The Conversation
ChatGPT and other generative AI could foster science denial and misunderstanding – here’s how you can be on alert
ChatGPT and other generative AI could foster science denial and misunderstanding – here's how you can be on alert
Guillermo Spelucin/Moment via Getty Images
Gale Sinatra, University of Southern California and Barbara K. Hofer, Middlebury
Until very recently, if you wanted to know more about a controversial scientific topic – stem cell research, the safety of nuclear energy, climate change – you probably did a Google search. Presented with multiple sources, you chose what to read, selecting which sites or authorities to trust.
Now you have another option: You can pose your question to ChatGPT or another generative artificial intelligence platform and quickly receive a succinct response in paragraph form.
ChatGPT does not search the internet the way Google does. Instead, it generates responses to queries by predicting likely word combinations from a massive amalgam of available online information.
Although it has the potential for enhancing productivity, generative AI has been shown to have some major faults. It can produce misinformation. It can create “hallucinations” – a benign term for making things up. And it doesn't always accurately solve reasoning problems. For example, when asked if both a car and a tank can fit through a doorway, it failed to consider both width and height. Nevertheless, it is already being used to produce articles and website content you may have encountered, or as a tool in the writing process. Yet you are unlikely to know if what you're reading was created by AI.
As the authors of “Science Denial: Why It Happens and What to Do About It,” we are concerned about how generative AI may blur the boundaries between truth and fiction for those seeking authoritative scientific information.
Every media consumer needs to be more vigilant than ever in verifying scientific accuracy in what they read. Here's how you can stay on your toes in this new information landscape.
Cobalt88/iStock via Getty Images Plus
How generative AI could promote science denial
Erosion of epistemic trust. All consumers of science information depend on judgments of scientific and medical experts. Epistemic trust is the process of trusting knowledge you get from others. It is fundamental to the understanding and use of scientific information. Whether someone is seeking information about a health concern or trying to understand solutions to climate change, they often have limited scientific understanding and little access to firsthand evidence. With a rapidly growing body of information online, people must make frequent decisions about what and whom to trust. With the increased use of generative AI and the potential for manipulation, we believe trust is likely to erode further than it already has.
Misleading or just plain wrong. If there are errors or biases in the data on which AI platforms are trained, that can be reflected in the results. In our own searches, when we have asked ChatGPT to regenerate multiple answers to the same question, we have gotten conflicting answers. Asked why, it responded, “Sometimes I make mistakes.” Perhaps the trickiest issue with AI-generated content is knowing when it is wrong.
Disinformation spread intentionally. AI can be used to generate compelling disinformation as text as well as deepfake images and videos. When we asked ChatGPT to “write about vaccines in the style of disinformation,” it produced a nonexistent citation with fake data. Geoffrey Hinton, former head of AI development at Google, quit to be free to sound the alarm, saying, “It is hard to see how you can prevent the bad actors from using it for bad things.” The potential to create and spread deliberately incorrect information about science already existed, but it is now dangerously easy.
Fabricated sources. ChatGPT provides responses with no sources at all, or if asked for sources, may present ones it made up. We both asked ChatGPT to generate a list of our own publications. We each identified a few correct sources. More were hallucinations, yet seemingly reputable and mostly plausible, with actual previous co-authors, in similar sounding journals. This inventiveness is a big problem if a list of a scholar's publications conveys authority to a reader who doesn't take time to verify them.
Dated knowledge. ChatGPT doesn't know what happened in the world after its training concluded. A query on what percentage of the world has had COVID-19 returned an answer prefaced by “as of my knowledge cutoff date of September 2021.” Given how rapidly knowledge advances in some areas, this limitation could mean readers get erroneous outdated information. If you're seeking recent research on a personal health issue, for instance, beware.
Rapid advancement and poor transparency. AI systems continue to become more powerful and learn faster, and they may learn more science misinformation along the way. Google recently announced 25 new embedded uses of AI in its services. At this point, insufficient guardrails are in place to assure that generative AI will become a more accurate purveyor of scientific information over time.
10'000 Hours/DigitalVision via Getty Images
What can you do?
If you use ChatGPT or other AI platforms, recognize that they might not be completely accurate. The burden falls to the user to discern accuracy.
Increase your vigilance. AI fact-checking apps may be available soon, but for now, users must serve as their own fact-checkers. There are steps we recommend. The first is: Be vigilant. People often reflexively share information found from searches on social media with little or no vetting. Know when to become more deliberately thoughtful and when it's worth identifying and evaluating sources of information. If you're trying to decide how to manage a serious illness or to understand the best steps for addressing climate change, take time to vet the sources.
Improve your fact-checking. A second step is lateral reading, a process professional fact-checkers use. Open a new window and search for information about the sources, if provided. Is the source credible? Does the author have relevant expertise? And what is the consensus of experts? If no sources are provided or you don't know if they are valid, use a traditional search engine to find and evaluate experts on the topic.
Evaluate the evidence. Next, take a look at the evidence and its connection to the claim. Is there evidence that genetically modified foods are safe? Is there evidence that they are not? What is the scientific consensus? Evaluating the claims will take effort beyond a quick query to ChatGPT.
If you begin with AI, don't stop there. Exercise caution in using it as the sole authority on any scientific issue. You might see what ChatGPT has to say about genetically modified organisms or vaccine safety, but also follow up with a more diligent search using traditional search engines before you draw conclusions.
Assess plausibility. Judge whether the claim is plausible. Is it likely to be true? If AI makes an implausible (and inaccurate) statement like “1 million deaths were caused by vaccines, not COVID-19,” consider if it even makes sense. Make a tentative judgment and then be open to revising your thinking once you have checked the evidence.
Promote digital literacy in yourself and others. Everyone needs to up their game. Improve your own digital literacy, and if you are a parent, teacher, mentor or community leader, promote digital literacy in others. The American Psychological Association provides guidance on fact-checking online information and recommends teens be trained in social media skills to minimize risks to health and well-being. The News Literacy Project provides helpful tools for improving and supporting digital literacy.
Arm yourself with the skills you need to navigate the new AI information landscape. Even if you don't use generative AI, it is likely you have already read articles created by it or developed from it. It can take time and effort to find and evaluate reliable information about science online – but it is worth it.
Gale Sinatra, Professor of Education and Psychology, University of Southern California and Barbara K. Hofer, Professor of Psychology Emerita, Middlebury
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The Conversation
Nationwide test of Wireless Emergency Alert system could test people’s patience – or help rebuild public trust in the system
Nationwide test of Wireless Emergency Alert system could test people's patience – or help rebuild public trust in the system
Jeff Greenberg/Education Images/Universal Images Group via Getty Images
Elizabeth Ellcessor, University of Virginia and Hamilton Bean, University of Colorado Denver
The Wireless Emergency Alert system is scheduled to have its third nationwide test on Oct. 4, 2023. The Wireless Emergency Alert system is a public safety system that allows authorities to alert people via their mobile devices of dangerous weather, missing children and other situations requiring public attention.
Similar tests in 2018 and 2021 caused a degree of public confusion and resistance. In addition, there was confusion around the first test of the U.K. system in April 2023, and an outcry surrounding accidental alert messages such as those sent in Hawaii in January 2018 and in Florida in April 2023.
The federal government lists five types of emergency alerts: National (formerly labeled Presidential), Imminent Threat, Public Safety, America's Missing: Broadcast Emergency Response (Amber), and Opt-in Test Messages. You can opt out of any except National Alerts, which are reserved for national emergencies. The Oct. 4 test is a National Alert.
We are a media studies researcher and a communications researcher who study emergency alert systems. We believe that concerns about previous tests raise two questions: Is public trust in emergency alerting eroding? And how might the upcoming test rebuild it?
Confusion and resistance
In an ever-updating digital media environment, emergency alerts appear as part of a constant stream of updates, buzzes, reminders and notifications on people's smartphones. Over-alerting is a common fear in emergency management circles because it can lead people to ignore alerts and not take needed action. The sheer volume of different updates can be similarly overwhelming, burying emergency alerts in countless other messages. Many people have even opted out of alerts when possible, rummaging through settings and toggling off every alert they can find.
Even when people receive alerts, however, there is potential for confusion and rejection. All forms of emergency alerts rely on the recipients' trust in the people or organization responsible for the alert. But it's not always clear who the sender is. As one emergency manager explained to one of us regarding alerts used during COVID-19: “People were more confused because they got so many different notifications, especially when they don't say who they're from.”
When the origin of an alert is unclear, or the recipient perceives it to have a political bias counter to their own views, people may become confused or resistant to the message. Prior tests and use of the Wireless Emergency Alert system have indicated strong anti-authority attitudes, particularly following the much-derided 2018 test of what was then called the Presidential Alert message class. There are already conspiracy theories online about the upcoming test.
Trust in alerts is further reduced by the overall lack of testing and public awareness work done on behalf of the Wireless Emergency Alert system since its launch in June 2012. As warning expert Dennis Mileti explained in his 2018 Federal Emergency Management Agency PrepTalk, routine public tests are essential for warning systems' effectiveness. However, the Wireless Emergency Alert system has been tested at the national level only twice, and there has been little public outreach to explain the system by either the government or technology companies.
More exposure and info leads to more trust
The upcoming nationwide test may offer a moment that could rebuild trust in the system. A survey administered in the days immediately following the 2021 national test found that more respondents believed that the National Alert message class label would signal more trustworthy information than the Presidential Alert message class label.
Similarly, in contrast to the 2021 test, which targeted only select users, the Oct. 4 test is slated to reach all compatible devices in the U.S. Since users cannot opt out of the National Alert message class, this week's test is a powerful opportunity to build awareness about the potential benefits of a functional federal emergency alert system.
The Oct. 4 test message is expected to state, “THIS IS A TEST of the National Wireless Emergency Alert system. No action is needed.” We instead suggest that action is, in fact, urgently needed to help people better understand the rapidly changing mobile alert and warning ecosystem that confronts them. Familiarity with this system is what will allow it to support public health and safety, and address the crises of the 21st century.
Here are steps that you can take now to help make the Wireless Emergency Alert system more effective:
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The Wireless Emergency Alert system is only one form of emergency alert. Identify which mobile notification systems are used by your local emergency management organizations: police, fire and emergency services. Know which systems are opt-in and opt-out, and opt in to those needed. Ensure access to other sources of information during an emergency, such as local radio and television, or National Oceanic and Atmospheric Administration weather radio.
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Understand the meaning of mobile device notification settings. Just because you are opted in to “Emergency Alerts” on your cellphone does not necessarily mean you are signed up to receive notifications from local authorities. Check the FEMA website for information about the Wireless Emergency Alert system and your local emergency management organizations' websites about opt-in systems.
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Have a plan for contacting family, friends and neighbors during an emergency. Decide in advance who will help the vulnerable members of your community.
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Find out if your local emergency management organizations test their alert systems, and make sure to receive those local tests.
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Anticipate the possibility that mobile systems will be damaged or unavailable during a crisis and prepare essentials for sheltering in place or quick evacuation.
Finally, push back on the lack of information and rise of misinformation about alerts by sharing reliable information about emergency alerts with your family and friends.
Elizabeth Ellcessor, Associate Professor of Media Studies, University of Virginia and Hamilton Bean, Associate Professor of Communication, University of Colorado Denver
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The Conversation
Superconductivity at room temperature remains elusive a century after a Nobel went to the scientist who demonstrated it below -450 degrees Fahrenheit
Superconductivity at room temperature remains elusive a century after a Nobel went to the scientist who demonstrated it below -450 degrees Fahrenheit
Benjamin Couprie/Wikimedia Commons
David D. Nolte, Purdue University
On April 8, 1911, Dutch physicist Heike Kamerlingh Onnes scribbled in pencil an almost unintelligible note into a kitchen notebook: “near enough null.”
The note referred to the electrical resistance he'd measured during a landmark experiment that would later be credited as the discovery of superconductivity. But first, he and his team would need many more trials to confirm the measurement.
Their discovery opened up a world of potential scientific applications. The century since has seen many advances, but superconductivity researchers today can take lessons from Onnes' original, Nobel Prize-winning work.
I have always been interested in origin stories. As a physics professor and the author of books on the history of physics, I look for the interesting backstory – the twists, turns and serendipities that lie behind great discoveries.
The true stories behind these discoveries are usually more chaotic than the rehearsed narratives crafted after the fact, and some of the lessons learned from Onnes' experiments remain relevant today as researchers search for new superconductors that might, one day, operate near room temperature.
Superconductivity
A rare quantum effect that allows electrical currents to flow without resistance in superconducting wires, superconductivity allows for a myriad of scientific applications. These include MRI machines and powerful particle accelerators.
Imagine giving a single push to a row of glass beads strung on a frictionless wire. Once the beads start moving down the wire, they never stop, like a perpetual motion machine. That's the idea behind superconductivity – particles flowing without resistance.
For superconductors to work, they need to be cooled to ultra-low temperatures colder than any Arctic blast. That's how Onnes' original work cooling helium to near absolute zero temperature set the stage for his unexpected discovery of superconductivity.
The discovery
Onnes, a physics professor at the University of Leiden in the Netherlands, built the leading low-temperature physics laboratory in the world in the first decade of the 20th century.
His lab was the first to turn helium from a gas to a liquid by making the gas expand and cool. His lab managed to cool helium this way to a temperature of -452 degrees Farenheit (-269 degrees Celsius).
Onnes then began studying the electrical conductivity of metals at these cold temperatures. He started with mercury because mercury in liquid form can conduct electricity, making it easy to fill into glass tubes. At low temperatures, the mercury would freeze solid, creating metallic wires that Onnes could use in his conductivity experiments.
On April 8, 1911, his lab technicians transferred liquid helium into a measurement cryostat – a glass container with a vacuum jacket to insulate it from the room's heat. They cooled the helium to -454 F (-270 C) and then measured the electrical resistance of the mercury wire by sending a small current through it and measuring the voltage.
It was then that Onnes wrote the cryptic “near enough null” measurement into his kitchen notebook, meaning that the wire was conducting electricity without any measurable resistance.
That date of April 8 is often quoted as the discovery of superconductivity, but the full story isn't so simple, because scientists can't accept a scribbled “near-enough null” as sufficient proof of a new discovery.
In pursuit of proof
Onnes' team performed its next experiment more than six weeks later, on May 23. On this day, they cooled the cryostat again to -454 F (-270 C) and then let the temperature slowly rise.
At first they barely measured any electrical resistance, indicating superconductivity. The resistance stayed small up to -452 F, when it suddenly rose by over a factor of 400 as the temperature inched up just a fraction of a degree.
The rise was so rapid and so unexpected that they started searching for some form of electrical fault or open circuit that might have been caused by the temperature shifts. But they couldn't find anything wrong. They spent five more months improving their system before trying again. On Oct. 26 they repeated the experiment, capturing the earlier sudden rise in resistance.
Heike Kamerlingh Onnes via Wikimedia Commons
One week later, Onnes presented these results to the first Solvay Conference, and two years later he received his Nobel Prize in physics, recognizing his low-temperature work generally but not superconductivity specifically.
It took another three years of diligent work before Onnes had his irrefutable evidence: He measured persistent currents that did not decay, demonstrating truly zero resistance and superconductivity on April 24, 1914.
New frontiers for critical temperatures
In the decades following Onnes' discovery, many researchers have explored how metals act at supercooled temperatures and have learned more about superconductivity.
But if researchers can observe superconductivity only at super low temperatures, it's hard to make anything useful. It is too expensive to operate a machine practically if it works only at -400 F (-240 C).
So, scientists began searching for superconductors that can work at practical temperatures. For instance, K. Alex Müller and J. Georg Bednorz at the IBM research laboratory in Switzerland figured out that metal oxides like lanthanum-barium-copper oxide, known as LBCO, could be good candidates.
It took the IBM team about three years to find superconductivity in LBCO. But when they did, their work set a new record, with superconductivity observed at -397 F (-238 C) in 1986.
A year later, in 1987, a lab in Houston replaced lanthanum in LBCO with the element yttrium to create YBCO. They demonstrated superconductivity at -292 F. This discovery made YBCO the first practical superconductor, because it could work while immersed in inexpensive liquid nitrogen.
Since then, researchers have observed superconductivity at temperatures as high as -164 F (-109 C), but achieving a room-temperature superconductor has remained elusive.
Gingras.ol/Wikimedia Commons, CC BY-NC-SA
In 2023, two groups claimed they had evidence for room-temperature superconductivity, though both reports have been met with sharp skepticism, and both are now in limbo following further scrutiny.
Superconductivity has always been tricky to prove because some metals can masquerade as superconductors. The lessons learned by Onnes a century ago – that these discoveries require time, patience and, most importantly, proof of currents that never stop – are still relevant today.
David D. Nolte, Distinguished Professor of Physics and Astronomy, Purdue University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The Conversation
Tenacious curiosity in the lab can lead to a Nobel Prize – mRNA research exemplifies the unpredictable value of basic scientific research
Tenacious curiosity in the lab can lead to a Nobel Prize – mRNA research exemplifies the unpredictable value of basic scientific research
Sebastian Condrea/Moment via Getty Images
André O. Hudson, Rochester Institute of Technology
The 2023 Nobel Prize in physiology or medicine will go to Katalin Karikó and Drew Weissman for their discovery that modifying mRNA – a form of genetic material your body uses to produce proteins – could reduce unwanted inflammatory responses and allow it to be delivered into cells. While the impact of their findings may not have been apparent at the time of their breakthrough over a decade ago, their work paved the way for the development of the Pfizer-BioNTech and Moderna COVID-19 vaccines, as well as many other therapeutic applications currently in development.
We asked André O. Hudson, a biochemist and microbiologist at the Rochester Institute of Technology, to explain how basic research like that of this year's Nobel Prize winners provides the foundations for science – even when its far-reaching effects won't be felt until years later.
What is basic science?
Basic research, sometimes called fundamental research, is a type of investigation with the overarching goal of understanding natural phenomena like how cells work or how birds can fly. Scientists are asking the fundamental questions of how, why, when, where and if in order to bridge a gap in curiosity and understanding about the natural world.
Researchers sometimes conduct basic research with the hope of eventually developing a technology or drug based on that work. But what many scientists typically do in academia is ask fundamental questions with answers that may or may not ever lead to practical applications.
Humans, and the animal kingdom as a whole, are wired to be curious. Basic research scratches that itch.
What are some basic science discoveries that went on to have a big influence on medicine?
The 2023 Nobel Prize in physiology or medicine acknowledges basic science work done in the early 2000s. Karikó and Weissman's discovery about modifying mRNA to reduce the body's inflammatory response to it allowed other researchers to leverage it to make improved vaccines.
Another example is the discovery of antibiotics, which was based on an unexpected observation. In the late 1920s, the microbiologist Alexander Fleming was growing a species of bacteria in his lab and found that his Petri dish was accidentally contaminated with the fungus Penicillium notatum. He noticed that wherever the fungus was growing, it impeded or inhibited the growth of the bacteria. He wondered why that was happening and subsequently went on to isolate penicillin, which was approved for medical use in the early 1940s.
This work fed into more questions that ushered in the age of antibiotics. The 1952 Nobel Prize in physiology or medicine was awarded to Selman Waksman for his discovery of streptomycin, the first antibiotic to treat tuberculosis.
Basic research often involves seeing something surprising, wanting to understand why and deciding to investigate further. Early discoveries start from a basic observation, asking the simple question of “How?” Only later are they parlayed into a medical technology that helps humanity.
Why does it take so long to get from curiosity-driven basic science to a new product or technology?
The mRNA modification discovery could be considered to be on a relatively fast track from basic science to application. Less than 15 years passed between Karikó and Weissman's findings and the COVID-19 vaccines. The importance of their discovery came to the forefront with the pandemic and the millions of lives they saved.
Most basic research won't reach the market until several decades after its initial publication in a science journal. One reason is because it depends on need. For example, orphan diseases that affect only a small number of people will get less attention and funding than conditions that are ubiquitous in a population, like cancer or diabetes. Companies don't want to spend billions of dollars developing a drug that will only have a small return on their investment. Likewise, because the return on investment for basic research often isn't clear, it can be a hard sell to support financially.
Another reason is cultural. Scientists are trained to chase after funding and support for their work wherever they can find it. But sometimes that's not as easy as it seems.
A good example of this was when the human genome was first sequenced in the early 2000s. A lot of people thought that having access to the full sequence would lead to treatments and cures for many different diseases. But that has not been the case, because there are many nuances to translating basic research to the clinic. What works in a cell or an animal might not translate into people. There are many steps and layers in the process to get there.
Why is basic science important?
For me, the most critical reason is that basic research is how we train and mentor future scientists.
In an academic setting, telling students “Let's go develop an mRNA vaccine” versus “How does mRNA work in the body” influences how they approach science. How do they design experiments? Do they start the study going forward or backward? Are they argumentative or cautious in how they present their findings?
Marco VDM/E+ via Getty Images
Almost every scientist is trained under a basic research umbrella of how to ask questions and go through the scientific method. You need to understand how, when and where mRNAs are modified before you can even begin to develop an mRNA vaccine. I believe the best way to inspire future scientists is to encourage them to expand on their curiosity in order to make a difference.
When I was writing my dissertation, I was relying on studies that were published in the late 1800s and early 1900s. Many of these studies are still cited in scientific articles today. When researchers share their work, though it may not be today or tomorrow, or 10 to 20 years from now, it will be of use to someone else in the future. You'll make a future scientist's job a little bit easier, and I believe that's a great legacy to have.
What is a common misconception about basic science?
Because any immediate use for basic science can be very hard to see, it's easy to think this kind of research is a waste of money or time. Why are scientists breeding mosquitoes in these labs? Or why are researchers studying migratory birds? The same argument has been made with astronomy. Why are we spending billions of dollars putting things into space? Why are we looking to the edge of the universe and studying stars when they are millions and billions of light years away? How does it affect us?
There is a need for more scientific literacy because not having it can make it difficult to understand why basic research is necessary to future breakthroughs that will have a major effect on society.
In the short term, the worth of basic research can be hard to see. But in the long term, history has shown that a lot of what we take for granted now, such as common medical equipment like X-rays, lasers and MRIs, came from basic things people discovered in the lab.
And it still goes down to the fundamental questions – we're a species that seeks answers to things we don't know. As long as curiosity is a part of humanity, we're always going to be seeking answers.
André O. Hudson, Dean of the College of Science, Professor of Biochemistry, Rochester Institute of Technology
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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