Bad Actors Getting Your Health Data Is the FBI’s Latest Worry
In February 2015, the health insurer Anthem revealed that criminal hackers had gained access to the company's servers, exposing the personal information of nearly 79 million patients. It's the largest known healthcare breach in history.
FBI agents worry that the vast amounts of healthcare data being generated for precision medicine efforts could leave the U.S. vulnerable to cyber and biological attacks.
That year, the data of millions more would be compromised in one cyberattack after another on American insurers and other healthcare organizations. In fact, for the past several years, the number of reported data breaches has increased each year, from 199 in 2010 to 344 in 2017, according to a September 2018 analysis in the Journal of the American Medical Association.
The FBI's Edward You sees this as a worrying trend. He says hackers aren't just interested in your social security or credit card number. They're increasingly interested in stealing your medical information. Hackers can currently use this information to make fake identities, file fraudulent insurance claims, and order and sell expensive drugs and medical equipment. But beyond that, a new kind of cybersecurity threat is around the corner.
Mr. You and others worry that the vast amounts of healthcare data being generated for precision medicine efforts could leave the U.S. vulnerable to cyber and biological attacks. In the wrong hands, this data could be used to exploit or extort an individual, discriminate against certain groups of people, make targeted bioweapons, or give another country an economic advantage.
Precision medicine, of course, is the idea that medical treatments can be tailored to individuals based on their genetics, environment, lifestyle or other traits. But to do that requires collecting and analyzing huge quantities of health data from diverse populations. One research effort, called All of Us, launched by the U.S. National Institutes of Health last year, aims to collect genomic and other healthcare data from one million participants with the goal of advancing personalized medical care.
Other initiatives are underway by academic institutions and healthcare organizations. Electronic medical records, genetic tests, wearable health trackers, mobile apps, and social media are all sources of valuable healthcare data that a bad actor could potentially use to learn more about an individual or group of people.
"When you aggregate all of that data together, that becomes a very powerful profile of who you are," Mr. You says.
A supervisory special agent in the biological countermeasures unit within the FBI's weapons of mass destruction directorate, it's Mr. You's job to imagine worst-case bioterror scenarios and figure out how to prevent and prepare for them.
That used to mean focusing on threats like anthrax, Ebola, and smallpox—pathogens that could be used to intentionally infect people—"basically the dangerous bugs," as he puts it. In recent years, advances in gene editing and synthetic biology have given rise to fears that rogue, or even well-intentioned, scientists could create a virulent virus that's intentionally, or unintentionally, released outside the lab.
"If a foreign source, especially a criminal one, has your biological information, then they might have some particular insights into what your future medical needs might be and exploit that."
While Mr. You is still tracking those threats, he's been traveling around the country talking to scientists, lawyers, software engineers, cyber security professionals, government officials and CEOs about new security threats—those posed by genetic and other biological data.
Emerging threats
Mr. You says one possible situation he can imagine is the potential for nefarious actors to use an individual's sensitive medical information to extort or blackmail that person.
"If a foreign source, especially a criminal one, has your biological information, then they might have some particular insights into what your future medical needs might be and exploit that," he says. For instance, "what happens if you have a singular medical condition and an outside entity says they have a treatment for your condition?" You could get talked into paying a huge sum of money for a treatment that ends up being bogus.
Or what if hackers got a hold of a politician or high-profile CEO's health records? Say that person had a disease-causing genetic mutation that could affect their ability to carry out their job in the future and hackers threatened to expose that information. These scenarios may seem far-fetched, but Mr. You thinks they're becoming increasingly plausible.
On a wider scale, Kavita Berger, a scientist at Gryphon Scientific, a Washington, D.C.-area life sciences consulting firm, worries that data from different populations could be used to discriminate against certain groups of people, like minorities and immigrants.
For instance, the advocacy group Human Rights Watch in 2017 flagged a concerning trend in China's Xinjiang territory, a region with a history of government repression. Police there had purchased 12 DNA sequencers and were collecting and cataloging DNA samples from people to build a national database.
"The concern is that this particular province has a huge population of the Muslim minority in China," Ms. Berger says. "Now they have a really huge database of genetic sequences. You have to ask, why does a police station need 12 next-generation sequencers?"
Also alarming is the potential that large amounts of data from different groups of people could lead to customized bioweapons if that data ends up in the wrong hands.
Eleonore Pauwels, a research fellow on emerging cybertechnologies at United Nations University's Centre for Policy Research, says new insights gained from genomic and other data will give scientists a better understanding of how diseases occur and why certain people are more susceptible to certain diseases.
"As you get more and more knowledge about the genomic picture and how the microbiome and the immune system of different populations function, you could get a much deeper understanding about how you could target different populations for treatment but also how you could eventually target them with different forms of bioagents," Ms. Pauwels says.
Economic competitiveness
Another reason hackers might want to gain access to large genomic and other healthcare datasets is to give their country a leg up economically. Many large cyber-attacks on U.S. healthcare organizations have been tied to Chinese hacking groups.
"This is a biological space race and we just haven't woken up to the fact that we're in this race."
"It's becoming clear that China is increasingly interested in getting access to massive data sets that come from different countries," Ms. Pauwels says.
A year after U.S. President Barack Obama conceived of the Precision Medicine Initiative in 2015—later renamed All of Us—China followed suit, announcing the launch of a 15-year, $9 billion precision health effort aimed at turning China into a global leader in genomics.
Chinese genomics companies, too, are expanding their reach outside of Asia. One company, WuXi NextCODE, which has offices in Shanghai, Reykjavik, and Cambridge, Massachusetts, has built an extensive library of genomes from the U.S., China and Iceland, and is now setting its sights on Ireland.
Another Chinese company, BGI, has partnered with Children's Hospital of Philadelphia and Sinai Health System in Toronto, and also formed a collaboration with the Smithsonian Institute to sequence all species on the planet. BGI has built its own advanced genomic sequencing machines to compete with U.S.-based Illumina.
Mr. You says having access to all this data could lead to major breakthroughs in healthcare, such as new blockbuster drugs. "Whoever has the largest, most diverse dataset is truly going to win the day and come up with something very profitable," he says.
Some direct-to-consumer genetic testing companies with offices in the U.S., like Dante Labs, also use BGI to process customers' DNA.
Experts worry that China could race ahead the U.S. in precision medicine because of Chinese laws governing data sharing. Currently, China prohibits the exportation of genetic data without explicit permission from the government. Mr. You says this creates an asymmetry in data sharing between the U.S. and China.
"This is a biological space race and we just haven't woken up to the fact that we're in this race," he said in January at an American Society for Microbiology conference in Washington, D.C. "We don't have access to their data. There is absolutely no reciprocity."
Protecting your data
While Mr. You has been stressing the importance of data security to anyone who will listen, the National Academies of Sciences, Engineering, and Medicine, which makes scientific and policy recommendations on issues of national importance, has commissioned a study on "safeguarding the bioeconomy."
In the meantime, Ms. Berger says organizations that deal with people's health data should assess their security risks and identify potential vulnerabilities in their systems.
As for what individuals can do to protect themselves, she urges people to think about the different ways they're sharing healthcare data—such as via mobile health apps and wearables.
"Ask yourself, what's the benefit of sharing this? What are the potential consequences of sharing this?" she says.
Mr. You also cautions people to think twice before taking consumer DNA tests. They may seem harmless, he says, but at the end of the day, most people don't know where their genetic information is going. "If your genetic sequence is taken, once it's gone, it's gone. There's nothing you can do about 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.