Who Qualifies as an “Expert” And How Can We Decide Who Is Trustworthy?
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.
Expertise is a slippery concept. Who has it, who claims it, and who attributes or yields it to whom is a culturally specific, sociological process. During the COVID-19 pandemic, we have witnessed a remarkable emergence of legitimate and not-so-legitimate scientists publicly claiming or being attributed to have academic expertise in precisely my field: infectious disease epidemiology. From any vantage point, it is clear that charlatans abound out there, garnering TV coverage and hundreds of thousands of Twitter followers based on loud opinions despite flimsy credentials. What is more interesting as an insider is the gradient of expertise beyond these obvious fakers.
A person's expertise is not a fixed attribute; it is a hierarchical trait defined relative to others. Despite my protestations, I am the go-to expert on every aspect of the pandemic to my family. To a reporter, I might do my best to answer a question about the immune response to SARS-CoV-2, noting that I'm not an immunologist. Among other academic scientists, my expertise is more well-defined as a subfield of epidemiology, and within that as a particular area within infectious disease epidemiology. There's a fractal quality to it; as you zoom in on a particular subject, a differentiation of expertise emerges among scientists who, from farther out, appear to be interchangeable.
We all have our scientific domain and are less knowledgeable outside it, of course, and we are often asked to comment on a broad range of topics. But many scientists without a track record in the field have become favorites among university administrators, senior faculty in unrelated fields, policymakers, and science journalists, using institutional prestige or social connections to promote themselves. This phenomenon leads to a distorted representation of science—and of academic scientists—in the public realm.
Trustworthy experts will direct you to others in their field who know more about particular topics, and will tend to be honest about what is and what isn't "in their lane."
Predictably, white male voices have been disproportionately amplified, and men are certainly over-represented in the category of those who use their connections to inappropriately claim expertise. Generally speaking, we are missing women, racial minorities, and global perspectives. This is not only important because it misrepresents who scientists are and reinforces outdated stereotypes that place white men in the Global North at the top of a credibility hierarchy. It also matters because it can promote bad science, and it passes over scientists who can lend nuance to the scientific discourse and give global perspectives on this quintessentially global crisis.
Also at work, in my opinion, are two biases within academia: the conflation of institutional prestige with individual expertise, and the bizarre hierarchy among scientists that attributes greater credibility to those in quantitative fields like physics. Regardless of mathematical expertise or institutional affiliation, lack of experience working with epidemiological data can lead to over-confidence in the deceptively simple mathematical models that we use to understand epidemics, as well as the inappropriate use of uncertain data to inform them. Prominent and vocal scientists from different quantitative fields have misapplied the methods of infectious disease epidemiology during the COVID-19 pandemic so far, creating enormous confusion among policymakers and the public. Early forecasts that predicted the epidemic would be over by now, for example, led to a sense that epidemiological models were all unreliable.
Meanwhile, legitimate scientific uncertainties and differences of opinion, as well as fundamentally different epidemic dynamics arising in diverse global contexts and in different demographic groups, appear in the press as an indistinguishable part of this general chaos. This leads many people to question whether the field has anything worthwhile to contribute, and muddies the facts about COVID-19 policies for reducing transmission that most experts agree on, like wearing masks and avoiding large indoor gatherings.
So how do we distinguish an expert from a charlatan? I believe a willingness to say "I don't know" and to openly describe uncertainties, nuances, and limitations of science are all good signs. Thoughtful engagement with questions and new ideas is also an indication of expertise, as opposed to arrogant bluster or a bullish insistence on a particular policy strategy regardless of context (which is almost always an attempt to hide a lack of depth of understanding). Trustworthy experts will direct you to others in their field who know more about particular topics, and will tend to be honest about what is and what isn't "in their lane." For example, some expertise is quite specific to a given subfield: epidemiologists who study non-infectious conditions or nutrition, for example, use different methods from those of infectious disease experts, because they generally don't need to account for the exponential growth that is inherent to a contagion process.
Academic scientists have a specific, technical contribution to make in containing the COVID-19 pandemic and in communicating research findings as they emerge. But the liminal space between scientists and the public is subject to the same undercurrents of sexism, racism, and opportunism that society and the academy have always suffered from. Although none of the proxies for expertise described above are fool-proof, they are at least indicative of integrity and humility—two traits the world is in dire need of at this moment in history.
[Editor's Note: To read other articles in this special magazine issue, visit the beautifully designed 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.