Abortions Before Fetal Viability Are Legal: Might Science and the Change on the Supreme Court Undermine That?
This article is part of the magazine, "The Future of Science In America: The Election Issue," co-published by LeapsMag, the Aspen Institute Science & Society Program, and GOOD.
Viability—the potential for a fetus to survive outside the womb—is a core dividing line in American law. For almost 50 years, the Supreme Court of the United States has struck down laws that ban all or most abortions, ruling that women's constitutional rights include choosing to end pregnancies before the point of viability. Once viability is reached, however, states have a "compelling interest" in protecting fetal life. At that point, states can choose to ban or significantly restrict later-term abortions provided states allow an exception to preserve the life or health of the mother.
This distinction between a fetus that could survive outside its mother's body, albeit with significant medical intervention, and one that could not, is at the heart of the court's landmark 1973 decision in Roe v. Wade. The framework of viability remains central to the country's abortion law today, even as some states have passed laws in the name of protecting women's health that significantly undermine Roe. Over the last 30 years, the Supreme Court has upheld these laws, which have the effect of restricting pre-viability abortion access, imposing mandatory waiting periods, requiring parental consent for minors, and placing restrictions on abortion providers.
Viability has always been a slippery notion on which to pin legal rights.
Today, the Guttmacher Institute reports that more than half of American women live in states whose laws are considered hostile to abortion, largely as a result of these intrusions on pre-viability abortion access. Nevertheless, the viability framework stands: while states can pass pre-viability abortion restrictions that (ostensibly) protect the health of the woman or that strike some kind a balance between women's rights and fetal life, it is only after viability that they can completely favor fetal life over the rights of the woman (with limited exceptions when the woman's life is threatened). As a result, judges have struck down certain states' so-called heartbeat laws, which tried to prohibit abortions after detection of a fetal heartbeat (as early as six weeks of pregnancy). Bans on abortion after 12 or 15 weeks' gestation have also been reversed.
Now, with a new Supreme Court Justice expected to be hostile to abortion rights, advances in the care of preterm babies and ongoing research on artificial wombs suggest that the point of viability is already sooner than many assume and could soon be moved radically earlier in gestation, potentially providing a legal basis for earlier and earlier abortion bans.
Viability has always been a slippery notion on which to pin legal rights. It represents an inherently variable and medically shifting moment in the pregnancy timeline that the Roe majority opinion declined to firmly define, noting instead that "[v]iability is usually placed at about seven months (28 weeks) but may occur earlier, even at 24 weeks." Even in 1977, this definition was an optimistic generalization. Every baby is different, and while some 28-week infants born the year Roe was decided did indeed live into adulthood, most died at or shortly after birth. The prognosis for infants born at 24 weeks was much worse.
Today, a baby born at 28 weeks' gestation can be expected to do much better, largely due to the development of surfactant treatment in the early 1990s to help ease the air into babies' lungs. Now, the majority of 24-week-old babies can survive, and several very premature babies, born just shy of 22 weeks' gestation, have lived into childhood. All this variability raises the question: Should the law take a very optimistic, if largely unrealistic, approach to defining viability and place it at 22 weeks, even though the overall survival rate for those preemies remains less than 10% today? Or should the law recognize that keeping a premature infant alive requires specialist care, meaning that actual viability differs not just pregnancy-to-pregnancy but also by healthcare facility and from country to country? A 24-week premature infant born in a rural area or in a developing nation may not be viable as a practical matter, while one born in a major U.S. city with access to state-of-the-art care has a greater than 70% chance of survival. Just as some extremely premature newborns survive, some full-term babies die before, during, or soon after birth, regardless of whether they have access to advanced medical care.
To be accurate, viability should be understood as pregnancy-specific and should take into account the healthcare resources available to that woman. But state laws can't capture this degree of variability by including gestation limits in their abortion laws. Instead, many draw a somewhat arbitrary line at 22, 24, or 28 weeks' gestation, regardless of the particulars of the pregnancy or the medical resources available in that state.
As variable and resource-dependent as viability is today, science may soon move that point even earlier. Ectogenesis is a term coined in 1923 for the growth of an organism outside the body. Long considered science fiction, this technology has made several key advances in the past few years, with scientists announcing in 2017 that they had successfully gestated premature lamb fetuses in an artificial womb for four weeks. Currently in development for use in human fetuses between 22 and 23 weeks' gestation, this technology will almost certainly seek to push viability earlier in pregnancy.
Ectogenesis and other improvements in managing preterm birth deserve to be celebrated, offering new hope to the parents of very premature infants. But in the U.S., and in other nations whose abortion laws are fixed to viability, these same advances also pose a threat to abortion access. Abortion opponents have long sought to move the cutoff for legal abortions, and it is not hard to imagine a state prohibiting all abortions after 18 or 20 weeks by arguing that medical advances render this stage "the new viability," regardless of whether that level of advanced care is available to women in that state. If ectogenesis advances further, the limit could be moved to keep pace.
The Centers for Disease Control and Prevention reports that over 90% of abortions in America are performed at or before 13 weeks, meaning that in the short term, only a small number women would be affected by shifting viability standards. Yet these women are in difficult situations and deserve care and consideration. Research has shown that women seeking later terminations often did not recognize that they were pregnant or had their dates quite wrong, while others report that they had trouble accessing a termination earlier in pregnancy, were afraid to tell their partner or parents, or only recently received a diagnosis of health problems with the fetus.
Shifts in viability over the past few decades have already affected these women, many of whom report struggling to find a provider willing to perform a termination at 18 or 20 weeks out of concern that the woman may have her dates wrong. Ever-earlier gestational limits would continue this chilling effect, making doctors leery of terminating a pregnancy that might be within 2–4 weeks of each new ban. Some states' existing gestational limits on abortion are also inconsistent with prenatal care, which includes genetic testing between 12 and 20 weeks' gestation, as well as an anatomy scan to check the fetus's organ development performed at approximately 20 weeks. If viability moves earlier, prenatal care will be further undermined.
Perhaps most importantly, earlier and earlier abortion bans are inconsistent with the rights and freedoms on which abortion access is based, including recognition of each woman's individual right to bodily integrity and decision-making authority over her own medical care. Those rights and freedoms become meaningless if abortion bans encroach into the weeks that women need to recognize they are pregnant, assess their options, seek medical advice, and access appropriate care. Fetal viability, with its shifting goalposts, isn't the best framework for abortion protection in light of advancing medical science.
Ideally, whether to have an abortion would be a decision that women make in consultation with their doctors, free of state interference. The vast majority of women already make this decision early in pregnancy; the few who come to the decision later do so because something has gone seriously wrong in their lives or with their pregnancies. If states insist on drawing lines based on historical measures of viability, at 24 or 26 or 28 weeks, they should stick with those gestational limits and admit that they no longer represent actual viability but correspond instead to some form of common morality about when the fetus has a protected, if not absolute, right to life. Women need a reasonable amount of time to make careful and informed decisions about whether to continue their pregnancies precisely because these decisions have a lasting impact on their bodies and their lives. To preserve that time, legislators and the courts should decouple abortion rights from ectogenesis and other advances in the care of extremely premature infants that move the point of viability ever earlier.
[Editor's Note: This article was updated after publication to reflect Amy Coney Barrett's confirmation. To read other articles in this special magazine issue, visit the e-reader version.]
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.