But because this is a novel disease, scientists and public health authorities are learning in real time—and more than a year and a half in, knowledge around key topics like immunity and long covid is still evolving. Scientists are often looking for answers at the same time the public is, but that’s not always clear to ordinary people, who may expect immediate and authoritative information.
“One of the things [public health authorities] weren’t necessarily doing that we need to see moving forward is actually communicating about the uncertainty,” says Renée DiResta, technical research manager at the Stanford Internet Observatory.
This lack of clarity—and sometimes the conflict—in public health messages can filter down to the press and create a vacuum where misleading or unverified information can fester and spread, DiResta says.
“That void can be filled by anyone with an opinion,” she adds.
All those conflicting messages, combined with the reality of slow scientific timelines, can exacerbate distrust. Instead of seeing changes in official guidance as signs that health authorities are responding to new data responsibly, it‘s easy for the public to believe that those authorities and the media had it wrong again—for example, when the CDC changed its mask guidelines. Politically motivated actors exploit that distrust. Sloppy headlines and misleading tweets by reputable news outlets, or journalists’ predictions that age poorly, can be repurposed into ”gotcha” memes that hyperpartisan influencers use to continue chipping away at trust in the media.
“Entities like Newsmax will take any opportunity to find a misreported or changed fact from a CNN broadcast,” DiResta says.
Public health officials (and the reporters covering what officials say and do) need a better system of communicating what we don’t yet know and explaining that guidance could change on the basis of new information. DiResta has argued for a Wikipedia-like approach to public health, where the evolution of scientific knowledge and debate is public and transparent, and a wide range of experts can contribute what they know. “It is never going to go back to the old way, where they make some determination in some back room and present a unified consensus to a trusting public,” she says. “That model is over.”
“If journalists spent less time on this day-to-day, blow-by-blow, and more time developing these complex and nuanced stories, we will be doing a much greater public service.”
Erika Check Hayden, UC Santa Cruz
We already see that kind of scientific back-and-forth play out on social media between researchers, public health experts, and doctors. Erika Check Hayden, a science journalist and director of the science communication program at the University of California, Santa Cruz, says that journalists need to remember to do their due diligence with this increased access to scientific deliberation.
“It can be informative, from a journalist’s perspective, if you understand [how experts] are working out what is going on,” she says. “What’s unhelpful is if you latch on to that at any given moment and portray it as some sort of conclusion.”
That’s good advice for the average reader, too.
Focus on what’s most useful
So how can you find trustworthy news that feels relevant to your life? One option is to keep an eye out for sources, especially local ones, that don’t exclusively focus on blow-by-blow coverage. Reporting that contextualizes the daily numbers you see is likely more helpful than an endless series of stories that simply rattle off the top-line data.
South Side Weekly—a nonprofit newspaper based in Chicago—offers a model for something different. The Weekly covers the South Side of Chicago, a majority nonwhite area. The largely volunteer newspaper produced the ChiVaxBot, an automated Twitter account that shares two maps side by side each day: covid-19 vaccination rates by zip code and covid-19 death rates by zip code. Instead of showing a snapshot of the data on one day, the daily updates demonstrated a pattern over time. Because of this consistent, slow tracking, the bot sounded the alarm on vaccine disparities: Black and Latino areas showed high deaths but low rates of vaccinations, a situation that continues to this day.
The technology would likely be used first on infants born at 22 or 23 weeks who don’t have many other options. “You don’t want to put an infant on this device who would otherwise do well with conventional therapy,” Mychaliska says. At 22 weeks gestation, babies are tiny, often weighing less than a pound. And their lungs are still developing. When researchers looked at babies born between 2013 and 2018, survival among those who were resuscitated at 22 weeks was 30%. That number rose to nearly 56% at 23 weeks. And babies born at that stage who do survive have an increased risk of neurodevelopmental problems, cerebral palsy, mobility problems, hearing impairments, and other disabilities.
Selecting the right participants will be tricky. Some experts argue that gestational age shouldn’t be the only criteria. One complicating factor is that prognosis varies widely from center to center, and it’s improving as hospitals learn how best to treat these preemies. At the University of Iowa Stead Family Children’s Hospital, for example, survival rates are much higher than average: 64% for babies born at 22 weeks. They’ve even managed to keep a handful of infants born at 21 weeks alive. “These babies are not a hopeless case. They very much can survive. They very much can thrive if you are managing them appropriately,” says Brady Thomas, a neonatologist at Stead. “Are you really going to make that much of a bigger impact by adding in this technology, and what risks might exist to those patients as you’re starting to trial it?”
Prognosis also varies widely from baby to baby depending on a variety of factors. “The girls do better than the boys. The bigger ones do better than the smaller ones,” says Mark Mercurio, a neonatologist and pediatric bioethicist at the Yale School of Medicine. So “how bad does the prognosis with current therapy need to be to justify use of an artificial womb?” That’s a question Mercurio would like to see answered.
What are the risks?
One ever-present concern in the tiniest babies is brain bleeds. “That’s due to a number of factors—a combination of their brain immaturity, and in part associated with the treatment that we provide,” Mychaliska says. Babies in an artificial womb would need to be on a blood thinner to prevent clots from forming where the tubes enter the body. “I believe that places a premature infant at very high risk for brain bleeding,” he says.
And it’s not just about the baby. To be eligible for EXTEND, infants must be delivered via cesarean section, which puts the pregnant person at higher risk for infection and bleeding. Delivery via a C-section can also have an impact on future pregnancies.
So if it works, could babies be grown entirely outside the womb?
Not anytime soon. Maybe not ever. In a paper published in 2022, Flake and his colleagues called this scenario “a technically and developmentally naive, yet sensationally speculative, pipe dream.” The problem is twofold. First, fetal development is a carefully choreographed process that relies on chemical communication between the pregnant parent’s body and the fetus. Even if researchers understood all the factors that contribute to fetal development—and they don’t—there’s no guarantee they could recreate those conditions.
The second issue is size. The artificial womb systems being developed require doctors to insert a small tube into the infant’s umbilical cord to deliver oxygenated blood. The smaller the umbilical cord, the more difficult this becomes.
What are the ethical concerns?
In the near term, there are concerns about how to ensure that researchers are obtaining proper informed consent from parents who may be desperate to save their babies. “This is an issue that comes up with lots of last-chance therapies,” says Vardit Ravitsky, a bioethicist and president of the Hastings Center, a bioethics research institute.
Last week, Elon Musk made the bold assertion that sticking electrodes in people’s heads is going to lead to a huge increase in the rate of data transfer out of, and into, human brains.
The occasion of Musk’s post was the announcement by Neuralink, his brain-computer interface company, that it was officially seeking the first volunteer to receive an implant that contains more than twice the number of electrodes than previous versions to collect more data from more nerve cells.
The entrepreneur mentioned a long-term goal of vastly increasing “bandwidth” between people, or people and machines, by a factor of 1,000 or more. But what does he mean, and is it even possible? Read the full story.
—Antonio Regalado
This story is from The Checkup, MIT Technology Review’s weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
Everything you need to know about artificial wombs
Earlier this month, US Food and Drug Administration advisors met to discuss how to move research on artificial wombs from animals into humans.
These medical devices are designed to give extremely premature infants a bit more time to develop in a womb-like environment before entering the outside world. They have been tested with hundreds of lambs (and some piglets), but animal models can’t fully predict how the technology will work for humans.
The reason we can’t just wish away or “fix” complexity is that every solution—whether it’s a technology or methodology—redistributes complexity in some way. Solutions reorganize problems. When microservices emerged (a software architecture approach where an application or system is composed of many smaller parts), they seemingly solved many of the maintenance and development challenges posed by monolithic architectures (where the application is one single interlocking system). However, in doing so microservices placed new demands on engineering teams; they require greater maturity in terms of practices and processes. This is one of the reasons why we cautioned people against what we call “microservice envy” in a 2018 edition of the Technology Radar, with CTO Rebecca Parsons writing that microservices would never be recommended for adoption on Technology Radar because “not all organizations are microservices-ready.” We noticed there was a tendency to look to adopt microservices simply because it was fashionable.
This doesn’t mean the solution is poor or defective. It’s more that we need to recognize the solution is a tradeoff. At Thoughtworks, we’re fond of saying “it depends” when people ask questions about the value of a certain technology or approach. It’s about how it fits with your organization’s needs and, of course, your ability to manage its particular demands. This is an example of essential complexity in tech—it’s something that can’t be removed and which will persist however much you want to get to a level of simplicity you find comfortable.
In terms of microservices, we’ve noticed increasing caution about rushing to embrace this particular architectural approach. Some of our colleagues even suggested the term “monolith revivalists” to describe those turning away from microservices back to monolithic software architecture. While it’s unlikely that the software world is going to make a full return to monoliths, frameworks like Spring Modulith—a framework that helps developers structure code in such a way that it becomes easier to break apart a monolith into smaller microservices when needed—suggest that practitioners are becoming more keenly aware of managing the tradeoffs of different approaches to building and maintaining software.
Supporting practitioners with concepts and tools
Because technical solutions have a habit of reorganizing complexity, we need to carefully attend to how this complexity is managed. Failing to do so can have serious implications for the productivity and effectiveness of engineering teams. At Thoughtworks we have a number of concepts and approaches that we use to manage complexity. Sensible defaults, for instance, are starting points for a project or piece of work. They’re not things that we need to simply embrace as a rule, but instead practices and tools that we collectively recognize are effective for most projects. They give individuals and teams a baseline to make judgements about what might be done differently.
One of the benefits of sensible defaults is that they can guard you against the allure of novelty and hype. As interesting or exciting as a new technology might be, sensible defaults can anchor you in what matters to you. This isn’t to say that new technologies like generative AI shouldn’t be treated with enthusiasm and excitement—some of our teams have been experimenting with these tools and seen impressive results—but instead that adopting new tools needs to be done in a way that properly integrates with the way you work and what you want to achieve. Indeed, there are a wealth of approaches to GenAI, from high profile tools like ChatGPT to self-hosted LLMs. Using GenAI effectively is as much a question of knowing the right way to implement for you and your team as it is about technical expertise.
Interestingly, the tools that can help us manage complexity aren’t necessarily new. One thing that came up in the latest edition of Technology Radar was something called risk-based failure modeling, a process used to understand the impact, likelihood and ability of detecting the various ways that a system can fail. This has origins in failure modes and effects analysis (FMEA), a practice that dates back to the period following World War II, used in complex engineering projects in fields such as aerospace. This signals that there are some challenges that endure; while new solutions will always emerge to combat them, we should also be comfortable looking to the past for tools and techniques.
Learning to live with complexity
McKinsey’s argument that the productivity of development teams can be successfully measured caused a stir across the software engineering landscape. While having the right metrics in place is certainly important, prioritizing productivity in our thinking can cause more problems than it solves when it comes to complex systems and an ever-changing landscape of solutions. Technology Radar called this out with an edition with the theme, “How productive is measuring productivity?”This highlighted the importance of focusing on developer experience with the help of tools like DX DevEx 360.
Focusing on productivity in the way McKinsey suggests can cause us to mistakenly see coding as the “real” work of software engineering, overlooking things like architectural decisions, tests, security analysis, and performance monitoring. This is risky—organizations that adopt such a view will struggle to see tangible benefits from their digital projects. This is why the key challenge in software today is embracing complexity; not treating it as something to be minimized at all costs but a challenge that requires thoughtfulness in processes, practices, and governance. The key question is whether the industry realizes this.
This content was produced by Thoughtworks. It was not written by MIT Technology Review’s editorial staff.
