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This is Amity Technology Review. There's a fight playing out in the courts between the networking site LinkedIn and a company called Kikue Labs, a startup that tells corporations when their employees are at risk of being poached by other companies. The problem is it does this by pulling data off of LinkedIn Web site.

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With more than 500 million users worldwide, LinkedIn is a treasure trove of personal information. But what of the information that you don't want to share is actually getting back to your boss.

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But High argues that's OK. All the data is publicly available without a login. The case may go to the Supreme Court this year, though so far the legal system agrees with haiku. Do you know what your rights to privacy are on websites like LinkedIn or YouTube? Would it be surprising that photos, including some you've never seen but somehow wound up on the web, are being used by companies to grow their businesses, including to build things like A.I. systems that identify suspects for police?

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I'm Jennifer Strong and I'm part two of our series on face recognition and policing. We speak with chief executive one contact the founder of one of the world's most controversial tech companies, Clearview A.I..

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Welcome to The Age of with predict what's possible in the age of with, then translate insight into trustworthy performance. Deloitte brings together end to end offerings with domain and industry insight to drive stronger outcomes through human and machine collaboration. Hmm, well, let's go in machines we trust, I'm listening to a podcast about the automation of everything.

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You have reached your destination. Back in 2011, Google CEO at the time, Eric Schmidt, gave a keynote interview at a conference hosted by The Wall Street Journal.

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I'm very concerned personally about the union of mobile tracking and face recognition.

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Combining face ID with the tracking data off of cell phones could give away almost every detail about how and where we spend our time.

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Schmidt said he believed it could be used for good or evil, but in democracies, he thought it would be regulated quickly. He was on stage with journalist Walt Mossberg and Kara Swisher, and they pressed him on what capabilities did Google have and what might happen in the wrong hands.

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We built that technology and we withheld it. As far as I know, it's the only technology that Google built. And after looking at it, we decided to stop.

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Fast forward nearly a decade. Facial recognition is still not regulated, and big tech is back to questioning what should be built and who should have it.

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Amazon says it is temporarily banning police and law enforcement from using its controversial facial recognition software.

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IBM said that it is getting rid of their facial recognition programs. The company is also calling for a public debate on law enforcement's use of it.

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Microsoft President Brad Smith said legislation on facial recognition should be firmly grounded in human rights.

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Thing is, tech giants aren't the biggest players in that space. Companies that are including NBC cognito can clear of UAE are continuing to sell their systems. And because facial recognition isn't regulated, unless a company decides to tell us these tools exist or a journalist uncover something, we won't necessarily know what's out there, let alone how it's used, even when it's used on us. A clear view is sometimes referred to as the killer app of Pharcyde. It's also incredibly controversial.

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It quietly scraped billions of images off the Internet from Venmo, LinkedIn, Google, Facebook, Twitter, YouTube and so on. And it built a system that's believed to be widely used by law enforcement, including the FBI and ICE, as well as state and local police. There are many legal battles being fought over this practice called web scraping, not because there's something inherently bad about it. It's just a tool for gathering data from the Internet. It's how websites offer price comparisons and real estate listings.

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It's also how a great deal of public research gets done. So the real issue is there aren't really ground rules for what can be scraped. And the federal law most often used to sort out these cases. Well, it's from 1986 before the Web even existed. Throughout this series, we're going to hear from folks who build technologies, as well as those who fight against them. And I sat down with clear views, chief executive to talk about all of this from his perspective.

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My name is Juan Contest and I'm the founder and CEO of Clearview.

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OK, how would you describe your company, the technology and what it does?

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Basically, it is a search engine for faces. So you upload a photo of a face and it finds publicly available links that are online. And right now it's used for law enforcement to solve crimes after the fact. So an officer, if they're stuck on a case and they have something from video footage, they can run it through our system and then start an investigation.

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He says it immediately appealed to law enforcement, but that was after he shopped it around to a few different groups when we were building our facial recognition technology.

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We explored many different ideas in many different sectors, from private security to hospitality. When we gave it to some people in law enforcement, the uptick was huge and they called us back the next day, said we're solving cases. This is crazy. And in a week's time, we had a really thick booklet.

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So where did you get the idea to create a clear view? What were your motivations?

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I've always loved to learn about computer programming since I was a kid looking at the MIT video lectures or doing open source projects and downloading images to train better models for facial recognition. And eventually that morphed into doing facial search engine. And it was just a surprise to me how many people really didn't tackle this idea because it's such a hard problem, because you have to be very, very accurate. But we stuck at it and it ended up working really well.

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There's a growing list of reasons why researchers might choose not to work on a search engine. It faces a big one is how that work might be applied.

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Take the case of Steve Talli, a financial analyst from Colorado. Craig Crawford is a privacy advocate. This is from her TED talk.

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In 2015, Talley was charged with bank robbery on the basis of an error in a facial recognition system. Halie fought that case and he eventually was cleared of those charges, but he lost his house, his job and his kids.

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Steve Tali's case is an example of what can happen when the technology fails, but face surveillance is just as dangerous when it works as advertised. Just consider how trivial it would be for a government agency to put a surveillance camera outside a building where people meet for Alcoholics Anonymous meetings.

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It would be just as easy to use this technology to automatically identify every person who attended the Women's March or Black Lives Matter protest.

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Though Tonto believes clear views tool is safer than what she's describing, and a false positive in a live setting is more of an issue than it is in a after the fact setting.

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Because if you're getting an alert and you running down to find the person, you have maybe a lot less time to see if it's correct. Whereas if you're behind a desk doing an investigation, you have all the time in the world to make sure you're doing the right thing.

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But there's no agreement on what doing the right thing means. An episode one, we met Robert Williams. He was wrongly arrested in just the type of investigation is talking about after software incorrectly matched his driver's license photo to pictures of someone stealing watches. The tool used in the case of Mr. Williams wasn't built by Clearview, but a company called Data Works, though both of these systems rely on neural networks.

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So a neural network is a newer form of artificial intelligence where instead of hard coding certain factors, for example, we want to do facial recognition to find similar faces of the same person, sort of hard coding factors like the distance between your eyes, whether it is between the eyes and the nose, it just learns from a ton of different examples. And so what we do is we collect like a thousand examples of George Clooney or a thousand examples of Brad Pitt and the machine.

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Over time, it learns the difference between those two faces and then it can apply it to a face that hasn't seen before. Something everyone we spoke to for this series agreed on is that these systems work best when the lighting is good and cameras are placed at face height. But with security cameras, that's rare. There's also the challenge of scale.

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How do you search billions of faces or vectors in under a second typical database's look up by name and email? It looks up by similarity and doing that at scale as hard, we had to get our own data center as well for that. Typically, if you're buying a facial recognition system, there's a cold start problem. What photos do you put in there? So police department might have their own mug shots, but they don't have mug shots from other police departments.

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So it really limits the usefulness of it. And we just realized that trillions and trillions of Web pages on the Internet and on social media and news sites, mug shot websites, we're just a few miles away from each other in New York City.

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But because of the pandemic, we're chatting over Zoom.

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Jennifer, I took a screenshot of a photo of you before. You mind if I upload it?

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No, that's fine. And he puts an old. Photo from my LinkedIn account up on the screen, on the left side, you see this new search button where you pick a photo. So this is the one I'm going to use.

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Don't worry, no one can see this being set for us.

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And so it took about a second, huh? See, there's a link that you can click on. But as we go through, this is on Twitter. Do you remember this photo at all?

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No, I didn't know that was taken. And I look very, very serious in that one. There's this. Yeah, here you're giving a talk and Wall Street Journal, the future of everything. Yeah. You're interviewing someone here at Google. Yeah. So like I said, all these things are publicly available. So that's basically how it works.

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There's nothing unusual here, just pictures of me at work, reporting stories and hosting panels in different cities, though it is kind of jarring to find photos of myself I've never seen. And once again, it brings up this thorny question of consent. You see, I'm unlikely to check a box giving permission to companies like Clearview to scrape my image and use it to build their businesses. The thing is, they don't need it. We'll be back in a moment right after this.

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At Deloitte, we believe the age of width is upon us, what's happening around us, shared data, digital assistants, cloud platforms, connected devices. It's not about people versus A.I. it's about the potential for people to collaborate with A.I. to discover and solve complex problems.

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But for organizations to enable innovation and break boundaries, they must harness this power responsibly. Deloitte Trustworthy, a framework, is a common language for leaders and organizations to articulate the responsible use of A.I. across internal and external stakeholders prepared to turn possibility into performance.

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To take a closer look at the Deloitte Trustworthy Framework, visit Deloitte Dotcom Slash U.S. Trust I. What's unique about the A.I. and that makes it a little harder for people to understand, is only searching publicly available information, and this is where tantalite makes an argument.

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We may be debating and litigating for many years to come that the open Internet as we know it, including things like Google searches, wouldn't really exist had we put restrictions on the use of online data.

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Linton's billion dollar company or trillion dollar company, Microsoft, they don't have the right to block other people's access to public data. So it's a thing that is just kind of in an interesting spot because it is only searching publicly available information happens to work very well and things that people want to be private. We do know that we don't want to be plastered all over the Internet. So I think that we have an instinctive level to keep private. What we want to keep public and that will always be the case.

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It's safe to say not everyone agrees with him. Twitter is among a whole host of companies that sent a clear view, a cease and desist order, telling it to stop scraping their images and delete all of their data. Twitter also says their policies prohibit their data from being used for facial recognition. Given he's already scraped billions of images, you'd be forgiven for wondering just how much more information is still out there for him to capture. But it's only the tip of the iceberg.

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We're still not even one percent of what's out there.

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When you run the numbers, it's kind of crazy how much information is out there. So when that comes to privacy, we kind of have to look at ourselves and say, well, we are voluntarily sharing a lot of this information. And that may be true, but how do we feel about it? What we don't have any private information, say, like Google or Facebook does. Google has your location on Android all the time. Facebook knows all your habits and what you like and what you don't like.

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The Instagram explorative is just like phenomenal at finding out what you like. It's kind of scary, but they kind of know a lot more information than what we do. And we're just focused on trying to apply for the greatest good, we think, to make the world a lot safer.

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And to him, that means working with police. So already in a lot of law enforcement agencies that use facial recognition, they have a procedure saying you cannot just arrest someone based on a facial recognition match. You still have to do a follow up research. So there's always a human in the loop that checks. Is this person the right person? Do they have the right name? Does that person live in the same area where the crime was committed?

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But what about folks who are falsely accused dtente? That would argue that's a human failing in the same way we're still responsible for how we drive while using GPS. When the navigation says turn right and it's not safe to do so, it's up to us and our human brains to ignore it. And he reminds us that people get it wrong, too.

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Like one example is Human Lineup's. The Innocence Project says 70 percent of wrongful convictions are from eyewitness testimony. So if you're a bad police officer and you want to frame someone for a crime they didn't do, you can kind of like edge people into picking the person you want out of a lineup. And I think technology like clear, you can add a lot of transparency and accountability. Something that we'd love to really take a strong look at is how can actually help people not be mis identified.

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In a way, this argument is making against human judgment. It's the same one that's used against handing these decisions off to AI. There's error, bias and racism. But where Tom Tat doesn't believe we can reliably change people, he does believe he's removed these things from his system.

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And so we believe we've totally solved the accuracy problem and the racial bias problem that have kind of plagued other facial recognition companies. And we want other people to know that we can really take this technology and use it.

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These are very big claims that may not even be possible, and there's currently no way to verify any of it. Clearview hasn't provided the kind of public access that would allow their system to be audited in the same way that Amazon and others have. He says the company did its own audit, assembling an independent review board that took a similar approach to when the ACLU tested Amazon's facial recognition system by running photos of the U.S. Congress passed a mug shot database, which are photos of people who've been arrested for a crime.

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And so they ran this independent study. But instead of searching a gallery of twenty five thousand mug shots, it searched a gallery of 2.8 billion photos at the time. And we did other state legislatures like New York State and Texas. And each of the results, they came up with the correct person and they went through them individually from clear views perspective.

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This means the technology might actually help the justice system become more fair. He says they got Jonathan Lippman, the chief judge of the New York Court of Appeals, to be part of that review panel.

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And he really believes that if you have something that's more accurate, it's better for the defendants as well. They're not going to go to jail for a crime he didn't commit.

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If Silicon Valley has a brand, it's this techno optimism about how they're. Relations will change the world, but without the burden of being responsible for any unwanted changes that might go along with that, perhaps it really shouldn't be the job of tech creators to worry about what kinds of transparency, oversight and guardrails are needed to protect the public.

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I think it's the responsibility of government and policy makers to come up with regulations, and tech companies should have a seat at the table and it's in their interest to have a seat at the table. Sometimes you see bad policies passed because they really don't know how the technology works. So I think more tech companies are going to engage with policy. We're a very small company. People forget that and we've had a lot of attention, but we know we're doing the right thing.

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And I think in the long run, any kind of new technology is controversial from the printing press. And that's just part of the process. And I think people will then say, hey, the choice is not between no facial recognition and facial recognition. It's between responsible facial recognition and kind of a wild west.

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One group aiming to help tame that Wild West is the Police Executive Research Forum. The nonprofit has spent the last four decades helping police chiefs work through emerging issues, the use of tasers, the use of body worn cameras. And now we're also looking at the topic of facial recognition.

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Alexa, Daniel Spaull is leading this research in partnership with the US Department of Justice.

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We've been doing a lot of this research in this area with the goal of developing some national guidelines. They're using it in a variety of different ways and they've sort of all develop their own procedures, protocols and policies.

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We'll get into that next episode. But for now, the important thing is her research suggests that clear views adoption by police departments may not be as widespread as claimed.

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In January, he told The New York Times more than 600 law enforcement agencies have started using his product in the past year, and we've only seen a handful that follow through with a formal contract.

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Bottom line, police agencies are using clear view, but there's a big difference between trying and buying.

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I know that some tried it out and then decided not to use it. And at least some of them we spoke with said it just didn't work that well for them. And I think it probably depends on what you're testing it with and just where you are in the country, because I don't know that anyone has a sense of where like how many images are coming up in different areas.

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And not all policing agencies that tried it out did so knowingly or through official channels.

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The executives found out that detectives had been approached by the company to test it out. They then brought it to their bosses to say, hey, we should look into moving forward with it. And the executives sort of said, we're going to shut that down for now and go through our normal procurement and evaluation processes before we move forward. It's been sort of a mixed reception, I would say, from different agencies. And the more important question would be to find out how many sort of permanent long term contracts and how many agencies have done the formal procurement to make a relationship with the company rather than just those free trials that were going around.

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Next time, we'll meet police around the U.S. where using face ID, it is a little bit Hollywoodish, and we did that on purpose in terms of how we wanted to feel. So imagine walking into a large room and on the front wall you have this massive display, all kinds of camera feeds like a surveillance room, but a little bit more high tech, a little bit bigger, a little bit more advanced.

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And find out what role actor Woody Harrelson and other celebrities unwittingly play in naming police suspects. This episode was reported and produced by me with Tate, Ryan Mosley and Anna Silicon's with special thanks to Karen Howe and Benji Rosen were edited by Michael Reilly and Gideon Lichfield, our technical director is Jacob Gorsky. Thanks for listening. I'm Jennifer Strong. This is Amitay Technology Review.