Researchers Behaving Badly: Known Frauds Are "the Tip of the Iceberg"
Last week, the whistleblowers in the Paolo Macchiarini affair at Sweden's Karolinska Institutet went on the record here to detail the retaliation they suffered for trying to expose a star surgeon's appalling research misconduct.
Scientific fraud of the type committed by Macchiarini is rare, but studies suggest that it's on the rise.
The whistleblowers had discovered that in six published papers, Macchiarini falsified data, lied about the condition of patients and circumvented ethical approvals. As a result, multiple patients suffered and died. But Karolinska turned a blind eye for years.
Scientific fraud of the type committed by Macchiarini is rare, but studies suggest that it's on the rise. Just this week, for example, Retraction Watch and STAT together broke the news that a Harvard Medical School cardiologist and stem cell researcher, Piero Anversa, falsified data in a whopping 31 papers, which now have to be retracted. Anversa had claimed that he could regenerate heart muscle by injecting bone marrow cells into damaged hearts, a result that no one has been able to duplicate.
A 2009 study published in the Public Library of Science (PLOS) found that about two percent of scientists admitted to committing fabrication, falsification or plagiarism in their work. That's a small number, but up to one third of scientists admit to committing "questionable research practices" that fall into a gray area between rigorous accuracy and outright fraud.
These dubious practices may include misrepresentations, research bias, and inaccurate interpretations of data. One common questionable research practice entails formulating a hypothesis after the research is done in order to claim a successful premise. Another highly questionable practice that can shape research is ghost-authoring by representatives of the pharmaceutical industry and other for-profit fields. Still another is gifting co-authorship to unqualified but powerful individuals who can advance one's career. Such practices can unfairly bolster a scientist's reputation and increase the likelihood of getting the work published.
The above percentages represent what scientists admit to doing themselves; when they evaluate the practices of their colleagues, the numbers jump dramatically. In a 2012 study published in the Journal of Research in Medical Sciences, researchers estimated that 14 percent of other scientists commit serious misconduct, while up to 72 percent engage in questionable practices. While these are only estimates, the problem is clearly not one of just a few bad apples.
In the PLOS study, Daniele Fanelli says that increasing evidence suggests the known frauds are "just the 'tip of the iceberg,' and that many cases are never discovered" because fraud is extremely hard to detect.
Essentially everyone wants to be associated with big breakthroughs, and they may overlook scientifically shaky foundations when a major advance is claimed.
In addition, it's likely that most cases of scientific misconduct go unreported because of the high price of whistleblowing. Those in the Macchiarini case showed extraordinary persistence in their multi-year campaign to stop his deadly trachea implants, while suffering serious damage to their careers. Such heroic efforts to unmask fraud are probably rare.
To make matters worse, there are numerous players in the scientific world who may be complicit in either committing misconduct or covering it up. These include not only primary researchers but co-authors, institutional executives, journal editors, and industry leaders. Essentially everyone wants to be associated with big breakthroughs, and they may overlook scientifically shaky foundations when a major advance is claimed.
Another part of the problem is that it's rare for students in science and medicine to receive an education in ethics. And studies have shown that older, more experienced and possibly jaded researchers are more likely to fudge results than their younger, more idealistic colleagues.
So, given the steep price that individuals and institutions pay for scientific misconduct, what compels them to go down that road in the first place? According to the JRMS study, individuals face intense pressures to publish and to attract grant money in order to secure teaching positions at universities. Once they have acquired positions, the pressure is on to keep the grants and publishing credits coming in order to obtain tenure, be appointed to positions on boards, and recruit flocks of graduate students to assist in research. And not to be underestimated is the human ego.
Paolo Macchiarini is an especially vivid example of a scientist seeking not only fortune, but fame. He liberally (and falsely) claimed powerful politicians and celebrities, even the Pope, as patients or admirers. He may be an extreme example, but we live in an age of celebrity scientists who bring huge amounts of grant money and high prestige to the institutions that employ them.
The media plays a significant role in both glorifying stars and unmasking frauds. In the Macchiarini scandal, the media first lifted him up, as in NBC's laudatory documentary, "A Leap of Faith," which painted him as a kind of miracle-worker, and then brought him down, as in the January 2016 documentary, "The Experiments," which chronicled the agonizing death of one of his patients.
Institutions can also play a crucial role in scientific fraud by putting more emphasis on the number and frequency of papers published than on their quality. The whole course of a scientist's career is profoundly affected by something called the h-index. This is a number based on both the frequency of papers published and how many times the papers are cited by other researchers. Raising one's ranking on the h-index becomes an overriding goal, sometimes eclipsing the kind of patient, time-consuming research that leads to true breakthroughs based on reliable results.
Universities also create a high-pressured environment that encourages scientists to cut corners. They, too, place a heavy emphasis on attracting large monetary grants and accruing fame and prestige. This can lead them, just as it led Karolinska, to protect a star scientist's sloppy or questionable research. According to Dr. Andrew Rosenberg, who is director of the Center for Science and Democracy at the U.S.-based Union of Concerned Scientists, "Karolinska defended its investment in an individual as opposed to the long-term health of the institution. People were dying, and they should have outsourced the investigation from the very beginning."
Having institutions investigate their own practices is a conflict of interest from the get-go, says Rosenberg.
Scientists, universities, and research institutions are also not immune to fads. "Hot" subjects attract grant money and confer prestige, incentivizing scientists to shift their research priorities in a direction that garners more grants. This can mean neglecting the scientist's true area of expertise and interests in favor of a subject that's more likely to attract grant money. In Macchiarini's case, he was allegedly at the forefront of the currently sexy field of regenerative medicine -- a field in which Karolinska was making a huge investment.
The relative scarcity of resources intensifies the already significant pressure on scientists. They may want to publish results rapidly, since they face many competitors for limited grant money, academic positions, students, and influence. The scarcity means that a great many researchers will fail while only a few succeed. Once again, the temptation may be to rush research and to show it in the most positive light possible, even if it means fudging or exaggerating results.
Though the pressures facing scientists are very real, the problem of misconduct is not inevitable.
Intense competition can have a perverse effect on researchers, according to a 2007 study in the journal Science of Engineering and Ethics. Not only does it place undue pressure on scientists to succeed, it frequently leads to the withholding of information from colleagues, which undermines a system in which new discoveries build on the previous work of others. Researchers may feel compelled to withhold their results because of the pressure to be the first to publish. The study's authors propose that more investment in basic research from governments could alleviate some of these competitive pressures.
Scientific journals, although they play a part in publishing flawed science, can't be expected to investigate cases of suspected fraud, says the German science blogger Leonid Schneider. Schneider's writings helped to expose the Macchiarini affair.
"They just basically wait for someone to retract problematic papers," he says.
He also notes that, while American scientists can go to the Office of Research Integrity to report misconduct, whistleblowers in Europe have no external authority to whom they can appeal to investigate cases of fraud.
"They have to go to their employer, who has a vested interest in covering up cases of misconduct," he says.
Science is increasingly international. Major studies can include collaborators from several different countries, and he suggests there should be an international body accessible to all researchers that will investigate suspected fraud.
Ultimately, says Rosenberg, the scientific system must incorporate trust. "You trust co-authors when you write a paper, and peer reviewers at journals trust that scientists at research institutions like Karolinska are acting with integrity."
Without trust, the whole system falls apart. It's the trust of the public, an elusive asset once it has been betrayed, that science depends upon for its very existence. Scientific research is overwhelmingly financed by tax dollars, and the need for the goodwill of the public is more than an abstraction.
The Macchiarini affair raises a profound question of trust and responsibility: Should multiple co-authors be held responsible for a lead author's misconduct?
Karolinska apparently believes so. When the institution at last owned up to the scandal, it vindictively found Karl Henrik-Grinnemo, one of the whistleblowers, guilty of scientific misconduct as well. It also designated two other whistleblowers as "blameworthy" for their roles as co-authors of the papers on which Macchiarini was the lead author.
As a result, the whistleblowers' reputations and employment prospects have become collateral damage. Accusations of research misconduct can be a career killer. Research grants dry up, employment opportunities evaporate, publishing becomes next to impossible, and collaborators vanish into thin air.
Grinnemo contends that co-authors should only be responsible for their discrete contributions, not for the data supplied by others.
"Different aspects of a paper are highly specialized," he says, "and that's why you have multiple authors. You cannot go through every single bit of data because you don't understand all the parts of the article."
This is especially true in multidisciplinary, translational research, where there are sometimes 20 or more authors. "You have to trust co-authors, and if you find something wrong you have to notify all co-authors. But you couldn't go through everything or it would take years to publish an article," says Grinnemo.
Though the pressures facing scientists are very real, the problem of misconduct is not inevitable. Along with increased support from governments and industry, a change in academic culture that emphasizes quality over quantity of published studies could help encourage meritorious research.
But beyond that, trust will always play a role when numerous specialists unite to achieve a common goal: the accumulation of knowledge that will promote human health, wealth, and well-being.
[Correction: An earlier version of this story mistakenly credited The New York Times with breaking the news of the Anversa retractions, rather than Retraction Watch and STAT, which jointly published the exclusive on October 14th. The piece in the Times ran on October 15th. We regret the error.]
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.
Podcast: Wellness chatbots and meditation pods with Deepak Chopra
Over the last few decades, perhaps no one has impacted healthy lifestyles more than Deepak Chopra. While several of his theories and recommendations have been criticized by prominent members of the scientific community, he has helped bring meditation, yoga and other practices for well-being into the mainstream in ways that benefit the health of vast numbers of people every day. His work has led many to accept new ways of thinking about alternative medicine, the power of mind over body, and the malleability of the aging process.
His impact is such that it's been observed our culture no longer recognizes him as a human being but as a pervasive symbol of new-agey personal health and spiritual growth. Last week, I had a chance to confirm that Chopra is, in fact, a human being – and deserving of his icon status – when I talked with him for the Leaps.org podcast. He relayed ideas that were wise and ancient, yet highly relevant to our world today, with the fluidity and ease of someone discussing the weather. Showing no signs of slowing down at age 76, he described his prolific work, including the publication of two books in the past year and a range of technologies he’s developing, including a meditation app, meditation pods for the workplace, and a chatbot for mental health called Piwi.
Take a listen and get inspired to do some meditation and deep thinking on the future of health. As Chopra told me, “If you don’t have time to meditate once per day, you probably need to meditate twice per day.”
Highlights:
2:10: Chopra talks about meditation broadly and meditation pods, including the ones made by OpenSeed for meditation in the workplace.
6:10: The drawbacks of quick fixes like drugs for mental health.
10:30: The benefits of group meditation versus individual meditation.
14:35: What is a "metahuman" and how to become one.
19:40: The difference between the conditioned mind and the mind that's infinitely creative.
22:48: How Chopra's views of free will differ from the views of many neuroscientists.
28:04: Thinking Fast and Slow, and the role of intuition.
31:20: Athletic and creative geniuses.
32:43: The nature of fundamental truth.
34:00: Meditation for kids.
37:12: Never alone.Love and how AI chatbots can support mental health.
42:30: Extending lifespan, gene editing and lifestyle.
46:05: Chopra's mentor in living a long good life (and my mentor).
47:45: The power of yoga.
Links:
- OpenSeed meditation pods for people to meditate at work (Chopra is an advisor to OpenSeed).
- Chopra's book from 2021, Metahuman: Unleash Your Infinite Potential
- Chopra's book from 2022, Abundance: The Inner Path to Wealth
- NeverAlone.Love, Chopra's collaboration of businesses, policy makers, mental health professionals and others to raise awareness about mental health, advance scientific research and "create a global technology platform to democratize access to resources."
- The Piwi chatbot for mental health
- The Chopra Meditation & Well-Being App for people of all ages
- Only 1.6 percent of U.S. children meditate, according to the National Center for Complementary and Integrative Health