Americans Fell for a Theranos-Style Scam 100 Years Ago. Will We Ever Learn?
The huckster understands what people want – an easy route to good health -- and figures out just how to provide it as long as no one asks too many questions.
"Americans are very much prone to this sort of thinking: Give me a pill or give me a magical bean that can make me lose weight!"
The keys to success: Hoopla, fancy technology, and gullibility. And oh yes, one more thing: a blood sample. Well, lots and lots of blood samples. Every testing fee counts.
Sound familiar? It could be the story of the preternaturally persuasive Elizabeth Holmes, the disgraced founder of Theranos who stands accused of perpetrating a massive blood-testing fraud. But this is a different story from a different time, one that dates back 100 years but sounds almost like it could unfold on the front page of The Wall Street Journal today.
The main difference: Back then, watchdogs thought they'd be able to vanquish fake medicine and scam science. Fat chance, it turned out. It seems like we're more likely to lose-weight-quick than make much of a dent into quackery and health fraud.
Why? Have we learned anything at all over the past century? As we sweep into a new decade, experts says we're not as advanced as we'd like to think. But the fight against fraud and fakery continues.
Quackery: As American As America Itself
In the 17th century, British healers of questionable reputation got a new name -- "quack," from the Dutch word "quacksalver," which originally referred to someone who treats others with home remedies but developed a new meaning along the lines of "charlatan." And these quacks got a new place to sell their wares: the American colonies.
By 1692, a Boston newspaper advertised a patent medicine that promised to cure "the Griping of the Guts, and the Wind Cholick" and – for good measure – "preventeth that woeful Distemper of the Dry Belly Ach." A couple centuries later, the most famous woman in the United States wasn't a first lady or feminist but a hawker of nostrums named Lydia Estes Pinkham whose "vegetable compound" promised to banish "female complaints." One advertisement suggested that the "sure cure" would have saved the life of a Connecticut clergyman whose wife killed him after suffering from feminine maladies for 16 years.
By the early 20th century, Americans were fascinated by electricity and radiation, and both healers and hucksters embraced the new high-tech era. Men with flagging libidos, for example, could irradiate their private parts with the radioactive Radiendocrinator or buy battery-powered electric belts equipped with dangling bits to supercharge their, um, dangling bits.
The Rise of the Radio Wave 'Cure'
Enter radionics, the (supposed) science of better health via radio waves. The idea was that "healthy people radiate healthy energy," and sickness could be reversed through diagnosis and re-tuning, write Dr. Lydia Kang and Nate Pedersen in their 2017 book "Quackery: A Brief History of the Worst Ways to Cure Everything."
Detecting illness and fixing it required machinery -- Dynamizers, Radioclasts and Oscillocasts – that could cost hundreds of dollars each. Thousands of physicians bought them. Fortunately, they could work remotely, for a fee. The worried-and-potentially-unwell just needed to send a blood sample and, of course, a personal check.
Sting operations revealed radionics to be bogus. A skeptic sent a blood sample to one radionics practitioner in Albuquerque who reported back with news of an infected fallopian tube. In fact, the blood sample came from a male guinea pig. As an American Medical Association leader reported, the guinea pig "had shown no female characteristics up to that time, and a postmortem examination yielded no evidence of ladylike attributes."
When Quackery Refused to Yield
The rise of bogus medical technology in the early 20th century spawned a watchdog industry as organizations like the American Medical Association swept into action, said medical historian Eric Boyle, author of 2012's "Quack Medicine: A History of Combating Health Fraud in Twentieth-Century America."
"When quackery was recognized as a major problem, the people who campaigned for its demise were confident that they could get rid of it," he said. "A lot of people believed that increased education, the truths of science, and laws designed to protect consumers would ultimately drive quackery from the marketplace. And then throughout the century, as modern medicine developed, and more effectively treated one disease after another, many observers remained confident in that prediction."
There's a bid to "flood the information highway with truth to turn the storm of fake promotional stuff into a trickle."
But fake medicine persisted as Americans continued their quest to get- healthy-quick… or get-rich-quick by promising to help others to get- healthy-quick. Even radionics refused to die. It's still around in various forms. And, as the Theranos scandal reveals, we're still hoping our blood can offer the keys to longevity and good health.
Why Do We Still Fall for Scams?
In our own era, the Theranos company rose to prominence when founder and CEO Elizabeth Holmes convinced journalists and investors that she'd found a way to cheaply test drops of blood for hundreds of conditions. Then it all fell apart, famously, when the world learned that the technology didn't work. The company has folded, and Holmes faces a federal trial on fraud charges this year.
"There were a lot of prominent, very smart people who bought into the myth of Elizabeth Holmes," a former employee told "60 Minutes," even though the blood tests never actually worked as advertised.
Shouldn't "prominent, very smart people" know better? "People are gullible," said Dr. Stephen Barrett, a psychiatrist and leading quack-buster who runs the QuackWatch website. But there's more to the story. According to him, we're uniquely vulnerable as individuals to bogus medicine.
Scam artists specifically pinpoint their target audiences, such as "smart people," desperate people and alienated people, he said.
Smart people, for example, might be overconfident about their ability to detect fraud and fall for bogus medicine. Alienated people may distrust the establishment, whether it's the medical field or government watchdogs, and be more receptive to alternative sources of information.
Dr. Barrett also points a finger at magical thinking, which comes in different forms. It could mean a New Age-style belief that our minds can control the world around us. Or, as professional quack-buster Alex Berezow said, it could refer to "our cultural obsession with quick fixes."
"Americans are very much prone to this sort of thinking: Give me a pill or give me a magical bean that can make me lose weight! But complex problems need complex solutions," said Berezow, a microbiologist who debunks junk science in his job as a spokesman for the American Council on Science & Health.
American mistrust of expertise makes matters worse, he said. "When I tell people they need to get vaccinated, I'm called a shill for the pharmaceutical industry," he said. "If I say dietary supplements generally don't work, I'm a shill for doctors who want to keep people sick."
What can ordinary citizens do to protect themselves from fake medicine? "You have to have a healthy skepticism of everything," Berezow said. "When you come across something new, is someone trying to take advantage of you? It's a horrible way to think about the world, but there's some truth to it."
"Like any chronic disease, we will have to live with it while we do our best to fight it."
The government and experts have their own roles to play via regulation and education, respectively. For all the criticism it gets, the Food & Drug Administration does serve as a bulwark against fakery in prescription medicine. And while celebrities like Gwyneth "Goop" Paltrow hawk countless questionable medical products on the Internet, scientists and physicians are fighting back by using social media as a tool to promote the truth. There's a bid to "flood the information highway with truth to turn the storm of fake promotional stuff into a trickle," said Dr. Randi Hutter Epstein, a writer in residence at Yale School of Medicine and author of 2018's "Aroused: The History of Hormones and How They Control Just About Everything."
What's next? Like death, taxes and Cher, charlatans are likely to always be with us. Boyle quoted the late William Jarvis, a pioneering quack-buster in the late 20th century who believed health fraud would never be eradicated: "Like any chronic disease, we will have to live with it while we do our best to fight it."
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.