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AI hallucination in Mike Lindell case serves as a stark warning : NPR

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MyPillow CEO Mike Lindell arrives at a gathering of supporters of Donald Trump near Trump’s residence in Palm Beach, Fla., on April 4, 2023. On July 7, 2025, Lindell’s lawyers were fined thousands of dollars for submitting a legal filing riddled with AI-generated mistakes.

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A federal judge ordered two attorneys representing MyPillow CEO Mike Lindell in a Colorado defamation case to pay $3,000 each after they used artificial intelligence to prepare a court filing filled with a host of mistakes and citations of cases that didn’t exist.

Christopher Kachouroff and Jennifer DeMaster violated court rules when they filed the document in February filled with more than two dozen mistakes — including hallucinated cases, meaning fake cases made up by AI tools, Judge Nina Y. Wang of the U.S. District Court in Denver ruled Monday.

“Notwithstanding any suggestion to the contrary, this Court derives no joy from sanctioning attorneys who appear before it,” Wang wrote in her decision. “Indeed, federal courts rely upon the assistance of attorneys as officers of the court for the efficient and fair administration of justice.”

The use of AI by lawyers in court is not, itself illegal. But Wang found the lawyers violated a federal rule that requires lawyers to certify that claims they make in court are “well grounded” in the law. Turns out, fake cases don’t meet that bar.

Kachouroff and DeMaster didn’t respond to NPR’s request for comment.

The error-riddled court filing was part of a defamation case involving Lindell, the MyPillow creator, President Trump supporter and conspiracy theorist known for spreading lies about the 2020 election. Last month, Lindell lost this case being argued in front of Wang. He was ordered to pay Eric Coomer, a former employee of Denver-based Dominion Voting Systems, more than $2 million after claiming Coomer and Dominion used election equipment to flip votes to former President Joe Biden.

The financial sanctions, and reputational damage, for the two lawyers are a stark reminder for attorneys who, like many others, are increasingly using artificial intelligence in their work, according to Maura Grossman, a professor at the University of Waterloo’s David R. Cheriton School of Computer Science and an adjunct law professor at York University’s Osgoode Hall Law School.

Grossman said the $3,000 fines “in the scheme of things was reasonably light, given these were not unsophisticated lawyers who just really wouldn’t know better. The kind of errors that were made here … were egregious.”

There have been a host of high-profile cases where the use of generative AI has gone wrong for lawyers and others filing legal cases, Grossman said. It’s become a familiar trend in courtrooms across the country: Lawyers are sanctioned for submitting motions and other court filings filled with case citations that are not real and created by generative AI.

Damien Charlotin tracks court cases from across the world where generative AI produced hallucinated content and where a court or tribunal specifically levied warnings or other punishments. There are 206 cases identified as of Thursday — and that’s only since the spring, he told NPR. There were very few cases before April, he said, but for months since there have been cases “popping up every day.”

Charlotin’s database doesn’t cover every single case where there is a hallucination. But he said, “I suspect there are many, many, many more, but just a lot of courts and parties prefer not to address it because it’s very embarrassing for everyone involved.”

What went wrong in the MyPillow filing

The $3,000 fine for each attorney, Judge Wang wrote in her order this week, is “the least severe sanction adequate to deter and punish defense counsel in this instance.”

The judge wrote that the two attorneys didn’t provide any proper explanation of how these mistakes happened, “most egregiously, citation of cases that do not exist.”

Wang also said Kachouroff and DeMaster were not forthcoming when questioned about whether the motion was generated using artificial intelligence.

Kachouroff, in response, said in court documents that it was DeMaster who “mistakenly filed” a draft version of this filing rather than the right copy that was more carefully edited and didn’t include hallucinated cases.

But Wang wasn’t persuaded that the submission of the filing was an “inadvertent error.” In fact, she called out Kachouroff for not being honest when she questioned him.

“Not until this Court asked Mr. Kachouroff directly whether the Opposition was the product of generative artificial intelligence did Mr. Kachouroff admit that he did, in fact, use generative artificial intelligence,” Wang wrote.

Grossman advised other lawyers who find themselves in the same position as Kachouroff to not attempt to cover it up, and fess up to the judge as soon as possible.

“You are likely to get a harsher penalty if you don’t come clean,” she said.

An illustration picture shows ChatGPT artificial intelligence software, which generates human-like conversation, in February 2023 in Lierde, Belgium. Experts say AI can be incredibly useful for lawyers — they just have to verify their work.

An illustration picture shows ChatGPT artificial intelligence software, which generates human-like conversation, in February 2023 in Lierde, Belgium. Experts say AI can be incredibly useful for lawyers — they just have to verify their work.

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Trust and verify

Charlotin has found three main issues when lawyers, or others, use AI to file court documents: The first are the fake cases created, or hallucinated, by AI chatbots.

The second is AI creates a fake quote from a real case.

The third is harder to spot, he said. That’s when the citation and case name are correct but the legal argument being cited is not actually supported by the case that is sourced, Charlotin said.

This case involving the MyPillow lawyers is just a microcosm of the growing dilemma of how courts and lawyers can strike the balance between welcoming life-changing technology and using it responsibly in court. The use of AI is growing faster than authorities can make guardrails around its use.

It’s even being used to present evidence in court, Grossman said, and to provide victim impact statements.

Earlier this year, a judge on a New York state appeals court was furious after a plaintiff, representing himself, tried to use a younger, more handsome AI-generated avatar to argue his case for him, CNN reported. That was swiftly shut down.

Despite the cautionary tales that make headlines, both Grossman and Charlotin view AI as an incredibly useful tool for lawyers and one they predict will be used in court more, not less.

Rules over how best to use AI differ from one jurisdiction to the next. Judges have created their own standards, requiring lawyers and those representing themselves in court to submit AI disclosures when it’s been used. In a few instances judges in North Carolina, Ohio, Illinois and Montana have established various prohibitions on the use of AI in their courtrooms, according to a database created by the law firm Ropes & Gray.

The American Bar Association, the national representative of the legal profession, issued its first ethical guidance on the use of AI last year. The organization warned that because these tools “are subject to mistakes, lawyers’ uncritical reliance on content created by a [generative artificial intelligence] tool can result in inaccurate legal advice to clients or misleading representations to courts and third parties.”

It continued, “Therefore, a lawyer’s reliance on, or submission of, a GAI tool’s output—without an appropriate degree of independent verification or review of its output—could violate the duty to provide competent representation …”

The Advisory Committee on Evidence Rules, the group responsible for studying and recommending changes to the national rules of evidence for federal courts, has been slow to act and is still working on amendments for the use of AI for evidence.

In the meantime, Grossman has this suggestion for anyone who uses AI: “Trust nothing, verify everything.”



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Tampa General Hospital, USF developing artificial intelligence to monitor NICU baby’s pain in real-time

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Researchers are looking to use artificial intelligence to detect when a baby is in pain.

The backstory:

A baby’s cry is enough to alert anyone that something’s wrong. But for some of the most critical babies in hospital care, they can’t cry when they are hurting.

READ: FDA approves first AI tool to predict breast cancer risk

“As a bedside nurse, it is very hard. You are trying to read from the signals from the baby,” said Marcia Kneusel, a clinical research nurse with TGH and USF Muma NICU.

With more than 20 years working in the neonatal intensive care unit, Kneusel said nurses read vital signs and rely on their experience to care for the infants.

“However, it really, it’s not as clearly defined as if you had a machine that could do that for you,” she said.

MORE: USF doctor enters final year of research to see if AI can detect vocal diseases

Big picture view:

That’s where a study by the University of South Florida comes in. USF is working with TGH to develop artificial intelligence to detect a baby’s pain in real-time.

“We’re going to have a camera system basically facing the infant. And the camera system will be able to look at the facial expression, body motion, and hear the crying sound, and also getting the vital signal,” said Yu Sun, a robotics and AI professor at USF.

Yu heads up research on USF’s AI study, and he said it’s part of a two-year $1.2 million National Institutes of Health grant.

He said the study will capture data by recording video of the babies before a procedure for a baseline. Video will record the babies for 72 hours after the procedure, then be loaded into a computer to create the AI program. It will help tell the computer how to use the same basic signals a nurse looks at to pinpoint pain.

READ: These states are spending the most on health insurance, study shows

“Then there’s alarm will be sent to the nurse, the nurse will come and check the situation, decide how to treat the pain,” said Sun.

What they’re saying:

Kneusel said there’s been a lot of change over the years in the NICU world with how medical professionals handle infant pain.

“There was a time period we just gave lots of meds, and then we realized that that wasn’t a good thing. And so we switched to as many non-pharmacological agents as we could, but then, you know, our baby’s in pain. So, I’ve seen a lot of change,” said Kneusel.

Why you should care:

Nurses like Kneusel said the study could change their care for the better.

“I’ve been in this world for a long time, and these babies are dear to me. You really don’t want to see them in pain, and you don’t want to do anything that isn’t in their best interest,” said Kneusel.

MORE: California woman gets married after lifesaving surgery to remove 40-pound tumor

USF said there are 120 babies participating in the study, not just at TGH but also at Stanford University Hospital in California and Inova Hospital in Virginia.

What’s next:

Sun said the study is in the first phase of gathering the technological data and developing the AI model. The next phase will be clinical trials for real world testing in hospital settings, and it would be through a $4 million NIH grant, Sun said.

The Source: The information used in this story was gathered by FOX13’s Briona Arradondo from the University of South Florida and Tampa General Hospital.

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Ramp Debuts AI Agents Designed for Company Controllers

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Financial operations platform Ramp has debuted its first artificial intelligence (AI) agents.

The new offering is designed for controllers, helping them to automatically enforce company expense policies, block unauthorized spending, and stop fraud, and is the first in a series of agents slated for release this year, the company said in a Thursday (July 10) news release.

“Finance teams are being asked to do more with less, yet the function remains largely manual,” Ramp said in the release. “Teams using legacy platforms today spend up to 70% of their time on tasks like expense review, policy enforcement, and compliance audits. As a result, 59% of professionals in controllership roles report making several errors each month.”

Ramp says its controller-centric agents solve these issues by doing away with redundant tasks, and working autonomously to go over expenses and enforce policy, applying “context-aware, human-like” reasoning to manage entire workflows on their own.

“Unlike traditional automation that relies on basic rules and conditional logic, these agents reason and act on behalf of the finance team, working independently to enforce spend policies at scale, immediately prevent violations, and continuously improve company spending guidelines,” the release added.

PYMNTS wrote earlier this week about the “promise of agentic AI,” systems that not only generate content or parse data, but move beyond passive tasks to make decisions, initiate workflows and even interact with other software to complete projects.

“It’s AI not just with brains, but with agency,” that report said.

Industries including finance, logistics and healthcare are using these tools for things like booking meetings, processing invoices or managing entire workflows autonomously.

But although some corporate leaders might hold lofty views for autonomous AI, the latest PYMNTS Intelligence in the June 2025 CAIO Report, “AI at the Crossroads: Agentic Ambitions Meet Operational Realities,” shows a trust gap among executives when it comes to agentic AI that highlights serious concerns about accountability and compliance.

“However, full-scale enterprise adoption remains limited,” PYMNTS wrote. “Despite growing capabilities, agentic AI is being deployed in experimental or limited pilot settings, with the majority of systems operating under human supervision.”

But what makes mid-market companies uneasy about tapping into the power of autonomous AI? The answer is strategic and psychological, PYMNTS added, noting that while the technological potential is enormous, the readiness of systems (and humans) is much murkier.

“For AI to take action autonomously, executives must trust not just the output, but the entire decision-making process behind it. That trust is hard to earn — and easy to lose,” PYMNTS wrote, noting that the research “found that 80% of high-automation enterprises cite data security and privacy as their top concern with agentic AI.”



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How automation is using the latest technology across various sectors

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Artificial Intelligence and automation are often used interchangeably. While the technologies are similar, the concepts are different. Automation is often used to reduce human labor for routine or predictable tasks, while A.I. simulates human intelligence that can eventually act independently.

“Artificial intelligence is a way of making workers more productive, and whether or not that enhanced productivity leads to more jobs or less jobs really depends on a field-by-field basis,” said senior advisor Gregory Allen with the Wadhwani A.I. center at the Center for Strategic and International Studies. “Past examples of automation, such as agriculture, in the 1920s, roughly one out of every three workers in America worked on a farm. And there was about 100 million Americans then. Fast forward to today, and we have a country of more than 300 million people, but less than 1% of Americans do their work on a farm.”

A similar trend happened throughout the manufacturing sector. At the end of the year 2000, there were more than 17 million manufacturing workers according to the U.S. Bureau of Labor statistics and the Federal Reserve Bank of St. Louis. As of June, there are 12.7 million workers. Research from the University of Chicago found, while automation had little effect on overall employment, robots did impact the manufacturing sector. 

“Tractors made farmers vastly more productive, but that didn’t result in more farming jobs. It just resulted in much more productivity in agriculture,” Allen said.

ARTIFICIAL INTELLIGENCE DRIVES DEMAND FOR ELECTRIC GRID UPDATE

Researchers are able to analyze the performance of Major League Baseball pitchers by using A.I. algorithms and stadium camera systems. (University of Waterloo / Fox News)

According to our Fox News Polling, just 3% of voters expressed fear over A.I.’s threat to jobs when asked about their first reaction to the technology without a listed set of responses. Overall, 43% gave negative reviews while 26% reacted positively.

Robots now are being trained to work alongside humans. Some have been built to help with household chores, address worker shortages in certain sectors and even participate in robotic sporting events.

The most recent data from the International Federation of Robotics found more than 4 million robots working in factories around the world in 2023. 70% of new robots deployed that year, began work alongside humans in Asia. Many of those now incorporate artificial intelligence to enhance productivity.

“We’re seeing a labor shortage actually in many industries, automotive, transportation and so on, where the older generation is going into retirement. The middle generation is not interested in those tasks anymore and the younger generation for sure wants to do other things,” Arnaud Robert with Hexagon Robotics Division told Reuters.

Hexagon is developing a robot called AEON. The humanoid is built to work in live industrial settings and has an A.I. driven system with special intelligence. Its wheels help it move four times faster than humans typically walk. The bot can also go up steps while mapping its surroundings with 22 sensors.

ARTIFICIAL INTELLIGENCE FUELS BIG TECH PARTNERSHIPS WITH NUCLEAR ENERGY PRODUCERS

gif of AI rendering of pitching throwing a ball

Researchers are able to create 3D models of pitchers, which athletes and trainers could study from multiple angles. (University of Waterloo)

“What you see with technology waves is that there is an adjustment that the economy has to make, but ultimately, it makes our economy more dynamic,” White House A.I. and Crypto Czar David Sacks said. “It increases the wealth of our economy and the size of our economy, and it ultimately improves productivity and wages.”

Driverless cars are also using A.I. to safely hit the road. Waymo uses detailed maps and real-time sensor data to determine its location at all times.

“The more they send these vehicles out with a bunch of sensors that are gathering data as they drive every additional mile, they’re creating more data for that training data set,” Allen said.

Even major league sports are using automation, and in some cases artificial intelligence. Researchers at the University of Waterloo in Canada are using A.I. algorithms and stadium camera systems to analyze Major League Baseball pitcher performance. The Baltimore Orioles joint-funded the project called Pitchernet, which could help improve form and prevent injuries. Using Hawk-Eye Innovations camera systems and smartphone video, researchers created 3D models of pitchers that athletes and trainers could study from multiple angles. Unlike most video, the models remove blurriness, giving a clearer view of the pitcher’s movements. Researchers are also exploring using the Pitchernet technology in batting and other sports like hockey and basketball.

ELON MUSK PREDICTS ROBOTS WILL OUTSHINE EVEN THE BEST SURGEONS WITHIN 5 YEARS

graphic overview of ptichernet system of baseball player's pitching skills

Overview of a PitcherNet System graphics analyzing a pitcher’s baseball throw. (University of Waterloo)

The same technology is also being used as part of testing for an Automated Ball-Strike System, or ABS. Triple-A minor league teams have been using the so-called robot umpires for the past few seasons. Teams tested both situations in which the technology called every pitch and when it was used as challenge system. Major League Baseball also began testing the challenge system in 13 of its spring training parks across Florida and Arizona this February and March.

Each team started a game with two challenges. The batter, pitcher and catcher were the only players who could contest a ball-strike call. Teams lost a challenge if the umpire’s original call was confirmed. The system allowed umpires to keep their jobs, while strike zone calls were slightly more accurate. According to MLB, just 2.6% of calls were challenged throughout spring training games that incorporated ABS. 52.2% of those challenges were overturned. Catchers had the highest success rate at 56%, followed by batters at 50% and pitchers at 41%.

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Triple-A announced last summer it would shift to a full challenge system. MLB commissioner Rob Manfred said in June, MLB could incorporate the automated system into its regular season as soon as 2026. The Athletic reports, major league teams would use the same challenge system from spring training, with human umpires still making the majority of the calls.

Many companies across other sectors agree that machines should not go unsupervised.

“I think that we should always ensure that AI remains under human control,” Microsoft Vice Chair and President Brad Smith said.  “One of first proposals we made early in 2023 was to insure that A.I., always has an off switch, that it has an emergency brake. Now that’s the way high-speed trains work. That’s the way the school buses, we put our children on, work. Let’s ensure that AI works this way as well.”



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