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.]
Podcast: The Friday Five weekly roundup in health research
The Friday Five covers five stories in health research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Covered in this week's Friday Five:
- Sex differences in cancer
- Promising research on a vaccine for Lyme disease
- Using a super material for brain-like devices
- Measuring your immunity to Covid
- Reducing dementia risk with leisure activities
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.