Every breath you fake

Summary

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Podcast Summary: "Every breath you fake"

Podcast: Click Here
Host: Dena Temple Raston (Recorded Future News)
Episode Date: April 7, 2026
Featured Reporter: Karen Duffin
Key Guest: Dr. Mark Frank (Professor of Communication, University at Buffalo)

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Episode Overview

This episode dives into the rapidly evolving field of "emotion AI"—technologies designed to read and interpret human feelings from facial expressions using artificial intelligence. Host Dena Temple Raston and reporter Karen Duffin explore the science behind emotional detection, how it’s being tested in real-life settings, its potential benefits, and the serious concerns it raises about privacy, bias, and our own social capabilities. With insights from Dr. Mark Frank, an expert whose career spans from reading faces as a bar bouncer to developing emotion-recognition tech, the episode examines whether AI could someday understand us better than we understand each other—and what might be lost in that exchange.

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Key Discussion Points and Insights

1. The Human Skill of Reading Emotions

• Instinctive Social Judgments:
  • Karen Duffin explains how, every day, people "decode furrowed brows" or "measure half smiles," a deeply ingrained survival skill. (03:32)
• Mark Frank’s Origin Story:
  • Frank learned to read subtle cues as a college-bar bouncer in the 1980s, where recognizing who might cause trouble was "a survival skill." (02:21)
  • "Bodies talk, and they often reveal truths that we're trying to hide, sometimes in the smallest of ways." —Karen Duffin (02:53)

2. The Science of Facial Expressions

• Influence of Paul Ekman:
  • Frank was inspired by Ekman, the researcher who mapped 10,000 facial expressions and created a coding system, which became a tool across law enforcement, therapy, and animation. (03:41)
  • "Ekman believed that emotions leave physical signatures on the face. So he built a system to measure them muscle by muscle." —Karen Duffin (03:52)
• Genuine vs. Fake Expressions:
  • True emotion comes from the brain’s limbic system and shows up automatically on the face, while faked expressions are constructed and less fluid. (05:10)
  • "Emotions are automatic. Expressions are often deliberate, which means you can manipulate an expression." —Karen Duffin (05:52)

3. Man vs. Machine: Who Detects Emotion Better?

• Testing Human vs AI Abilities:
  • Frank describes experiments where humans were 55% accurate at distinguishing real pain from fake, while AI computer vision systems reached 85%. (07:08)
  • "Human judges are not really picking this up with their eyes, but the computer vision systems are able to pick that up." —Mark Frank (07:33)
• Machines Spot Subtle Differences:
  • AI excels at noticing micro-differences in the "flow" of facial expressions—details invisible to most people. (07:19)

4. Using Emotion AI in Real Life

• AI as a Teacher's Aid:
  • Frank’s team worked on using AI to detect when children with learning disabilities are attentive, bored, or frustrated, acting as "an extra teacher’s aid." (10:08)
  • "So creating systems that can interact with the child to facilitate their development... is obviously, I think, a really positive thing." —Mark Frank (10:22)

5. The Data and Context Challenge

• Narrow Data Sets:
  • Many AI systems are trained on small, subjective facial databases, sometimes just a few hundred images, leading to "a very thin foundation." (10:51)
  • "A lot of these data sets... were not really normed on anything all that impressive. It might be a handful of graduate students just making their judgments." —Mark Frank (11:04)
• Ambiguity and Context:
  • AI struggles to interpret why someone feels an emotion, sometimes leading to dangerous misclassifications (e.g., reading airport frustration as aggression). (12:34)
  • "Machines don't know what happened five seconds earlier. And that missing context can make all the difference." —Karen Duffin (12:50)

6. AI in Society: Promise and Peril

• Emotion as Data and Commodity:
  • There is concern about companies monetizing emotional data—raising the risk they’ll "press our buttons" for profit or manipulation. (14:31)
  • "If you can start pushing the anger button, you know, the next thing you know, then you know people start to get hurt." —Mark Frank (15:00)
• Commercial Deployment and Risks:
  • Companies like Neurologica are already deploying this tech at scale in venues like soccer stadiums and airports to gauge and influence crowd emotions for advertising. (15:13)
  • "We can determine whether a crowd is interested, enthusiastic, bored, excited and dynamically change advertising." —Neurologica rep (15:13)
• Regulatory Landscape:
  • The EU moved in 2024 to ban emotion AI in workplaces and schools over concerns of bias and misinterpretation. (18:52)
  • "This technology is trying to interpret human feelings, something that's already hard for people to agree on." —Karen Duffin (19:06)
• Human Biases Become Machine Biases:
  • "AI makes the same mistakes that people do, and often in a more extreme fashion." —Mark Frank (19:31)

7. The Bigger Picture: What Could We Lose?

• Empathy and Social Skills:
  • Frank warns of the "cost" if we let AI take over our emotional readings: humans might atrophy their own empathy skills, just as relying on AI for thinking can lead to "souffle brains." (21:14)
  • "There's a correlation between how much time you spend on social media and how good you are at reading subtle emotions. ...The more time people are in these little social media things, the worse they are at reading emotions and just interacting with people face to face." —Mark Frank (21:31)
• Empathy is Learned, Not Installed:
  • "We learn empathy, he says, through face to face interaction... You have to have those experiences, like exercise. You have to go to the gym. You got to actually move something physically to build a muscle. Well, you have to move your brain to do this." —Mark Frank (22:14)
• Machines Alone Can't Be Trusted:
  • Frank is adamant that AI must augment, not replace, human judgment:
    • "There is the risk a lot of people are just going to turn their brains off and just go with what the AI says. It was not designed to be a standalone system." (20:47)

8. A Note on Dual-Use Technology

• Knife Metaphor:
  • "Each coin has two sides, and, you know, knives can be used to cut up a lovely mule or a human being..." —Mark Frank (19:46)
  • Emphasizes that technology, like knives or AI, is not inherently good or bad—it's how it's used that matters.

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Notable Quotes & Memorable Moments

• Mark Frank on Human-AI Partnership:

  • "Humans have an inborn ability to read the room, so to speak, and we understand context. AI excels at reading those tiny dynamic changes that help distinguish lies from truth, and the combination of those strengths could probably be helpful if it's done right." (20:10)

• Karen Duffin on Empathy:

  • "Because empathy, he says, isn't something you can just install. It's something you practice. Because recognizing emotion isn't the same as understanding it." (22:46)

• Concluding Reflection:

  • "Maybe that's the line we hold onto here. Not whether machines can read us, but what do we lose if we stop reading each other?" —Dena Temple Raston (22:57)

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Timestamps for Key Segments

• 00:02–01:15: Episode introduction: Can machines read our emotions?
• 02:11–03:24: Mark Frank’s early career and learning to read people
• 03:37–04:20: Frank’s collaboration with Paul Ekman on facial expressions
• 05:00–06:27: Differences between genuine and fake emotions; the science of "flow"
• 07:08–07:41: Man vs. machine: AI beats humans in detecting real vs. fake pain
• 08:06–08:15 / 10:08–10:22: Using AI to help children with learning disabilities in classrooms
• 10:51–12:13: Problems with data sets and context in emotion AI
• 14:31–15:32: The commodification of emotional data and commercial deployments
• 18:52–19:37: EU regulation and the limitations and biases of emotion AI
• 20:47–22:26: Risks of reliance on AI; the necessity of human empathy and social practice
• 22:57: Final reflections on the episode's core message

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Tone and Style

• The episode features clear, accessible explanations, avoiding jargon but diving deep into the science and societal implications.
• Mark Frank’s tone balances enthusiasm for AI’s potential to help (especially vulnerable populations) with strong caution and questions about misuse, bias, and what it means for human relationships.

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Takeaway

"Every breath you fake" thoughtfully unpacks the promise and peril of emotion AI. While machines might soon “know” what we’re feeling—even better than other people can—understanding why we feel what we do remains a distinctly human challenge. As the technology advances, the episode urges listeners to consider not just what AI can do, but what we might lose if we stop practicing empathy and reading each other in real life.

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Transcript

[Dena Temple Raston]

From recorded future news and prx, this is click here. Artificial intelligence is supposed to help us understand the world. But what if it's starting somewhere else with us? Not just what we click or where we go, but what we feel. AI can already plan your vacation, manage your calendar, even write thank you notes. But now researchers are asking a more intimate question. Can a machine look at your face and know what's going on inside your head? And maybe more unsettling, could it be better at it than we are? From Recorded Future News and prx, this is Click Here, a podcast about the people making and breaking our digital world. This week, Karen Duffin follows a deceptively simple idea. Can a machine understand emotion just by looking at us?

[Mark Frank]

Human judges are not really picking this up with their eyes, but the computer vision systems are able to pick that up.

[Dena Temple Raston]

That's after the break. Stay with us.

[Karen Duffin]

I'm Karen Duffin and this is Click Here. Every day we navigate our jobs, our relationships, getting safely home at night by doing something so instinctive, we probably don't even realize we're doing it at all. We assess people's body language. We. We decode furrowed brows. We measure half smiles, trying to decipher what other people are feeling. Dr. Mark Frank has built an entire career on that. He's a professor of communications at the University at Buffalo, and he studies how emotion shows up on our faces, in our voices. But his research didn't exactly start in a lab.

[Mark Frank]

When I was an undergraduate, in order to make some money, I worked in a bar, and I used to, on the weekends, to bounce.

[Karen Duffin]

This was the 1980s. College bars were packed, testosterone thick in the air. It was Mark's job to figure out which guys were just loud and which ones were about to throw a punch. So reading people wasn't so much a hobby as it was a survival skill.

[Mark Frank]

If you can see and you anticipate, you can see how this person's looking at that other person and how this one's reacting, and you can kind of intervene before trouble is afoot. And I thought I got really good at reading people.

[Karen Duffin]

So good, he says, that the nights he worked the door, the number of fights went down. And what Mark learned, scanning those crowds was simple. Bodies talk, and they often reveal truths that we're trying to hide, sometimes in the smallest of ways.

[Mark Frank]

There were certain things, like somebody coming around the corner, if they were underage. There was always this little break in their stride. Very subtle, but detective, they told me, you know, better double check their id, you know, all this other kind of business.

[Karen Duffin]

Mark took that instinct to graduate school trying to answer this question.

[Mark Frank]

I'm really curious, like, how good are we reading people?

[Karen Duffin]

And that led him to someone else who was interested in the same question.

[Mark Frank]

And this is where I first come across the work of Paul Ekman, the

[Karen Duffin]

man who put micro expressions on the map. If you've seen the TV series Lie to Me, it was influenced by Ekman's work.

[Mark Frank]

I would know if you were telling the truth. Right. Also know if you're hiding something me from it.

[Karen Duffin]

Ekman believed that emotions leave physical signatures on the face. So he built a system to measure them muscle by muscle. And by the time he was through, ekman's map included 10,000 facial expressions. It became a tool for everyone from the FBI to therapists, and even animators use it to draw emotions more realistically. After Mark graduated, he went to work with Paul Ekman in his lab.

[Mark Frank]

So I went and worked with Paul Ekman for three years and just learned just so much. He's like the smartest guy I ever met.

[Karen Duffin]

Mark had developed an interest in deception. And this is one of the first things he and Paul Ekman studied together. At a very basic level. They mapped the difference between a genuine smile and a fake one. And it turns out emotions have two components. The emotion itself, which is more of an internal experience. And then there's the outward expression of that emotion. A smile, a laugh, an eye roll, and the inward expression, the feeling, so to speak. That part is hard to control.

[Mark Frank]

When you're feeling an emotion, it comes from down in the limbic system, and it travels through the extrapyramidal motor system, which works in a pulse of ballistic, like fashion.

[Karen Duffin]

In other words, emotions fire through us automatically, almost like a bullet. Like, say you remember something funny at

[Mark Frank]

a funeral, and it's inappropriate to laugh, but you feel that little, you know, like, bulge coming onto your face and you got, you know, like, sucked your cheeks to try not to laugh. Or the.

[Karen Duffin]

But not every expression comes from that same deep, rapid emotion system. Sometimes we manufacture them. Like when someone asks you to take a more formal picture.

[Mark Frank]

Okay, like, I'm going to smile. One, two, three, right. That comes from the same part of your brain that causes you to raise your hand up or down or give a thumbs up. Things that you're doing deliberately.

[Karen Duffin]

Emotions are automatic. Expressions are often deliberate, which means you can manipulate an expression. So if you want to spot deception, look at those expressions. A fake smile, a forced frown. And what Mark and Paul Ekman found is that the difference between a fake smile And a real one is in something very subtle. Flow. Specifically, the flow when a smile reaches the eyes. A real smile, they found, unfolds smoothly in a predictable way.

[Mark Frank]

So the flow, the dynamic qualities of the smiles, the duration tends to be more consistent, the onsets tend to be smooth, and so on.

[Karen Duffin]

But when someone fakes a smile, that flow becomes slightly mechanical because the brain is assembling it piece by piece. And it turns out that humans are actually not that good at spotting when that flow is off. So Mark began to wonder, would a machine be better at detecting that deception, at detecting when the flow is off? About 10 years ago, he decided to test this man versus machine and the task he gave them. Which was better at identifying real pain versus fake pain.

[Mark Frank]

The human judges were only about 55% correct in telling which was real pain from fake pain. Well, the computer vision system became 85% correct.

[Karen Duffin]

85%. And this came down to that flow thing we talked about before. Because machines can spot the micro differences between a grimace that develops naturally versus

[Mark Frank]

mechanically, human judges are not really picking this up with their eyes, but the computer vision systems are able to pick that up.

[Karen Duffin]

For about a decade now, Mark has been studying this distinction. When it comes to reading emotions, which parts are humans good at and which parts might machines actually do better and even trickier? Could we teach machines to get better at this? It's something that feels so innately human. When we come back, Mark puts that idea to the test. Not in a lab, but in a classroom.

[Mark Frank]

We were trying to develop these computer vision systems to interact with children and to recognize when the children are upset, when they're attentive, when they're bored.

[Karen Duffin]

Stay with us.

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[Dena Temple Raston]

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[Mark Frank]

Cyber security.

[Dena Temple Raston]

Why do things go viral?

[Karen Duffin]

Click here. Mark Frank started a project focused on children who have learning disabilities because there's a shortage of teachers trained to work with them. So Mark imagines AI acting almost like an extra teacher's aid, spotting when a child is frustrated or when they quietly

[Mark Frank]

check out because, you know, the kids aren't attentive, they're not going to learn anything, right?

[Karen Duffin]

The system flags the distracted student so the teacher can step in. And then the AI system gives the other kids something to do in the meantime.

[Mark Frank]

So creating systems that can interact with the child to facilitate their development, understanding, and so on and so forth is obviously, I think, a really positive thing.

[Karen Duffin]

Though Mark's enthusiasm comes with some heavy caveats. For starters, he's worried about the data they're using to train the algorithms.

[Mark Frank]

Well, this is one of the issues that we've just sort of discovered. Like, you read the papers, trained on well established data sets, you hear this phrase a lot.

[Karen Duffin]

But those well established data sets, often it's just a couple hundred images. And when you consider the vast range of human emotion, that is a very thin foundation.

[Mark Frank]

A lot of these data sets that were considered well established data sets were not really normed on anything all that impressive. It might be a handful of graduate students just making their judgments. Okay, this is anger, this is fear, this is sadness. And that was a little bit frightening as we did a deep dive into it.

[Karen Duffin]

And interpreting expressions is just very subjective. You know, a grimace to one person might read as anger to another. I mean, just look at the captions of celebrity tabloid photos.

[Mark Frank]

Here's celebrity X is upset at the cameraperson. Right? Okay, well, what exactly is upset? What emotion is that? And sometimes you look at them go, well, no, that doesn't look like they're upset.

[Karen Duffin]

In contrast, Paul Ekman's hand coded system, it used very strict physical criteria, a mathematical computation that he called action units.

[Mark Frank]

For example, Ekman's FAC system head, okay, with happiness is the action unit 6 +12. With fear, it's got action unit 20 this, this.

[Karen Duffin]

Not to mention, AI systems are fed in part, at least on data from the Internet. Which is of course full of conflicting and deceptive material.

[Mark Frank]

So you can only imagine if AI systems are trying to harvest the Internet for these various things, it's going to be taken off in some really strange directions.

[Karen Duffin]

And even if you fixed that data problem, there's another challenge. Context. Machines might recognize what an emotion is, but they can't tell you why someone feels it.

[Mark Frank]

You know, it's a two step process. First you have to recognize something, but then you have to interpret it. Okay, now was that the nervousness of the person who's lying and afraid to get, afraid of getting caught, or is that the nervousness of the, you know, innocent person who's afraid of being disbelieved?

[Karen Duffin]

Machines don't know what happened five seconds earlier. And that missing context can make all the difference. I mean, just imagine a scenario like this. Your friend texts you at the last minute that they can't pick you up at the airport and so you're annoyed and you're waiting in a long customs line and now you're standing there just frustrated. What if an AI system is scanning faces and reads that frustration as aggression and flags you as a potential threat and suddenly you're pulled out of line for questioning and facing possible detainment. You can see how that missing context becomes critical, especially as we start asking machines to make decisions based on the emotions they spot. And this is something that may not be hypothetical for long. There's been a recent explosion in emotion reading technology, what's known as emotion AI in sectors from healthcare to hr. Like AI that analyzes emotional responses in people who have dementia and Alzheimer's. Researchers are building systems to decode their emotional cues when they can no longer communicate verbally. Over in human resources departments, AI is being developed that will scan applicants faces during interviews, trying to infer personality traits or suitability. Different sectors, all with high stakes. The difference between landing the job or not getting it, between faster medical care or care that lags. Mark also worries about what happens when our emotions become data and that data

[Mark Frank]

becomes a commodity because that's what moves people, right? Emotions are about movement, you know, they're about behavioral intentions and what you're going to do.

[Karen Duffin]

Companies already monetize our behavior and if data about what's happening inside us, our emotions, if that becomes a commodity too. Mark worries it will allow companies to press our buttons more effectively. Not just make us shop more, but say, make us angrier than we already are.

[Mark Frank]

Ian, if you can start pushing the anger button, you know, the next thing you know, then you know people start to get hurt.

[Karen Duffin]

Some companies, like the Spanish startup Neurologica, are already building systems to read emotions at scale.

[Mark Frank]

Our technology is installed in soccer stadiums and airports, and we can determine whether a crowd is interested, enthusiastic, bored, excited and dynamically change Advertising.

[Dena Temple Raston]

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[Karen Duffin]

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[Karen Duffin]

Given all of this, it's perhaps no surprise that regulators are wary of the technology, particularly in the EU. They passed legislation in 2024 banning emotion AI at work and in schools, in part due to concerns it may exaggerate existing biases. Though the concern isn't just bias, which affects all AI, but something deeper. This technology is trying to interpret human feelings, something that's already hard for people to agree on. And while AI is doing the interpretation, it's humans with all of their biases who teach the machines.

[Mark Frank]

AI makes the same mistakes that people do, and often in a more extreme fashion.

[Karen Duffin]

Given all of Mark's reservations, it was hard to tell if he was enthusiastic about AI doing all of this, or skeptical.

[Mark Frank]

Yeah, it's, you know, each coin has two sides, and, you know, knives can be used to cut up a lovely mule or a human being, and Mark

[Karen Duffin]

is working hard to ensure that these systems fall on the helping, not hurting side of that coin. He's creating broader and more systematized data sets and focusing on efforts he thinks will truly benefit people, like those kids with learning disabilities. But here's the real key, Mark says. We have to get crystal clear about what humans are good at and what machines are. Humans have an inborn ability to read the room, so to speak, and we understand context. AI excels at reading those tiny dynamic changes that help distinguish lies from truth, and the combination of those strengths could probably be helpful if it's done right. But Mark believes that machines alone can't be trusted.

[Mark Frank]

There is the risk a lot of people are just going to turn their brains off and just go with what the AI says. It was not designed to be a

[Karen Duffin]

standalone system, and Mark's biggest concern isn't just misinterpretation. It's something more existential. What happens if we stop exercising our emotional muscles, our empathy muscles? We've already seen the effects of letting machines carry our mental load.

[Mark Frank]

There are some recent studies you Know, kids using AI on papers and using things that turn their brains into these like souffle brains I call it. You know, there's like nothing there. You poke it and it all goes away.

[Karen Duffin]

And Mark has seen early evidence that something similar happens with emotion and machines.

[Mark Frank]

There's a correlation between how much time you spend on social media and how good you are at reading subtle emotions. And the more time people are in these little social media things, the worse they are at reading emotions and just interacting with people face to face and evolutionarily. This is what we were designed for, right? We were designed for a face to face world where you interacted with somebody and all five of your senses are engaged as you interact with people and so on. And so people want to sit back and just sort of let AI do this for them. There will be a cost to that.

[Karen Duffin]

We learn empathy, he says, through face to face interaction by saying something hurtful and seeing its impact or meeting someone we thought we'd hate and finding out actually we like them.

[Mark Frank]

So you have to have those experiences, like exercise. You have to go to the gym. You got to actually move something physically to build a muscle. Well, you have to move your brain to do this.

[Karen Duffin]

Mark Frank started his career outside a bar, scanning faces for trouble. And now he's helping build machines that might do some of that work, too. Reading faces, searching for signals and human emotion. But he's careful about what those machines should do. Because empathy, he says, isn't something you can just install. It's something you practice. Because recognizing emotion isn't the same as understanding it.

[Dena Temple Raston]

That was Karen Duffin, and maybe that's the line we hold onto here. Not whether machines can read us, but what do we lose if we stop reading each other? I'm Dena Temple Raston, and this is Click here.

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[Dena Temple Raston]

Here's what you need to know about the tech world this week. It's Tuesday, April 7th. First, the federal government is stepping into a growing fight over a new kind of online betting. Mike Selig runs the federal agency overseeing prediction markets. He says they're here to stay. Nationwide prediction markets are basically websites where people can bet on real world events, everything from sports to elections to military conflicts. Now, Illinois, Connecticut, and Arizona want to regulate these sites and limit how they operate within state lines. They argue they're basically unlicensed gambling. But last week, the Trump administration sued those states, and the agency that regulates such things, the Commodity Futures Trading Commission, says these platforms aren't gambling at all. They're financial tools, more like stock markets than casinos. And they warned that if states make their own rules, it could open the door to fraud and weaker protection for users. Critics aren't convinced. Some lawmakers, especially Democrats, are now pushing to ban betting on things like elections and war altogether. So this is turning into a bigger question. Who gets to control this new kind of market, states or the federal government? And that fight could eventually land at the Supreme Court Court next Cambodia has extradited a man accused of helping power one of the world's largest online scam networks.

[Mark Frank]

A Cambodian and Chinese police joint task force arrested Chinese citizen Li Xiong.

[Dena Temple Raston]

Li Xiong is the former chairman of the Weihuan Group, a company that at its peak operated a cryptocurrency exchange, a bank, and an online marketplace. Researchers say that combination made it a kind of one stop shop for criminal groups. According to the US treasury, the network helped move at least $4 billion in illicit money, including funds linked to North Korean hackers. This all ties into a broader crackdown in Southeast Asia, where large scale scam compounds have trafficked workers from around the world, forcing them to run investment schemes, sometimes called pig butchering. Authorities are trying to dismantle these networks, but analysts warn even when one is taken down, another often takes its place. This is one of the biggest leaks in the AI world's history.

[Mark Frank]

This is not an April Fool's Day joke.

[Dena Temple Raston]

And one of the biggest AI companies in the world is dealing with an embarrassing leak. Anthropic, maker of the AI Assistant clone, accidentally exposed some of the internal instructions behind its coding tool. The company says it wasn't a hack just human error during a software release, no user data was exposed, but the leak did reveal something valuable how Anthropic guides its AI. Those instructions, called a hardness, shape how the system behaves, what it does and what it avoids. Now competitors and developers have a close, clearer blueprint on how Claude works, and security experts say it could also give hackers new ways to probe for weaknesses. People digging through the code found some unusual features, including something Anthropic calls dreaming, where the AI periodically organizes its own memory, and another mode where the system might go undercover and not identify itself as AI when posting code online. There was even a built in digital pet named Buddy. Anthropic has since tried to pull the code offline. One cybersecurity expert called the leak embarrassing but not dangerous. And full disclosure here. Anthropic is a financial supporter of this program through paid advertising, but that support doesn't influence our editorial decisions and finally, a small but long awaited change from Google Account holders will now be able to change their Gmail address name. In the past, if you wanted a new name, you'd have to start over with a brand new account. For years, if you didn't like your Gmail address, you were stuck with it. Now Google says US users can finally change their email address without losing their inbox, contacts or history. It's a fix for a very specific kind of digital regret, like that username you picked in middle school that somehow followed you into adulthood. There are limits. You can only change your address a few times, and if you switch, you're locked into a new one for a year. Your old email won't disappear and messages will still forward over. So yes, you can move on from Smart Guy 766, but you don't have to erase him completely. And for the record, my last name at Gmail seemed weird enough. Click Here is a production of recorded Future News and prx. Today's show was written and produced by Megan Dietre, Sean Powers, Erica Gajda, Zach Hirsch, and Casey Georgie. It was edited by Karen Duffin and Sarah Covedo and fact checked by Darren Ancrum. Original music is by Ben Levingston with additional music from Blue Dot Sessions. Our staff writer is Lucas Riley, our illustrator is Megan Gough, and our sound designers and engineers are Jake Cook and Jesse Niswonger. Find us on X or Facebook @ClickHerearShow or leave us a voice message at 6615CH. Talk. Sometimes we'll turn those moments into reporting, sometimes into a conversation, and sometimes into a future story. You'll hear on this show. I'm Dena Temple Raston, and thanks for listening.

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