Black Participants Are Sorely Absent from Medical Research
After years of suffering from mysterious symptoms, my mother Janice Thomas finally found a doctor who correctly diagnosed her with two autoimmune diseases, Lupus and Sjogren's. Both diseases are more prevalent in the black population than in other races and are often misdiagnosed.
The National Institutes of Health has found that minorities make up less than 10 percent of trial participants.
Like many chronic health conditions, a lack of understanding persists about their causes, individual manifestations, and best treatment strategies.
On the search for relief from chronic pain, my mother started researching options and decided to participate in clinical trials as a way to gain much-needed insights. In return, she received discounted medical testing and has played an active role in finding answers for all.
"When my doctor told me I could get financial or medical benefits from participating in clinical trials for the same test I was already doing, I figured it would be an easy way to get some answers at little to no cost," she says.
As a person of color, her presence in clinical studies is rare. The National Institutes of Health has found that minorities make up less than 10 percent of trial participants.
Without trial participation that is reflective of the general population, pharmaceutical companies and medical professionals are left guessing how various drugs work across racial lines. For example, albuterol, a widely used asthma treatment, was found to have decreased effectiveness for black American and Puerto Rican children. Many high mortality conditions, like cancer, also show different outcomes based on race.
Over the last decade, the pervasive lack of representation has left communities of color demanding higher levels of involvement in the research process. However, no consensus yet exists on how best to achieve this.
But experts suggest that before we can improve black participation in medical studies, key misconceptions must be addressed, such as the false assumption that such patients are unwilling to participate because they distrust scientists.
Jill A. Fisher, a professor in the Center for Bioethics at the University of North Carolina at Chapel Hill, learned in one study that mistrust wasn't the main barrier for black Americans. "There is a lot of evidence that researchers' recruitment of black Americans is generally poorly done, with many black patients simply not asked," Fisher says. "Moreover, the underrepresentation of black Americans is primarily true for efficacy trials - those testing whether an investigational drug might therapeutically benefit patients with specific illnesses."
Without increased minority participation, research will not accurately reflect the diversity of the general population.
Dr. Joyce Balls-Berry, a psychiatric epidemiologist and health educator, agrees that black Americans are often overlooked in the process. One study she conducted found that "enrollment of minorities in clinical trials meant using a variety of culturally appropriate strategies to engage participants," she explained.
To overcome this hurdle, The National Black Church Initiative (NBCI), a faith-based organization made up of 34,000 churches and over 15.7 million African Americans, last year urged the Food and Drug Administration to mandate diversity in all clinical trials before approving a drug or device. However, the FDA declined to implement the mandate, declaring that they don't have the authority to regulate diversity in clinical trials.
"African Americans have not been successfully incorporated into the advancement of medicine and research technologies as legitimate and natural born citizens of this country," admonishes NBCI's president Rev. Anthony Evans.
His words conjure a reminder of the medical system's insidious history for people of color. The most infamous incident is the Tuskegee syphilis scandal, in which white government doctors perpetrated harmful experiments on hundreds of unsuspecting black men for forty years, until the research was shut down in the early 1970s.
Today, in the second decade of twenty-first century, the pernicious narrative that blacks are outsiders in science and medicine must be challenged, says Dr. Danielle N. Lee, assistant professor of biological sciences at Southern Illinois University. And having majority white participants in clinical trials only furthers the notion that "whiteness" is the default.
According to Lee, black individuals often see themselves disconnected from scientific and medical processes. "One of the critiques with science and medical research is that communities of color, and black communities in particular, regard ourselves as outsiders of science," Lee says. "We are othered."
Without increased minority participation, research will not accurately reflect the diversity of the general population.
"We are all human, but we are different, and yes, even different populations of people require modified medical responses," she points out.
Another obstacle is that many trials have health requirements that exclude black Americans, like not wanting individuals who have high blood pressure or a history of stroke. Considering that this group faces health disparities at a higher rate than whites, this eliminates eligibility for millions of potential participants.
One way to increase the diversity in sample participation without an FDA mandate is to include more black Americans in both volunteer and clinical roles during the research process to increase accountability in treatment, education, and advocacy.
"When more of us participate in clinical trials, we help build out the basic data sets that account for health disparities from the start, not after the fact," Lee says. She also suggests that researchers involve black patient representatives throughout the clinical trial process, from the study design to the interpretation of results.
"This allows for the black community to give insight on how to increase trial enrollment and help reduce stigma," she explains.
Thankfully, partnerships are popping up like the one between The Howard University's Cancer Center and Driver, a platform that connects cancer patients to treatment and trials. These sorts of targeted and culturally tailored efforts allow black patients to receive assistance from well-respected organizations.
Some observers suggest that the federal government and pharmaceutical industries must step up to address the gap.
However, some experts say that the black community should not be held solely responsible for solving a problem it did not cause. Instead, some observers suggest that the federal government and pharmaceutical industries must step up to address the gap.
According to Balls-Berry, socioeconomic barriers like transportation, time off work, and childcare related to trial participation must be removed. "These are real-world issues and yet many times researchers have not included these things in their budgets."
When asked to comment, a spokesperson for BIO, the world's largest biotech trade association, emailed the following statement:
"BIO believes that that our members' products and services should address the needs of a diverse population, and enhancing participation in clinical trials by a diverse patient population is a priority for BIO and our member companies. By investing in patient education to improve awareness of clinical trial opportunities, we can reduce disparities in clinical research to better reflect the country's changing demographics."
For my mother, the patient suffering from autoimmune disease, the need for broad participation in medical research is clear. "Without clinical trials, we would have less diagnosis and solutions to diseases," she says. "I think it's an underutilized resource."
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
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.