How Can We Decide If a Biomedical Advance Is Ethical?
"All fixed, fast-frozen relations, with their train of ancient and venerable prejudices and opinions, are swept away, all new-formed ones become antiquated before they can ossify. All that is solid melts into air, all that is holy is profaned…"
On July 25, 1978, Louise Brown was born in Oldham, England, the first human born through in vitro fertilization, through the work of Patrick Steptoe, a gynecologist, and Robert Edwards, a physiologist. Her birth was greeted with strong (though not universal) expressions of ethical dismay. Yet in 2016, the latest year for which we have data, nearly two percent of the babies born in the United States – and around the same percentage throughout the developed world – were the result of IVF. Few, if any, think of these children as unnatural, monsters, or freaks or of their parents as anything other than fortunate.
How should we view Dr. He today, knowing that the world's eventual verdict on the ethics of biomedical technologies often changes?
On November 25, 2018, news broke that Chinese scientist, Dr. He Jiankui, claimed to have edited the genomes of embryos, two of whom had recently become the new babies, Lulu and Nana. The response was immediate and overwhelmingly negative.
Times change. So do views. How will Dr. He be viewed in 40 years? And, more importantly, how should we view him today, knowing that the world's eventual verdict on the ethics of biomedical technologies often changes? And when what biomedicine can do changes with vertiginous frequency?
How to determine what is and isn't ethical is above my pay grade. I'm a simple law professor – I can't claim any deeper insight into how to live a moral life than the millennia of religious leaders, philosophers, ethicists, and ordinary people trying to do the right thing. But I can point out some ways to think about these questions that may be helpful.
First, consider two different kinds of ethical commands. Some are quite specific – "thou shalt not kill," for example. Others are more general – two of them are "do unto others as you would have done to you" or "seek the greatest good for the greatest number."
Biomedicine in the last two centuries has often surprised us with new possibilities, situations that cultures, religions, and bodies of ethical thought had not previously had to consider, from vaccination to anesthesia for women in labor to genome editing. Sometimes these possibilities will violate important and deeply accepted precepts for a group or a person. The rise of blood transfusions around World War I created new problems for Jehovah's Witnesses, who believe that the Bible prohibits ingesting blood. The 20th century developments of artificial insemination and IVF both ran afoul of Catholic doctrine prohibiting methods other than "traditional" marital intercourse for conceiving children. If you subscribe to an ethical or moral code that contains prohibitions that modern biomedicine violates, the issue for you is stark – adhere to those beliefs or renounce them.
If the harms seem to outweigh the benefits, it's easy to conclude "this is worrisome."
But many biomedical changes violate no clear moral teachings. Is it ethical or not to edit the DNA of embryos? Not surprisingly, the sacred texts of various religions – few of which were created after, at the latest, the early 19th century, say nothing specific about this. There may be hints, precedents, leanings that could argue one way or another, but no "commandments." In that case, I recommend, at least as a starting point, asking "what are the likely consequences of these actions?"
Will people be, on balance, harmed or helped by them? "Consequentialist" approaches, of various types, are a vast branch of ethical theories. Personally I find a completely consequentialist approach unacceptable – I could not accept, for example, torturing an innocent child even in order to save many lives. But, in the absence of a clear rule, looking at the consequences is a great place to start. If the harms seem to outweigh the benefits, it's easy to conclude "this is worrisome."
Let's use that starting place to look at a few bioethical issues. IVF, for example, once proven (relatively) safe seems to harm no one and to help many, notably the more than 8 million children worldwide born through IVF since 1978 – and their 16 million parents. On the other hand, giving unknowing, and unconsenting, intellectually disabled children hepatitis A harmed them, for an uncertain gain for science. And freezing the heads of the dead seems unlikely to harm anyone alive (except financially) but it also seems almost certain not to benefit anyone. (Those frozen dead heads are not coming back to life.)
Now let's look at two different kinds of biomedical advances. Some are controversial just because they are new; others are controversial because they cut close to the bone – whether or not they violate pre-established ethical or moral norms, they clearly relate to them.
Consider anesthesia during childbirth. When first used, it was controversial. After all, said critics, in Genesis, the Bible says God told Eve, "I will greatly multiply Your pain in childbirth, In pain you will bring forth children." But it did not clearly prohibit pain relief and from the advent of ether on, anesthesia has been common, though not universal, in childbirth in western societies. The pre-existing ethical precepts were not clear and the consequences weighed heavily in favor of anesthesia. Similarly, vaccination seems to violate no deep moral principle. It was, and for some people, still is just strange, and unnatural. The same was true of IVF initially. Opposition to all of these has faded with time and familiarity. It has not disappeared – some people continue to find moral or philosophical problems with "unnatural" childbirth, vaccination, and IVF – but far fewer.
On the other hand, human embryonic stem cell research touches deeper issues. Human embryos are destroyed to make those stem cells. Reasonable people disagree on the moral status of the human embryo, and the moral weight of its destruction, but it does at least bring into play clear and broadly accepted moral precepts, such as "Thou shalt not kill." So, at the far side of an individual's time, does euthanasia. More exposure to, and familiarity with, these practices will not necessarily lead to broad acceptance as the objections involve more than novelty.
The first is "what would I do?" The second – what should my government, culture, religion allow or forbid?
Finally, all this ethical analysis must work at two levels. The first is "what would I do?" The second – what should my government, culture, religion allow or forbid? There are many things I would not do that I don't think should be banned – because I think other people may reasonably have different views from mine. I would not get cosmetic surgery, but I would not ban it – and will try not to think ill of those who choose it
So, how should we assess the ethics of new biomedical procedures when we know that society's views may change? More specifically, what should we think of He Jiankui's experiment with human babies?
First, look to see whether the procedure in question violates, at least fairly clearly, some rule in your ethical or moral code. If so, your choice may not be difficult. But if the procedure is unmentioned in your moral code, probably because it was inconceivable to the code's creators, examine the consequences of the act.
If the procedure is just novel, and not something that touches on important moral concerns, looking at the likely consequences may be enough for your ethical analysis –though it is always worth remembering that predicting consequences perfectly is impossible and predicting them well is never certain. If it does touch on morally significant issues, you need to think those issues through. The consequences may be important to your conclusions but they may not be determinative.
And, then, if you conclude that it is not ethical from your perspective, you need to take yet another step and consider whether it should be banned for people who do not share your perspective. Sometimes the answer will be yes – that psychopaths may not view murder as immoral does not mean we have to let them kill – but sometimes it will be no.
What does this say about He Jiankui's experiment? I have no qualms in condemning it, unequivocally. The potential risks to the babies grossly outweighed any benefits to them, and to science. And his secret work, against a near universal scientific consensus, privileged his own ethical conclusions without giving anyone else a vote, or even a voice.
But if, in ten or twenty years, genome editing of human embryos is shown to be safe (enough) and it is proposed to be used for good reasons – say, to relieve human suffering that could not be treated in other good ways – and with good consents from those directly involved as well as from the relevant society and government – my answer might well change. Yours may not. Bioethics is a process for approaching questions; it is not a set of universal answers.
This article opened with a quotation from the 1848 Communist Manifesto, referring to the dizzying pace of change from industrialization and modernity. You don't need to be a Marxist to appreciate that sentiment. Change – especially in the biosciences – keeps accelerating. How should we assess the ethics of new biotechnologies? The best we can, with what we know, at the time we inhabit. And, in the face of vast uncertainty, with humility.
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.
DNA gathered from animal poop helps protect wildlife
On the savannah near the Botswana-Zimbabwe border, elephants grazed contentedly. Nearby, postdoctoral researcher Alida de Flamingh watched and waited. As the herd moved away, she went into action, collecting samples of elephant dung that she and other wildlife conservationists would study in the months to come. She pulled on gloves, took a swab, and ran it all over the still-warm, round blob of elephant poop.
Sequencing DNA from fecal matter is a safe, non-invasive way to track and ultimately help protect over 42,000 species currently threatened by extinction. Scientists are using this DNA to gain insights into wildlife health, genetic diversity and even the broader environment. Applied to elephants, chimpanzees, toucans and other species, it helps scientists determine the genetic diversity of groups and linkages with other groups. Such analysis can show changes in rates of inbreeding. Populations with greater genetic diversity adapt better to changes and environmental stressors than those with less diversity, thus reducing their risks of extinction, explains de Flamingh, a postdoctoral researcher at the University of Illinois Urbana-Champaign.
Analyzing fecal DNA also reveals information about an animal’s diet and health, and even nearby flora that is eaten. That information gives scientists broader insights into the ecosystem, and the findings are informing conservation initiatives. Examples include restoring or maintaining genetic connections among groups, ensuring access to certain foraging areas or increasing diversity in captive breeding programs.
Approximately 27 percent of mammals and 28 percent of all assessed species are close to dying out. The IUCN Red List of threatened species, simply called the Red List, is the world’s most comprehensive record of animals’ risk of extinction status. The more information scientists gather, the better their chances of reducing those risks. In Africa, populations of vertebrates declined 69 percent between 1970 and 2022, according to the World Wildlife Fund (WWF).
“We put on sterile gloves and use a sterile swab to collect wet mucus and materials from the outside of the dung ball,” says Alida de Flamingh, a postdoctoral researcher at the University of Illinois Urbana-Champaign.
“When people talk about species, they often talk about ecosystems, but they often overlook genetic diversity,” says Christina Hvilsom, senior geneticist at the Copenhagen Zoo. “It’s easy to count (individuals) to assess whether the population size is increasing or decreasing, but diversity isn’t something we can see with our bare eyes. Yet, it’s actually the foundation for the species and populations.” DNA analysis can provide this critical information.
Assessing elephants’ health
“Africa’s elephant populations are facing unprecedented threats,” says de Flamingh, the postdoc, who has studied them since 2009. Challenges include ivory poaching, habitat destruction and smaller, more fragmented habitats that result in smaller mating pools with less genetic diversity. Additionally, de Flamingh studies the microbial communities living on and in elephants – their microbiomes – looking for parasites or dangerous microbes.
Approximately 415,000 elephants inhabit Africa today, but de Flamingh says the number would be four times higher without these challenges. The IUCN Red List reports African savannah elephants are endangered and African forest elephants are critically endangered. Elephants support ecosystem biodiversity by clearing paths that help other species travel. Their very footprints create small puddles that can host smaller organisms such as tadpoles. Elephants are often described as ecosystems’ engineers, so if they disappear, the rest of the ecosystem will suffer too.
There’s a process to collecting elephant feces. “We put on sterile gloves (which we change for each sample) and use a sterile swab to collect wet mucus and materials from the outside of the dung ball,” says de Flamingh. They rub a sample about the size of a U.S. quarter onto a paper card embedded with DNA preservation technology. Each card is air dried and stored in a packet of desiccant to prevent mold growth. This way, samples can be stored at room temperature indefinitely without the DNA degrading.
Earlier methods required collecting dung in bags, which needed either refrigeration or the addition of preservatives, or the riskier alternative of tranquilizing the animals before approaching them to draw blood samples. The ability to collect and sequence the DNA made things much easier and safer.
“Our research provides a way to assess elephant health without having to physically interact with elephants,” de Flamingh emphasizes. “We also keep track of the GPS coordinates of each sample so that we can create a map of the sampling locations,” she adds. That helps researchers correlate elephants’ health with geographic areas and their conditions.
Although de Flamingh works with elephants in the wild, the contributions of zoos in the United States and collaborations in South Africa (notably the late Professor Rudi van Aarde and the Conservation Ecology Research Unit at the University of Pretoria) were key in studying this method to ensure it worked, she points out.
Protecting chimpanzees
Genetic work with chimpanzees began about a decade ago. Hvilsom and her group at the Copenhagen Zoo analyzed DNA from nearly 1,000 fecal samples collected between 2003 and 2018 by a team of international researchers. The goal was to assess the status of the West African subspecies, which is critically endangered after rapid population declines. Of the four subspecies of chimpanzees, the West African subspecies is considered the most at-risk.
In total, the WWF estimates the numbers of chimpanzees inhabiting Africa’s forests and savannah woodlands at between 173,000 and 300,000. Poaching, disease and human-caused changes to their lands are their major risks.
By analyzing genetics obtained from fecal samples, Hvilsom estimated the chimpanzees’ population, ascertained their family relationships and mapped their migration routes.
“One of the threats is mining near the Nimba Mountains in Guinea,” a stronghold for the West African subspecies, Hvilsom says. The Nimba Mountains are a UNESCO World Heritage Site, but they are rich in iron ore, which is used to make the steel that is vital to the Asian construction boom. As she and colleagues wrote in a recent paper, “Many extractive industries are currently developing projects in chimpanzee habitat.”
Analyzing DNA allows researchers to identify individual chimpanzees more accurately than simply observing them, she says. Normally, field researchers would install cameras and manually inspect each picture to determine how many chimpanzees were in an area. But, Hvilsom says, “That’s very tricky. Chimpanzees move a lot and are fast, so it’s difficult to get clear pictures. Often, they find and destroy the cameras. Also, they live in large areas, so you need a lot of cameras.”
By analyzing genetics obtained from fecal samples, Hvilsom estimated the chimpanzees’ population, ascertained their family relationships and mapped their migration routes based upon DNA comparisons with other chimpanzee groups. The mining companies and builders are using this information to locate future roads where they won’t disrupt migration – a more effective solution than trying to build artificial corridors for wildlife.
“The current route cuts off communities of chimpanzees,” Hvilsom elaborates. That effectively prevents young adult chimps from joining other groups when the time comes, eventually reducing the currently-high levels of genetic diversity.
“The mining company helped pay for the genetics work,” Hvilsom says, “as part of its obligation to assess and monitor biodiversity and the effect of the mining in the area.”
Of 50 toucan subspecies, 11 are threatened or near-threatened with extinction because of deforestation and poaching.
Identifying toucan families
Feces aren't the only substance researchers draw DNA samples from. Jeffrey Coleman, a Ph.D. candidate at the University of Texas at Austin relies on blood tests for studying the genetic diversity of toucans---birds species native to Central America and nearby regions. They live in the jungles, where they hop among branches, snip fruit from trees, toss it in the air and catch it with their large beaks. “Toucans are beautiful, charismatic birds that are really important to the ecosystem,” says Coleman.
Of their 50 subspecies, 11 are threatened or near-threatened with extinction because of deforestation and poaching. “When people see these aesthetically pleasing birds, they’re motivated to care about conservation practices,” he points out.
Coleman works with the Dallas World Aquarium and its partner zoos to analyze DNA from blood draws, using it to identify which toucans are related and how closely. His goal is to use science to improve the genetic diversity among toucan offspring.
Specifically, he’s looking at sections of the genome of captive birds in which the nucleotides repeat multiple times, such as AGATAGATAGAT. Called microsatellites, these consecutively-repeating sections can be passed from parents to children, helping scientists identify parent-child and sibling-sibling relationships. “That allows you to make strategic decisions about how to pair (captive) individuals for mating...to avoid inbreeding,” Coleman says.
Jeffrey Coleman is studying the microsatellites inside the toucan genomes.
Courtesy Jeffrey Coleman
The alternative is to use a type of analysis that looks for a single DNA building block – a nucleotide – that differs in a given sequence. Called single nucleotide polymorphisms (SNPs, pronounced “snips”), they are very common and very accurate. Coleman says they are better than microsatellites for some uses. But scientists have already developed a large body of microsatellite data from multiple species, so microsatellites can shed more insights on relations.
Regardless of whether conservation programs use SNPs or microsatellites to guide captive breeding efforts, the goal is to help them build genetically diverse populations that eventually may supplement endangered populations in the wild. “The hope is that the ecosystem will be stable enough and that the populations (once reintroduced into the wild) will be able to survive and thrive,” says Coleman. History knows some good examples of captive breeding success.
The California condor, which had a total population of 27 in 1987, when the last wild birds were captured, is one of them. A captive breeding program boosted their numbers to 561 by the end of 2022. Of those, 347 of those are in the wild, according to the National Park Service.
Conservationists hope that their work on animals’ genetic diversity will help preserve and restore endangered species in captivity and the wild. DNA analysis is crucial to both types of efforts. The ability to apply genome sequencing to wildlife conservation brings a new level of accuracy that helps protect species and gives fresh insights that observation alone can’t provide.
“A lot of species are threatened,” Coleman says. “I hope this research will be a resource people can use to get more information on longer-term genealogies and different populations.”