No face to hide

Summary

Click Here — “No Face to Hide” (May 29, 2026)
Podcast by Recorded Future News
Host: Dina Temple-Raston

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Episode Overview

This episode delves into the profound ways that facial recognition technology is transforming the search for missing people, exploring both the miracles it affords distraught families and the sweeping privacy and ethical concerns it raises. The story centers on Crystal Catron’s desperate attempt to find her missing daughter, Callie, and broadens into a discussion about the implications of making faces searchable data points — for better and for worse.

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Key Discussion Points and Insights

The Personal Toll: Crystal and Callie’s Story

• Intimate Mother-Daughter Bond:
  Crystal Catron shares the extraordinarily close relationship she had with her daughter Callie, emphasizing their best-friend dynamic.

  "She was my firstborn. Oh. My best friend. I did everything with her and spent half of my life with her." — Crystal Catron (04:14)

• Descent into Disconnection:
  Callie, struggling with addiction, started vanishing for short periods, the disappearances growing longer and more ominous until she vanished entirely in 2022.

  "She wouldn't have went Halloween or Thanksgiving without calling me and the kids. I don't care how mad she was, she wouldn't have missed the holidays." — Crystal Catron (07:32)

• Traditional Search vs. Tech:
  Crystal’s search began with flyers and phone calls but highlighted the slow, rumor-driven nature of traditional searches for the missing — until technology entered the picture.

The Game-Changer: Facial Recognition in Missing Person Cases

• How It Works:
  Facial recognition transforms physical characteristics — the distance between the eyes, jawline, etc. — into a digital fingerprint searchable against billions of images.
• Law Enforcement’s View — Kevin Metcalfe:
  The former prosecutor explains how facial recognition, e.g., Clearview AI, revolutionized investigations.

  "When I was at the Oklahoma AG's office... it's one of the first tools, I said, you need this." (08:29)
  He recounts a case where investigators found a young exploitation victim’s identity within minutes after years of searching:
  "...within two minutes of taking that picture. I said, well, here she is. That's her. This is her now with her current family. This is her when she was a kid." — Kevin Metcalfe (09:15)

• Controversial Data Practices:
  Clearview’s massive, unconsented image scraping (from social media, news sites, etc.) is key to its effectiveness but has brought legal challenges and international scrutiny.
• Advantages and Risks:
  Facial recognition’s power lies in finding those without digital “paper trails” (like missing children), but once faces are indexed, anonymity becomes much harder.

Data Privacy and Bias: Beyond the Missing

• Data Permanence and Consent — Matthias Marx:
  After discovering his photo in Clearview’s database without consent, German researcher Matthias Marx requested removal under GDPR laws — only to find the system continually rebuilds itself, making erasure nearly impossible.

  "Now that Clearview AI had a copy of my biometric face print, that was more scary because I cannot change my face." — Matthias Marx (14:56)

• Algorithmic Bias — Ethan Zuckerman:
  The privacy concern is compounded by reliability issues:

  "Some of the world is digitally underrepresented. ...The bias is almost always a white guy selected a bunch of data. That data is not as representative as it should be, and therefore you end up inheriting a bunch of biases from it." — Ethan Zuckerman (16:11, 16:28)
  Facial recognition typically works best for people who are well-represented in the training data, which often excludes or misrepresents people of color and other marginalized groups.

• Social Implications:
  Uneven algorithmic effectiveness means some people are found quickly while others remain invisible; errors have real-world consequences, determining who gets found, missed, or wrongfully identified.

Grassroots Digital Efforts: Crowd-Sourcing Hope

• Digital Vigilance — Rhianna Bly:
  Non-law enforcement volunteers comb government databases and public records to help solve unidentified cases, often leveraging facial recognition tools (e.g., Pim Eyes).

  "Everyone deserves their name, regardless if they have family that's still living or may not have the best situation. We just try our best to make sure they go home for somebody." — Rhianna Bly (18:12)

• Social Media’s Power — and Trauma:
  In Callie’s case, it was one of Rhianna’s TikTok posts that led a friend to recognize her in autopsy photos — a harrowing but definitive end to Crystal’s search.

  "She's like, crystal, get on TikTok. And I'm like, TikTok. And I'm like, I don't have TikTok. ...she's sending me these autopsy pictures. ...they're posted on TikTok." — Crystal Catron (20:27)

Broader Questions Raised

• Miracle vs. Menace:
  While the technology performed a “miracle” for Crystal, the same underlying infrastructure that lets us find missing loved ones can track or expose anyone at any time.
• Societal Impact:
  The episode challenges listeners to consider the costs and benefits of living in a world where “recognition is the default,” asking:

  What does it mean for privacy and freedom when a face can be instantly identified — willingly or not — by strangers, authorities, or corporations?

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Memorable Quotes & Moments (with Timestamps)

• “A face can become data. The distance between the eyes, the curve of the jaw, the shape of the nose. Tiny measurements turned into a kind of digital fingerprint.” — Dina Temple-Raston (07:43)
• “This means everyone who has a copy of my face can then identify me. And that was also a bit scary.” — Matthias Marx (14:22)
• “When my password gets lost, I can change the password. Now that Clearview AI had a copy of my biometric face print, that was more scary because I cannot change my face.” — Matthias Marx (14:56)
• “The bias is almost always a white guy selected a bunch of data. ...you end up inheriting a bunch of biases from it.” — Ethan Zuckerman (16:28)
• “Everyone deserves their name, regardless if they have family that's still living or may not have the best situation.” — Rhianna Bly (18:12)
• “Her hair was cut off. She had a black eye. It looked like a hole, like, underneath her ear.” — Crystal Catron, on seeing Callie’s autopsy photos (21:10)

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Key Timestamps

• 00:02–08:29: Crystal Catron’s story — personal account and traditional search methods
• 08:29–10:31: Law enforcement turns to facial recognition — Kevin Metcalfe on Clearview AI
• 13:38–15:27: Data privacy and the permanence of biometric data — Matthias Marx’s experience
• 16:11–16:59: Algorithmic bias and societal blind spots — Ethan Zuckerman
• 18:12–21:19: Volunteer digital searchers & the power of social media — Rhianna Bly and the discovery of Callie
• 21:19–end: Reflection on the promise and peril of facial recognition tech

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Takeaway

“No Face to Hide” masterfully weaves the intimate pain of a mother’s search with the sweeping ramifications of facial recognition technology. The episode makes clear that while new tools can deliver miracle moments for the desperate, they also rewire society’s relationship to privacy, anonymity, and consent — raising the pressing question: what will we gain, and what do we risk losing, when our faces can be found anywhere, by anyone, at any time?

Transcript

[Dena Temple Ralston]

From recorded future news and prx, this is click here. When Crystal Catron's daughter Callie disappeared, her world narrowed into a single question. Where is she?

[Crystal Catron]

So I was asking around, leaving flyers like, have you seen Callie here lately?

[Dena Temple Ralston]

For most families of missing people, the search still looks like this. Flyers, phone calls, social media posts, all waiting for someone, somewhere to recognize a face. But now there's another way to look for someone. Facial recognition software. From Recorded Future News and prx, this is Click Here, a show about how technology is changing everything. I'm Dena Temple Ralston. And this week, how facial recognition is quietly reshaping the search for missing people. For some families, it can feel almost miraculous. A match, a lead, a second chance. But the same technology is making it harder to disappear and may also be making it harder to stay anonymous. And it raises a bigger question. What happens when recognition becomes the default? Stay with us. Support for Click Here comes from Servol. Every company says AI will make employees more productive. But most employees are still stuck waiting on it, waiting for app access and password resets, waiting for someone to fix a laptop issue so they can get back to work. That operational drag adds up fast, and IT teams are overwhelmed trying to keep up. Servil was built to automate that work. You describe what you want in plain English and Servl builds it for you. No complicated workflow, no consultants, just faster support and fewer tickets slowing everyone down. The platform is designed to eliminate repetitive tickets so it can focus on strategic work instead of constant firefighting. The company guarantees customers can automate 50% of it tickets. Learn more or start a free four week pilot at serval.com clickhere that's S-E-R-V-A-L.com clickhere servil.com clickhere Support for Click Here comes from NPR's Planet Money podcast. Planet Money has a knack for taking big, complicated stories and making them feel human. Take the conflict between the US And Iran in a recent episode. The show followed a Seattle comic book publisher trying to track down two comic books stuck on a container ship in the Strait of Hormuz. To explain what was happening, Planet Money called a man in Tehran and he described Iran's strategy like 5 seconds to pay or your ship doesn't get to pass through. Just like that, a geopolitical standoff turned into something that you could actually picture. That's the trick Planet Money pulls off over and over again. It funds the people living inside these enormous economic stories and through them, helps explain how the world really works. From global shipping to sanctions to why Pokemon cards are suddenly worth so much money, Planet Money makes complicated things feel surprisingly clear. Follow NPR's Planet Money podcast and understand how money shapes the world. Crystal catron was barely 19 when she gave birth to her daughter, Callie. And from the beginning, they were less like mother and daughter and more like two people growing up side by side.

[Crystal Catron]

She was my firstborn. Oh. My best friend. I did everything with her and spent half of my life with her.

[Dena Temple Ralston]

In some ways, they raised each other.

[Crystal Catron]

We've been pretty much everywhere. The beach, stargazing, and everything. Literally, my best friend.

[Dena Temple Ralston]

They shared road trips, inside jokes, even matching Disney tattoos. When Callie was about 16, Crystal got a Mickey Mouse tattoo on her leg.

[Crystal Catron]

It's all colored in. It looks just like Mickey Mouse with the red outfit on the big yellow shoes.

[Dena Temple Ralston]

So Callie got Minnie Mouse in that pink polka dot dress.

[Crystal Catron]

And every time we saw each other and hugged, we'd always make sure that they touched, too. It was just a thing.

[Dena Temple Ralston]

But over time, Crystal started to feel her daughter slipping away. Kelly had two children of her own. Then came the drugs. And slowly, the person Crystal knew started to fade.

[Crystal Catron]

I didn't know who she was. Like, she didn't act like she cared about anybody or anything. And the phone calls were getting further apart.

[Dena Temple Ralston]

By the time Callie was 24, disappearing had become part of a pattern. A few days gone, then a call, an apology, an excuse, and then it would happen again. But when she vanished in 2022, something felt different this time. No call came, no explanation. And mothers know things.

[Crystal Catron]

My stomach started hurting, and I was sitting there like, something is wrong with Callie and she needs to be found. Now,

[Dena Temple Ralston]

if Callie had been famous, her disappearance might have triggered helicopters overhead, reporters doing live shots outside the family home, photos pushed to millions of phones. But Callie wasn't famous. She was a young woman struggling with addiction. And for Crystal, it was just one mother trying to keep her daughter from disappearing.

[Crystal Catron]

So I was asking around, leaving flyers like, have you seen Callie here lately? I was asking everybody to please, like, let me know if they heard from her or talked to her or saw her.

[Dena Temple Ralston]

The search moved at the speed of rumor. One possible sighting, one more person saying they might have seen her. And then after weeks of dead ends, Crystal got this thing she'd been waiting for. A call from Callie.

[Crystal Catron]

I actually talked to her. She had first called and told me that she was in Chicago, that Chicago.

[Dena Temple Ralston]

Callie said she needed help and could her mom possibly come get her. Crystal didn't hesitate. She started mapping out the drive, asking for time off work, trying not to push too hard in case her daughter disappeared again before she got there.

[Crystal Catron]

And then I didn't hear anything from her after October 14th.

[Dena Temple Ralston]

Days passed, then weeks. Halloween came and went, then Thanksgiving, her father's birthday. Nothing.

[Crystal Catron]

She wouldn't have went Halloween or Thanksgiving without calling me and the kids. I don't care how mad she was, she wouldn't have missed the holidays.

[Dena Temple Ralston]

Crystal didn't know what to do next. All she really had was her daughter's face on flyers and old photos on her phone. And that turned out to matter more than she realized. Because a face on a flyer used to be something you handed to strangers at a gas station or taped to telephone poles. Now it's something software can search. A face can become data. The distance between the eyes, the curve of the jaw, the shape of the nose. Tiny measurements turned into a kind of digital fingerprint. And once that happens, the software can compare one face against millions, sometimes billions of other faces.

[Kevin Metcalfe]

When I was at the Oklahoma AG's office, when I was asked to come in and build their Human Trafficking Response Unit, it's one of the first tools, I said, you need this.

[Dena Temple Ralston]

This is Kevin Metcalfe. He's a former prosecutor who started the National Child Protection Task Force. It's a group that helps law enforcement find missing and exploited children. And he says facial recognition has changed investigations that once seemed impossible, including one case involving a young girl investigators had spent nearly a decade trying to identify. She kept appearing in child sexual abuse material online, but there was no name, no history, just a face. Then investigators ran her image through a program called Clearview AI and suddenly they

[Kevin Metcalfe]

had her name within two minutes of taking that picture. I said, well, here she is. That's her. This is her now with her current family. This is her when she was a kid.

[Dena Temple Ralston]

Two minutes after 10 years. That kind of speed can change everything. But it only works because systems like Clearview operate at enormous scale. The company built its database by scraping images from across the Internet. Social media, news sites, personal web pages, pretty much anywhere a face appeared.

[Kevin Metcalfe]

Clearview's database is made up of photos, all taken without permission, scraped right off

[Dena Temple Ralston]

the Internet and our social media accounts. Billions of images collected without asking the people in them. That's why Clearview has gotten cease and desist letters from companies like Google and Facebook and has faced fines and lawsuits in places like the UK and Italy. But for someone like Kevin Metcalfe, that scale is what makes the tool useful. Because missing children aren't usually in criminal databases or in driver's license records.

[Kevin Metcalfe]

Well, if you've got a six year old child, you can't run her face through a mugshot database because she is not going to be in there.

[Dena Temple Ralston]

That's the promise of facial recognition. It can find people who would otherwise stay invisible. Missing children, fugitives, people lost in disasters. But facial recognition cuts both ways, because once a face becomes searchable, it can stop being entirely yours. More on that after the break. Stay with us. Support for Clickure comes from Quince Lately I've been more intentional about what I wear day to day, leaning into pieces that feel effortless, comfortable, but still put together. It just makes getting dressed less of a choreography. And for a while now, Quince has been my go to. The fabrics feel elevated, the fits are flattering, and everything just works without overthinking it. Quince makes it easy to refresh your everyday this spring with pieces that feel as good as they look. They use premium materials like 100% European linen, organic cotton and ultra soft denim. Their lightweight linen pants, dresses and tops start at $30 and are effortless, breathable and easy to wear on repeat. Everything at Quint's is priced 50 to 80% less than similar brands. They work directly with ethical factories and cut out the middlemen. So you're paying for quality and craftsmanship, not brand markup. I just got a Quince bathing suit that looks like one of those expensive European brands, but for a fraction of the price. Refresh your everyday with luxury you'll actually use. Head to Quince.com clickhere for free shipping on your order and 365 day returns. Now available in Canada too. That's Q-U-I-N-C-E.com clickhere for free shipping and 365 day returns. Quints.com clickhere this show is supported by Odoo.

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[Mike Pesca]

Pesca, host of the Gist, the longest running podcast on news and analysis in the business. Every day I do, I think, a fascinating interview, but I also do a spiel where I will challenge you Because I hate listening to podcasts that just agree with each other and only agree with me. So I'm going to say 25% of the time you'll think you're wrong and 75% of the time you'll say, great point. Well said. Now, those numbers might be reversed. For some people, they probably shouldn't listen. Check out the gist every single day for a decade now.

[Dena Temple Ralston]

In 2021, Matthias Marx was an IT security researcher living in Hamburg, Germany. One morning he was reading the New York Times and came across a story about Clearview.

[Matthias Marx]

I was not aware that Clearview AI exists, and I read this article and just out of curiosity, I asked Clearview AI to provide a copy of my data. That is something I can do under the GDPR in Europe.

[Dena Temple Ralston]

GDPR, the General Data Protection Regulation. That's the EU's data privacy law. And under those rules, companies have to tell people what data they hold on them. So clearview responded. They had his face.

[Matthias Marx]

Which blew my mind, because I didn't expect to be in this search engine. This means everyone who has a copy of my face can then identify me. And that was also a bit scary.

[Dena Temple Ralston]

He asked them to delete it. Authorities ordered them to. But even then, there was a problem. Because databases like this don't stay frozen in time. They keep rebuilding themselves. New photos get uploaded, new faces get scraped again and again and again.

[Matthias Marx]

By now, I assume that Clearview AI has my face in their database again, and also many others without their consent. We all probably leave some data on the Internet, and usually this data get lost over time. When my password gets lost, I can change the password. Now that Clearview AI had a copy of my biometric face print, that was more scary because I cannot change my face.

[Dena Temple Ralston]

And that gets to the heart of the fear surrounding facial recognition. Your face isn't just another piece of data. It's permanent. And once it's searchable, it no longer belongs only to you. It can be tracked, copied, used without your permission by government scammers or anyone else who gets access. Just ask Ethan Zuckerman. He's an associate professor at the University of Massachusetts Amherst, and he has spent years studying the blind spots built into digital systems. And he says privacy is only part of the problem with facial recognition. There's another issue, too. These systems don't work equally well on everyone.

[Ethan Zuckerman]

Some of the world is digitally underrepresented. And this comes up in facial recognition. This comes up in AI.

[Dena Temple Ralston]

That imbalance often starts with the data these systems are trained on, who gets photographed who gets uploaded and who gets left out.

[Ethan Zuckerman]

The bias is almost always a white guy selected a bunch of data. That data is not as representative as it should be, and therefore you end up inheriting a bunch of biases from it.

[Dena Temple Ralston]

And as those systems learn from that data, the problems don't necessarily disappear.

[Ethan Zuckerman]

And what happens is peculiarities of that model get amplified through that training process. And over time, you end up with something that's wholly unreliable and that has not been been well solved yet, especially

[Dena Temple Ralston]

for people of color. Study after study has found that these systems are often less accurate at identifying them. Sometimes they fail to recognize people at all, and sometimes they identify the wrong person instead, which creates a dangerous contradiction. The technology can find some people almost instantly while completely misreading others. And those mistakes have consequences. They shape who gets stopped, who gets questioned, who gets found, and who gets missed. When the technology falls short, sometimes communities form around those gaps. There are online volunteers all across the country who spend hours combing through databases of the missing, trying to identify the people official systems overlook. Rhianna Bly is one of those people. She spends hours searching through the case files on namis, the federal database of missing and unidentified people. But Rhianna isn't a detective. She's not law enforcement. She's just someone who kept looking.

[Rhianna Bly]

Everyone deserves their name, regardless if they have family that's still living or may not have the best situation. We just try our best to make sure they go home for somebody.

[Dena Temple Ralston]

Over time, Rihanna noticed a pattern. The cases that seemed to stall were often those involving people without resources, the ones who never became national news. The people the system seemed to move past. So she started posting the cases herself. Photos, names, details. She built a Facebook group called the Unidentified and unsolved and then started putting cases on TikTok, hoping someone somewhere might recognize a face. And along the way, the group started experimenting with facial recognition tools like Pim Eyes.

[Rhianna Bly]

It's an advanced face recognition search engine, or it can be used as a reverse image search tool as well.

[Dena Temple Ralston]

Here's how it worked.

[Rhianna Bly]

I had a Jane Doe who was about the ages of 14 to 17, I think, in the New York area. It pulled a school website and it had different pictures of multiple children, which I felt was kind of od, but maybe similar to the case. Since she does look young.

[Dena Temple Ralston]

Pim Eyes pointed her toward a possible match. So Rhianna posted the images online, hoping someone else might see what she was seeing. And eventually someone did, and the girl got her name back. Now Rihanna's group functions like a kind of volunteer search party. Dozens of cases posted every day. Faces shared across TikTok and Facebook, hoping the algorithm puts them in front of the right person. And every once in a while, it works. A post travels further than expected and lands in front of someone who recognizes the face staring back at them. That's what happened with Callie. A friend was scrolling through TikTok when she came across one of Rihanna's posts. A photo, a familiar face. She stopped and looked again and realized she knew exactly who she was looking at. She called crystal right away.

[Crystal Catron]

She's like, crystal, get on TikTok. And I'm like, TikTok. And I'm like, I don't have TikTok. And so while I'm trying to install TikTok, she's sending me these autopsy pictures. Like, how are you getting these? And she's like, they're posted on TikTok.

[Dena Temple Ralston]

Finally, Crystal got the video to open.

[Crystal Catron]

Very sad song. Along with a slideshow of autopsy pictures.

[Dena Temple Ralston]

Photo after photo crossed the screen. And slowly, horribly, it became clear what she was looking at. They had found Callie.

[Crystal Catron]

Her hair was cut off. She had a black eye. It looked like a hole, like, underneath her ear.

[Dena Temple Ralston]

And then something she recognized immediately. The tattoo. Minnie mouse in the pink polka dot dress. It was her daughter. For months, Crystal had been searching for Kali. And in the end, it was if face online that finally gave her an answer. That's the promise of facial recognition technology. A face can travel further now, across platforms, across databases, across the world. But systems like these only work because they sort through billions of pictures of the rest of us to get there. Not just when we're missing, but while we're living ordinary lives. And once the system can identify almost anyone in a crowd, it doesn't stay limited to missing person cases for long. Sooner or later, someone asks what else it can do. This is click here. Click Here is a production of recorded future news and prx. Today's show was written and produced by Megan Dietre, Sean Powers, Erica gaeda, 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 at Click here. Show or leave us a voice message at 661-5ch. Talk. Sometimes we'll turn those moments into recording, 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.

[Recorded Future Announcer]

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[Recorded Future News Announcer]

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