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Bogus scientific papers are enriching fraudsters and slowing lifesaving medical research

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theconversation.com – Frederik Joelving, Contributing editor, Retraction Watch – 2025-01-31 08:02:00

Bogus scientific papers are enriching fraudsters and slowing lifesaving medical research

Assistant professor Frank Cackowski, left, and researcher Steven Zielske at Wayne State University in Detroit became suspicious of a paper on cancer research that was eventually retracted.
Amy Sacka, CC BY-ND

Frederik Joelving, Retraction Watch; Cyril Labbé, Université Grenoble Alpes (UGA), and Guillaume Cabanac, Institut de Recherche en Informatique de Toulouse

Over the past decade, furtive commercial entities around the world have industrialized the production, sale and dissemination of bogus scholarly research. These paper mills are profiting by undermining the literature that everyone from doctors to engineers rely on to make decisions about human lives.

It is exceedingly difficult to get a handle on exactly how big the problem is. About 55,000 scholarly papers have been retracted to date, for a variety of reasons, but scientists and companies who screen the scientific literature for telltale signs of fraud estimate that there are many more fake papers circulating – possibly as many as several hundred thousand. This fake research can confound legitimate researchers who must wade through dense equations, evidence, images and methodologies, only to find that they were made up.

Even when bogus papers are spotted – usually by amateur sleuths on their own time – academic journals are often slow to retract the papers, allowing the articles to taint what many consider sacrosanct: the vast global library of scholarly work that introduces new ideas, reviews and other research and discusses findings.

These fake papers are slowing research that has helped millions of people with lifesaving medicine and therapies, from cancer to COVID-19. Analysts’ data shows that fields related to cancer and medicine are particularly hard-hit, while areas such as philosophy and art are less affected.

To better understand the scope, ramifications and potential solutions of this metastasizing assault on science, we – a contributing editor at Retraction Watch, a website that reports on retractions of scientific papers and related topics, and two computer scientists at France’s Université Toulouse III–Paul Sabatier and Université Grenoble Alpes who specialize in detecting bogus publications – spent six months investigating paper mills.

Co-author Guillaume Cabanac also developed the Problematic Paper Screener, which filters 130 million new and old scholarly papers every week looking for nine types of clues that a paper might be fake or contain errors.

An obscure molecule

Frank Cackowski at Detroit’s Wayne State University was confused.

The oncologist was studying a sequence of chemical reactions in cells to see whether they could be a target for drugs against prostate cancer. A paper from 2018 in the American Journal of Cancer Research piqued his interest when he read that a little-known molecule called SNHG1 might interact with the chemical reactions he was exploring. He and fellow Wayne State researcher Steven Zielske began experiments but found no link.

Meanwhile, Zielske had grown suspicious of the paper. Two graphs showing results for different cell lines were identical, he noticed, which “would be like pouring water into two glasses with your eyes closed and the levels coming out exactly the same.” Another graph and a table in the article also inexplicably contained identical data.

Zielske described his misgivings in an anonymous post in 2020 at PubPeer, an online forum where many scientists report potential research misconduct, and also contacted the journal’s editor. The journal pulled the paper, citing “falsified materials and/or data.”

“Science is hard enough as it is if people are actually being genuine and trying to do real work,” said Cackowski, who also works at the Karmanos Cancer Institute in Michigan.

Two men sitting  across from each other at a table filled with papers
Wayne State scientists Cackowsi and Zielske carried out experiments based on a paper they later found to contain false data.
Amy Sacka, CC BY-ND

Legitimate academic journals evaluate papers before publication by having other researchers in the field carefully read them over. But this peer review process is far from perfect. Reviewers volunteer their time, typically assume research is real and so don’t look for fraud.

Some publishers may try to pick reviewers they deem more likely to accept papers, because rejecting a manuscript can mean losing out on thousands of dollars in publication fees.

Worse, some corrupt scientists form peer review rings. Paper mills may create fake peer reviewers. Others may bribe editors or plant agents on journal editorial boards.

An ‘absolutely huge’ problem

It’s unclear when paper mills began to operate at scale. The earliest suspected paper mill article retracted was published in 2004, according to the Retraction Watch database, which details retractions and is operated by The Center for Scientific Integrity, the parent nonprofit of Retraction Watch.

An analysis of 53,000 papers submitted to six publishers – but not necessarily published – found 2% to 46% suspect submissions across journals. The American publisher Wiley, which has retracted more than 11,300 articles and closed 19 heavily affected journals in its erstwhile Hindawi division, said its new paper mill detection tool flags up to 1 in 7 submissions.

As many as 2% of the several million scientific works published in 2022 were milled, according to Adam Day, who directs Clear Skies, a company in London that develops tools to spot fake papers. Some fields are worse than others: biology and medicine are closer to 3%, and some subfields, such as cancer, may be much larger, Day said.

The paper mill problem is “absolutely huge,” said Sabina Alam, director of Publishing Ethics and Integrity at Taylor & Francis, a major academic publisher. In 2019, none of the 175 ethics cases escalated to her team was about paper mills, Alam said. Ethics cases include submissions and already published papers. “We had almost 4,000 cases” in 2023, she said. “And half of those were paper mills.”

Jennifer Byrne, an Australian scientist who now heads up a research group to improve the reliability of medical research, testified at a July 2022 U.S. House of Representatives hearing that nearly 6% of 12,000 cancer research papers screened had errors that could signal paper mill involvement. Byrne shuttered her cancer research lab in 2017 because genes she had spent two decades researching and writing about became the target of fake papers.

In 2022, Byrne and colleagues, including two of us, found that suspect genetics research, despite not immediately affecting patient care, informs scientists’ work, including clinical trials. But publishers are often slow to retract tainted papers, even when alerted to obvious fraud. We found that 97% of the 712 problematic genetics research articles we identified remained uncorrected.

Potential solutions

The Cochrane Collaboration has a policy excluding suspect studies from its analyses of medical evidence and is developing a tool to spot problematic medical trials. And publishers have begun to share data and technologies among themselves to combat fraud, including image fraud.

Technology startups are also offering help. The website Argos, launched in September 2024 by Scitility, an alert service based in Sparks, Nevada, allows authors to check collaborators for retractions or misconduct. Morressier, a scientific conference and communications company in Berlin, offers research integrity tools. Paper-checking tools include Signals, by London-based Research Signals, and Clear Skies’ Papermill Alarm.

But Alam acknowledges that the fight against paper mills won’t be won as long as the booming demand for papers remains.

Today’s commercial publishing is part of the problem, Byrne said. Cleaning up the literature is a vast and expensive undertaking. “Either we have to monetize corrections such that publishers are paid for their work, or forget the publishers and do it ourselves,” she said.

There’s a fundamental bias in for-profit publishing: “We pay them for accepting papers,” said Bodo Stern, a former editor of the journal Cell and chief of Strategic Initiatives at Howard Hughes Medical Institute, a nonprofit research organization and funder in Chevy Chase, Maryland. With more than 50,000 journals on the market, bad papers shopped around long enough eventually find a home, Stern said.

To prevent this, we could stop paying journals for accepting papers and look at them as public utilities that serve a greater good. “We should pay for transparent and rigorous quality-control mechanisms,” he said.

Peer review, meanwhile, “should be recognized as a true scholarly product, just like the original article,” Stern said. And journals should make all peer-review reports publicly available, even for manuscripts they turn down.

This article is republished from The Conversation under a Creative Commons license. This is a condensed version. To learn more about how fraudsters around the globe use paper mills to enrich themselves and harm scientific research, read the full version.The Conversation

Frederik Joelving, Contributing editor, Retraction Watch; Cyril Labbé, Professor of Computer Science, Université Grenoble Alpes (UGA), and Guillaume Cabanac, Professor of Computer Science, Institut de Recherche en Informatique de Toulouse

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What causes RFK Jr.’s strained and shaky voice? A neurologist explains this little-known disorder

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theconversation.com – Indu Subramanian, Clinical Professor of Neurology, University of California, Los Angeles – 2025-05-01 07:45:00

U.S. Secretary of Health and Human Services Robert F. Kennedy Jr. speaks at an April 16, 2025, news conference in Washington, D.C.
Alex Wong via Getty Images

Indu Subramanian, University of California, Los Angeles

Health and Human Services Secretary Robert F. Kennedy Jr. has attracted a lot of attention for his raspy voice, which results from a neurological voice disorder called spasmodic dysphonia.

Kennedy, 71, says that in his 40s he developed a neurological disease that “robbed him of his strong speaking voice.” Kennedy first publicly spoke of the quiver he had noticed in his voice in a 2004 interview with journalist Diane Rehm, who also had spasmodic dysphonia.

In 2005, Kennedy was receiving shots of botulinum toxin, the neurotoxin that is now used in Botox as well as to treat migraines and other conditions, every four months. This first-line treatment for dysphonia helps to weaken the vocal folds that contract abnormally with this condition. He used botulinum toxin injections for 10 years and then stopped using them, saying they were “not a good fit” for him.

Kennedy initially developed symptoms while in the public eye teaching at Pace University in New York. Some viewers wrote to him suggesting that he had the condition spasmodic dysphonia and that he should contact a well-known expert on the disease, Dr. Andrew Blitzer. He followed this advice and had the diagnosis confirmed.

I am a movement disorders neurologist and have long been passionate about the psychological and social toll that conditions such as dysphonias have on my patients.

YouTube video
Kennedy says his condition began in 1996, when he was 42.

Types of dysphonias

In North America, an estimated 50,000 people have spasmodic dysphonia. The condition involves the involuntary pulling of the muscles that open and close the vocal folds, causing the voice to sound strained and strangled, at times with a breathy quality. About 30% to 60% of people with the condition also experience vocal tremor, which can alter the sound of the voice.

Typically, a neurologist may suspect the disorder by identifying characteristic voice breaks when the patients is speaking. The diagnosis is confirmed with the help of an ear, nose and throat specialist who can insert a small scope into the larynx, examine the vocal folds and rule out any other abnormalities.

Because the disorder is not well known to the public, many patients experience a delay in diagnosis and may be misdiagnosed with gastric reflux or allergies.

The most common type of spasmodic dysphonia is called adductor dysphonia, which accounts for 80% of cases. It is characterized by a strained or strangled voice quality with abrupt breaks on vowels due to the vocal folds being hyperadducted, or abnormally closed.

In contrast, a form of the condition called abductor dysphonia causes a breathy voice with breaks on consonants due to uncontrolled abduction – meaning coming apart of the vocal folds.

Potential treatments

Spasmodic dysphonia is not usually treatable with oral medications and sometimes can get better with botulinum toxin injections into the muscles that control the vocal cords. It is a lifelong disorder currently without a cure. Voice therapy through working with a speech pathologist alongside botulinum toxin administration may also be beneficial.

Surgical treatments can be an option for patients who fail botulinum toxin treatment, though surgeries come with risks and can be variably effective. Surgical techniques are being refined and require wider evaluation and long-term follow-up data before being considered as a standard treatment for spasmodic dysphonia.

YouTube video
The sudden, uncontrollable movements caused by irregular folding of the vocal folds are referred to as spasms, which gave rise to the name spasmodic dysphonia.

Dysphonias fall into a broader category of movement disorders

Spasmodic dysphonia is classified as a focal dystonia, a dystonia that affects one body part – the vocal folds, in the case of spasmodic dysphonia. Dystonia is an umbrella term for movement disorders characterized by sustained or repetitive muscle contractions that cause abnormal postures or movements.

The most common dystonia is cervical dystonia, which affects the neck and can cause pulling of the head to one side.

Another type, called blepharospasm, involves involuntary muscle contractions and spasms of the eyelid muscles that can cause forced eye closure that can even affect vision in some cases. There can be other dystonias such as writer’s cramp, which can make the hand cramp when writing. Musicians can develop dystonias from overusing certain body parts such as violinists who develop dystonia in their hands or trumpet players who develop dystonia in their lips.

Stigmas and psychological distress

Dystonias can cause tremendous psychological distress.

Many dystonias and movement disorders in general, including Parkinson’s disease and other conditions that result in tremors, face tremendous amounts of stigma. In Africa, for instance, there is a misconception that the affected person has been cursed by witchcraft or that the movement disorder is contagious. People with the condition may be hidden from society or isolated from others due to fear of catching the disease.

In the case of spasmodic dysphonia, the affected person may feel that they appear nervous or ill-prepared while speaking publicly. They may be embarrassed or ashamed and isolate themselves from speaking to others.

My patients have been very frustrated by the unpredictable nature of the symptoms and by having to avoid certain sounds that could trigger the dysphonia. They may then have to restructure their word choices and vocabulary so as not to trigger the dysphonia, which can be very mentally taxing.

Some patients with dysphonia feel that their abnormal voice issues affect their relationships and their ability to perform their job or take on leadership or public-facing roles. Kennedy said in an interview that he finds the sound of his own voice to be unbearable to listen to and apologizes to others for having to listen to it.

A 2005 study exploring the biopsychosocial consequences of spasmodic dysphonia through interviews with patients gives some insight into the experience of people living with the disorder.

A patient in that study said that their voice sounded “like some kind of wild chicken screeching out words,” and another patient said that it “feels like you’re having to grab onto a word and push it out from your throat.” Another felt like “there’s a rubber band around my neck. Someone was constricting it.” And another said, “It feels like you have a sore throat all the time … like a raw feeling in your throat.”

Patients in the study described feeling hopeless and disheartened, less confident and less competent. The emotional toll can be huge. One patient said, “I used to be very outgoing and now I find myself avoiding those situations.” Another said, “People become condescending like you’re not capable anymore because you don’t speak well.”

As conditions such as spasmodic dysphonia become better recognized, I am hopeful that not only will treatments improve, but that stigmas around such conditions will diminish.The Conversation

Indu Subramanian, Clinical Professor of Neurology, University of California, Los Angeles

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Note: The following A.I. based commentary is not part of the original article, reproduced above, but is offered in the hopes that it will promote greater media literacy and critical thinking, by making any potential bias more visible to the reader –Staff Editor.

Political Bias Rating: Centrist

This content is predominantly medical and informational, focusing on the neurological condition of spasmodic dysphonia and the personal experience of Robert F. Kennedy Jr. with it. The article provides a balanced, fact-based explanation of the disorder, its symptoms, treatments, and social impacts without introducing political or ideological commentary. The tone is neutral, aiming to educate rather than persuade, and it refrains from taking any political stance or showing bias toward any political ideology.

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AI is giving a boost to efforts to monitor health via radar

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theconversation.com – Chandler Bauder, Electronics Engineer, U.S. Naval Research Laboratory – 2025-04-30 07:48:00

AI-powered radar could enable contactless health monitoring in the home.
Chandler Bauder

Chandler Bauder, U.S. Naval Research Laboratory and Aly Fathy, University of Tennessee

If you wanted to check someone’s pulse from across the room, for example to remotely monitor an elderly relative, how could you do it? You might think it’s impossible, because common health-monitoring devices such as fingertip pulse oximeters and smartwatches have to be in contact with the body.

However, researchers are developing technologies that can monitor a person’s vital signs at a distance. One of those technologies is radar.

We are electrical engineers who study radar systems. We have combined advances in radar technology and artificial intelligence to reliably monitor breathing and heart rate without contacting the body.

Noncontact health monitoring has the potential to be more comfortable and easier to use than traditional methods, particularly for people looking to monitor their vital signs at home.

How radar works

Radar is commonly known for measuring the speed of cars, making weather forecasts and detecting obstacles at sea and in the air. It works by sending out electromagnetic waves that travel at the speed of light, waiting for them to bounce off objects in their path, and sensing them when they return to the device.

Radar can tell how far away things are, how fast they’re moving, and even their shape by analyzing the properties of the reflected waves.

Radar can also be used to monitor vital signs such as breathing and heart rate. Each breath or heartbeat causes your chest to move ever so slightly – movement that’s hard for people to see or feel. However, today’s radars are sensitive enough to detect these tiny movements, even from across a room.

Advantages of radar

There are other technologies that can be used to measure health remotely. Camera-based techniques can use infrared light to monitor changes in the surface of the skin in the same manner as pulse oximeters, revealing information about your heart’s activity. Computer vision systems can also monitor breathing and other activities, such as sleep, and they can detect when someone falls.

However, cameras often fail in cases where the body is obstructed by blankets or clothes, or when lighting is inadequate. There are also concerns that different skin tones reflect infrared light differently, causing inaccurate readings for people with darker skin. Additionally, depending on high-resolution cameras for long-term health monitoring brings up serious concerns about patient privacy.

side-by-side images, one of a person and the other a verticle series of nested blobs of color
Radar sees the world in terms of how strongly objects in its view reflect the transmitted signals. The resolution of images it can generate are much lower than images cameras produce.
Chandler Bauder

Radar, on the other hand, solves many of these problems. The wavelengths of the transmitted waves are much longer than those of visible or infrared light, allowing the waves to pass through blankets, clothing and even walls. The measurements aren’t affected by lighting or skin tone, making them more reliable in different conditions.

Radar imagery is also extremely low resolution – think old Game Boy graphics versus a modern 4K TV – so it doesn’t capture enough detail to be used to identify someone, but it can still monitor important activities. While it does project energy, the amount does not pose a health hazard. The health-monitoring radars operate at frequencies and power levels similar to the phone in your pocket.

Radar + AI

Radar is powerful, but it has a big challenge: It picks up everything that moves. Since it can detect tiny chest movements from the heart beating, it also picks up larger movements from the head, limbs or other people nearby. This makes it difficult for traditional processing techniques to extract vital signs clearly.

To address this problem we created a kind of “brain” to make the radar smarter. This brain, which we named mm-MuRe, is a neural network – a type of artificial intelligence – that learns directly from raw radar signals and estimates chest movements. This approach is called end-to-end learning. It means that, unlike other radar plus AI techniques, the network figures out on its own how to ignore the noise and focus only on the important signals.

a diagram with two cartoon representations of people on one side, a brain on the other and vertical curved lines in betwenn
In our study, we used AI to transform raw, unprocessed radar signals into vital signs waveforms of one or two people.
Chandler Bauder

We found that this AI enhancement not only gives more accurate results, it also works faster than traditional methods. It handles multiple people at once, for example an elderly couple, and adapts to new situations, even those it didn’t see during training – such as when people are sitting at different heights, riding in a car or standing close together.

Implications for health care

Reliable remote health monitoring using radar and AI could be a major boon for health care. With no need to touch the patient’s skin, risks of rashes, contamination and discomfort could be greatly reduced. It’s especially helpful in long-term care, where reducing wires and devices can make life significantly easier for patients and caregivers.

Imagine a nursing home where radar quietly watches over residents, alerting caregivers immediately if someone has breathing trouble, falls or needs help. It can be implemented as a home system that checks your breathing while you sleep – no wearables required. Doctors could even use radar to remotely monitor patients recovering from surgery or illness.

This technology is moving quickly toward real-world use. In the future, checking your health could be as simple as walking into a room, with invisible waves and smart AI working silently to take your vital signs.The Conversation

Chandler Bauder, Electronics Engineer, U.S. Naval Research Laboratory and Aly Fathy, Professor of Electrical Engineering, University of Tennessee

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Note: The following A.I. based commentary is not part of the original article, reproduced above, but is offered in the hopes that it will promote greater media literacy and critical thinking, by making any potential bias more visible to the reader –Staff Editor.

Political Bias Rating: Centrist

The article is focused on a scientific and technological development related to health monitoring using radar and artificial intelligence. It provides an overview of the research process, technical details, and potential health care applications without expressing a clear ideological stance. The tone remains neutral, emphasizing the technical capabilities and benefits of the technology, particularly in long-term care and home health monitoring. While it does mention potential privacy concerns with other methods like cameras, it does so without taking a political position, focusing instead on the advantages of radar. The content adheres to factual reporting and avoids overt bias or advocacy, presenting the information in a straightforward and informative manner.

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Forensics tool ‘reanimates’ the ‘brains’ of AIs that fail in order to understand what went wrong

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theconversation.com – David Oygenblik, Ph.D. Student in Electrical and Computer Engineering, Georgia Institute of Technology – 2025-04-30 07:47:00

Tesla crashes are only the most glaring of AI failures.
South Jordan Police Department via APPEAR

David Oygenblik, Georgia Institute of Technology and Brendan Saltaformaggio, Georgia Institute of Technology

From drones delivering medical supplies to digital assistants performing everyday tasks, AI-powered systems are becoming increasingly embedded in everyday life. The creators of these innovations promise transformative benefits. For some people, mainstream applications such as ChatGPT and Claude can seem like magic. But these systems are not magical, nor are they foolproof – they can and do regularly fail to work as intended.

AI systems can malfunction due to technical design flaws or biased training data. They can also suffer from vulnerabilities in their code, which can be exploited by malicious hackers. Isolating the cause of an AI failure is imperative for fixing the system.

But AI systems are typically opaque, even to their creators. The challenge is how to investigate AI systems after they fail or fall victim to attack. There are techniques for inspecting AI systems, but they require access to the AI system’s internal data. This access is not guaranteed, especially to forensic investigators called in to determine the cause of a proprietary AI system failure, making investigation impossible.

We are computer scientists who study digital forensics. Our team at the Georgia Institute of Technology has built a system, AI Psychiatry, or AIP, that can recreate the scenario in which an AI failed in order to determine what went wrong. The system addresses the challenges of AI forensics by recovering and “reanimating” a suspect AI model so it can be systematically tested.

Uncertainty of AI

Imagine a self-driving car veers off the road for no easily discernible reason and then crashes. Logs and sensor data might suggest that a faulty camera caused the AI to misinterpret a road sign as a command to swerve. After a mission-critical failure such as an autonomous vehicle crash, investigators need to determine exactly what caused the error.

Was the crash triggered by a malicious attack on the AI? In this hypothetical case, the camera’s faultiness could be the result of a security vulnerability or bug in its software that was exploited by a hacker. If investigators find such a vulnerability, they have to determine whether that caused the crash. But making that determination is no small feat.

Although there are forensic methods for recovering some evidence from failures of drones, autonomous vehicles and other so-called cyber-physical systems, none can capture the clues required to fully investigate the AI in that system. Advanced AIs can even update their decision-making – and consequently the clues – continuously, making it impossible to investigate the most up-to-date models with existing methods.

YouTube video
Researchers are working on making AI systems more transparent, but unless and until those efforts transform the field, there will be a need for forensics tools to at least understand AI failures.

Pathology for AI

AI Psychiatry applies a series of forensic algorithms to isolate the data behind the AI system’s decision-making. These pieces are then reassembled into a functional model that performs identically to the original model. Investigators can “reanimate” the AI in a controlled environment and test it with malicious inputs to see whether it exhibits harmful or hidden behaviors.

AI Psychiatry takes in as input a memory image, a snapshot of the bits and bytes loaded when the AI was operational. The memory image at the time of the crash in the autonomous vehicle scenario holds crucial clues about the internal state and decision-making processes of the AI controlling the vehicle. With AI Psychiatry, investigators can now lift the exact AI model from memory, dissect its bits and bytes, and load the model into a secure environment for testing.

Our team tested AI Psychiatry on 30 AI models, 24 of which were intentionally “backdoored” to produce incorrect outcomes under specific triggers. The system was successfully able to recover, rehost and test every model, including models commonly used in real-world scenarios such as street sign recognition in autonomous vehicles.

Thus far, our tests suggest that AI Psychiatry can effectively solve the digital mystery behind a failure such as an autonomous car crash that previously would have left more questions than answers. And if it does not find a vulnerability in the car’s AI system, AI Psychiatry allows investigators to rule out the AI and look for other causes such as a faulty camera.

Not just for autonomous vehicles

AI Psychiatry’s main algorithm is generic: It focuses on the universal components that all AI models must have to make decisions. This makes our approach readily extendable to any AI models that use popular AI development frameworks. Anyone working to investigate a possible AI failure can use our system to assess a model without prior knowledge of its exact architecture.

Whether the AI is a bot that makes product recommendations or a system that guides autonomous drone fleets, AI Psychiatry can recover and rehost the AI for analysis. AI Psychiatry is entirely open source for any investigator to use.

AI Psychiatry can also serve as a valuable tool for conducting audits on AI systems before problems arise. With government agencies from law enforcement to child protective services integrating AI systems into their workflows, AI audits are becoming an increasingly common oversight requirement at the state level. With a tool like AI Psychiatry in hand, auditors can apply a consistent forensic methodology across diverse AI platforms and deployments.

In the long run, this will pay meaningful dividends both for the creators of AI systems and everyone affected by the tasks they perform.The Conversation

David Oygenblik, Ph.D. Student in Electrical and Computer Engineering, Georgia Institute of Technology and Brendan Saltaformaggio, Associate Professor of Cybersecurity and Privacy, and Electrical and Computer Engineering, Georgia Institute of Technology

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The post Forensics tool ‘reanimates’ the ‘brains’ of AIs that fail in order to understand what went wrong appeared first on theconversation.com



Note: The following A.I. based commentary is not part of the original article, reproduced above, but is offered in the hopes that it will promote greater media literacy and critical thinking, by making any potential bias more visible to the reader –Staff Editor.

Political Bias Rating: Centrist

The article focuses on the development of a forensic tool, AI Psychiatry, designed to investigate the failure of AI systems. It provides technical insights into how this tool can help investigate and address AI failures, particularly in autonomous vehicles, without promoting any ideological stance. The content is centered on technological advancements and their practical applications, with an emphasis on problem-solving and transparency in AI systems. The tone is neutral, focusing on factual reporting about AI forensics and the technical capabilities of the system. There is no discernible political bias in the article, as it largely sticks to technical and academic subjects without introducing political viewpoints.

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