonene performed well as markers for liver disease. Owlstone achieved even better performance by examining the levels of the top four compounds together, distinguishing between liver disease cases and controls with 95 percent accuracy.
“It was a good proof of principle since it looks like there are breath biomarkers that can discriminate between diseases,” Boyle says. “That was a bit of a stepping stone for us to say, taking those identified, let’s try and dose with specific concentrations of probes. It's part of building the evidence and steering the clinical trials to get to liver disease sensitivity.”
Sabine Szunerits, a professor of chemistry in Institute of Electronics at the University of Lille, sees the potential of Owlstone’s technology.
“Breath analysis is showing real promise as a clinical diagnostic tool,” says Szunerits, who has no ties with the company. “Owlstone Medical’s technology is extremely effective in collecting small volatile organic biomarkers in the breath. In combination with pattern recognition it can give an answer on liver disease severity. I see it as a very promising way to give patients novel chances to be cured.”
Improving the breath sampling process
Challenges remain. With more than one thousand VOCs found in the breath, it can be difficult to identify markers for liver disease that are consistent across many patients.
Julian Gardner is a professor of electrical engineering at Warwick University who researches electronic sensing devices. “Everyone’s breath has different levels of VOCs and different ones according to gender, diet, age etc,” Gardner says. “It is indeed very challenging to selectively detect the biomarkers in the breath for liver disease.”
So Owlstone is putting chemicals in the body that they know interact differently with patients with liver disease, and then using the breath sampler to measure these specific VOCs. The chemicals they administer are called Exogenous Volatile Organic Compound) probes, or EVOCs.
Most recently, they used limonene as an EVOC probe, testing 29 patients with early cirrhosis and 29 controls. They gave the limonene to subjects at specific doses to measure how its concentrations change in breath. The aim was to try and see what was happening in their livers.
“They are proposing to use drugs to enhance the signal as they are concerned about the sensitivity and selectivity of their method,” Gardner says. “The approach of EVOC probes is probably necessary as you can then eliminate the person-to-person variation that will be considerable in the soup of VOCs in our breath.”
Through these probes, Owlstone could identify patients with liver disease with 83 percent sensitivity. By targeting what they knew was a disease mechanism, they were able to amplify the signal. The company is starting a larger clinical trial, and the plan is to eventually use a panel of EVOC probes to make sure they can see diverging VOCs more clearly.
“I think the approach of using probes to amplify the VOC signal will ultimately increase the specificity of any VOC breath tests, and improve their practical usability,” says Roger Yazbek, who leads the South Australian Breath Analysis Research (SABAR) laboratory in Flinders University. “Whilst the findings are interesting, it still is only a small cohort of patients in one location.”
The future of breath diagnosis
Owlstone wants to partner with pharmaceutical companies looking to learn if their drugs have an effect on liver disease. They’ve also developed a microchip, a miniaturized version of mass spectrometry instruments, that can be used with the breathalyzer. It is less sensitive but will enable faster detection.
Boyle says the company's mission is for their tests to save 100,000 lives. "There are lots of risks and lots of challenges. I think there's an opportunity to really establish breath as a new diagnostic class.”
Bacterial antibiotic resistance has been a concern in the medical field for several years. Now a new, similar threat is arising: drug-resistant fungal infections. The Centers for Disease Control and Prevention considers antifungal and antimicrobial resistance to be among the world’s greatest public health challenges.
One particular type of fungal infection caused by Candida auris is escalating rapidly throughout the world. And to make matters worse, C. auris is becoming increasingly resistant to current antifungal medications, which means that if you develop a C. auris infection, the drugs your doctor prescribes may not work. “We’re effectively out of medicines,” says Thomas Walsh, founding director of the Center for Innovative Therapeutics and Diagnostics, a translational research center dedicated to solving the antimicrobial resistance problem. Walsh spoke about the challenges at a Demy-Colton Virtual Salon, one in a series of interactive discussions among life science thought leaders.
Although C. auris typically doesn’t sicken healthy people, it afflicts immunocompromised hospital patients and may cause severe infections that can lead to sepsis, a life-threatening condition in which the overwhelmed immune system begins to attack the body’s own organs. Between 30 and 60 percent of patients who contract a C. auris infection die from it, according to the CDC. People who are undergoing stem cell transplants, have catheters or have taken antifungal or antibiotic medicines are at highest risk. “We’re coming to a perfect storm of increasing resistance rates, increasing numbers of immunosuppressed patients worldwide and a bug that is adapting to higher temperatures as the climate changes,” says Prabhavathi Fernandes, chair of the National BioDefense Science Board.
Most Candida species aren’t well-adapted to our body temperatures so they aren’t a threat. C. auris, however, thrives at human body temperatures.
Although medical professionals aren’t concerned at this point about C. auris evolving to affect healthy people, they worry that its presence in hospitals can turn routine surgeries into life-threatening calamities. “It’s coming,” says Fernandes. “It’s just a matter of time.”
An emerging global threat
“Fungi are found in the environment,” explains Fernandes, so Candida spores can easily wind up on people’s skin. In hospitals, they can be transferred from contact with healthcare workers or contaminated surfaces. Most Candida species aren’t well-adapted to our body temperatures so they aren’t a threat. C. auris, however, thrives at human body temperatures. It can enter the body during medical treatments that break the skin—and cause an infection. Overall, fungal infections cost some $48 billion in the U.S. each year. And infection rates are increasing because, in an ironic twist, advanced medical therapies are enabling severely ill patients to live longer and, therefore, be exposed to this pathogen.
The first-ever case of a C. auris infection was reported in Japan in 2009, although an analysis of Candida samples dated the earliest strain to a 1996 sample from South Korea. Since then, five separate varieties – called clades, which are similar to strains among bacteria – developed independently in different geographies: South Asia, East Asia, South Africa, South America and, recently, Iran. So far, C. auris infections have been reported in 35 countries.
In the U.S., the first infection was reported in 2016, and the CDC started tracking it nationally two years later. During that time, 5,654 cases have been reported to the CDC, which only tracks U.S. data.
What’s more notable than the number of cases is their rate of increase. In 2016, new cases increased by 175 percent and, on average, they have approximately doubled every year. From 2016 through 2022, the number of infections jumped from 63 to 2,377, a roughly 37-fold increase.
“This reminds me of what we saw with epidemics from 2013 through 2020… with Ebola, Zika and the COVID-19 pandemic,” says Robin Robinson, CEO of Spriovas and founding director of the Biomedical Advanced Research and Development Authority (BARDA), which is part of the U.S. Department of Health and Human Services. These epidemics started with a hockey stick trajectory, Robinson says—a gradual growth leading to a sharp spike, just like the shape of a hockey stick.
Another challenge is that right now medics don’t have rapid diagnostic tests for fungal infections. Currently, patients are often misdiagnosed because C. auris resembles several other easily treated fungi. Or they are diagnosed long after the infection begins and is harder to treat.
The problem is that existing diagnostics tests can only identify C. auris once it reaches the bloodstream. Yet, because this pathogen infects bodily tissues first, it should be possible to catch it much earlier before it becomes life-threatening. “We have to diagnose it before it reaches the bloodstream,” Walsh says.
The most alarming fact is that some Candida infections no longer respond to standard therapeutics.
“We need to focus on rapid diagnostic tests that do not rely on a positive blood culture,” says John Sperzel, president and CEO of T2 Biosystems, a company specializing in diagnostics solutions. Blood cultures typically take two to three days for the concentration of Candida to become large enough to detect. The company’s novel test detects about 90 percent of Candida species within three to five hours—thanks to its ability to spot minute quantities of the pathogen in blood samples instead of waiting for them to incubate and proliferate.
Unlike other Candida species C. auris thrives at human body temperatures
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Tackling the resistance challenge
The most alarming fact is that some Candida infections no longer respond to standard therapeutics. The number of cases that stopped responding to echinocandin, the first-line therapy for most Candida infections, tripled in 2020, according to a study by the CDC.
Now, each of the first four clades shows varying levels of resistance to all three commonly prescribed classes of antifungal medications, such as azoles, echinocandins, and polyenes. For example, 97 percent of infections from C. auris Clade I are resistant to fluconazole, 54 percent to voriconazole and 30 percent of amphotericin. Nearly half are resistant to multiple antifungal drugs. Even with Clade II fungi, which has the least resistance of all the clades, 11 to 14 percent have become resistant to fluconazole.
Anti-fungal therapies typically target specific chemical compounds present on fungi’s cell membranes, but not on human cells—otherwise the medicine would cause damage to our own tissues. Fluconazole and other azole antifungals target a compound called ergosterol, preventing the fungal cells from replicating. Over the years, however, C. auris evolved to resist it, so existing fungal medications don’t work as well anymore.
A newer class of drugs called echinocandins targets a different part of the fungal cell. “The echinocandins – like caspofungin – inhibit (a part of the fungi) involved in making glucan, which is an essential component of the fungal cell wall and is not found in human cells,” Fernandes says. New antifungal treatments are needed, she adds, but there are only a few magic bullets that will hit just the fungus and not the human cells.
Research to fight infections also has been challenged by a lack of government support. That is changing now that BARDA is requesting proposals to develop novel antifungals. “The scope includes C. auris, as well as antifungals following a radiological/nuclear emergency, says BARDA spokesperson Elleen Kane.
The remaining challenge is the number of patients available to participate in clinical trials. Large numbers are needed, but the available patients are quite sick and often die before trials can be completed. Consequently, few biopharmaceutical companies are developing new treatments for C. auris.
ClinicalTrials.gov reports only two drugs in development for invasive C. auris infections—those than can spread throughout the body rather than localize in one particular area, like throat or vaginal infections: ibrexafungerp by Scynexis, Inc., fosmanogepix, by Pfizer.
Scynexis’ ibrexafungerp appears active against C. auris and other emerging, drug-resistant pathogens. The FDA recently approved it as a therapy for vaginal yeast infections and it is undergoing Phase III clinical trials against invasive candidiasis in an attempt to keep the infection from spreading.
“Ibreafungerp is structurally different from other echinocandins,” Fernandes says, because it targets a different part of the fungus. “We’re lucky it has activity against C. auris.”
Pfizer’s fosmanogepix is in Phase II clinical trials for patients with invasive fungal infections caused by multiple Candida species. Results are showing significantly better survival rates for people taking fosmanogepix.
Although C. auris does pose a serious threat to healthcare worldwide, scientists try to stay optimistic—because they recognized the problem early enough, they might have solutions in place before the perfect storm hits. “There is a bit of hope,” says Robinson. “BARDA has finally been able to fund the development of new antifungal agents and, hopefully, this year we can get several new classes of antifungals into development.”