Who Gets To Read You in a Machine-Readable World?

What happens when your identity stops being something that you declare and starts becoming something that's inferred? Not by other people, but by machines. That's what we're here to investigate in The Machine-Readable Self.

As soon as something is captured in media, or as soon as you take a photograph or a video of anything, then it becomes machine legible, and then it can become part of a training canon, you know, to train a model that you may never even interact with.

Holly's point is deceptively simple. Once something's captured, a data point, it's read by machines, whether it's a photograph, a voice, a gesture, a fragment of media from anywhere really, it becomes the potential for training data. So identity then is no longer what we say about ourselves. It's actually what the system can extract from us. But all this kind of digital phenotyping, treating us almost like patients, capturing some of the atmospheric or environmental data around us, the way we move, the way— I was talking to somebody about, you know, motion prints in VR, for example, the way in which our gesture with our head and our hands, you know, it's so individual that it's like a fingerprint and they call it motion print.

I think it all depends on kind of where we end up in terms of who's using this data and for what purpose. I like to think of AI as, as us in aggregate, as a collective human accomplishment rather than a kind of like alien other that's here to just replace us.

So that's the first shift. The self becomes data, but once the self has become data, it's readable. And then what's readable becomes searchable. And that is really where the consequences become much more hard to contain.

When I first discovered Clearview AI, what it was doing was a secret. It was not known by the general public. People found it really shocking that their photos had been collected by this random company. And honestly, people were upset. What Clearview is doing is, you know, they built this database, they wanted to make money off of it. They would end up selling it to police departments, law enforcement officials, thousands of agencies. And it was tried by police departments around the world, including in the UK. There is an events venue here called Madison Square Garden. It's a really iconic venue for concert shows. And they started using facial recognition technology, not Clearview AI, a different company, a few years back to address security threats. But in the last year, the billionaire James Dolan, who owns that venue, decided to ban lawyers that worked at firms that have sued his company. I went with a lawyer. I watched it happen. She got pulled aside. They said, you're not allowed in here. You work at a firm that's banned. And she said, I'm not on that case. I'm not working on anything related to this. They said, it doesn't matter. You're banned until your firm drops its lawsuit against us. And so that is just, oh, you know, a very— striking way that this technology can be used to discriminate against people in new ways, even based on the work that you do. The other risk is just that you get slippage, that it starts out with, we don't want you coming in here because you're a threat, but then you realize there's other ways you can wield the superpower, Madison. Once you get this infrastructure in place, it might be used in a way that wasn't originally envisioned.

Kashmir Hill's point is absolutely profound because it shows us just how quickly a security technology can become an identity technology. What was just a face-to-face interaction became a recognition of a face, is now an access point. The face has become an access point, and permission is either given or permission is denied. It's to keep some people out, and to allow other people in. And what's very interesting, of course, is on what grounds.

To me, a digital identity is a set of facts about me, but a person is not the same as a set of facts in a folder. So there comes a moment in the life of every digital identity when the facts in that, when that folder has to be bound to a real-life physical human being, and that's our job. So our job is to bind that buff folder full of facts about you to the real, to a genuinely present human being who is the owner of that folder. I'm sometimes asked, you know, what's so special about the face? You know, why not the iris or the voice? And the answer is, well, frankly, the government, when the government creates credentials of my identity, they don't put my iris into it. The only thing That binds this passport that I'm invisibly waving at you at this moment, Tracey, that shows that it's mine, is my face on page 2. The face will continue to become— be the core root of trust in the digital economy, and assuring the genuine presence of that face will become fundamental to the stability of society.

Andrew gives us the other side of the problem, of course, which is in a world of bots, deepfakes, synthetic self, synthetic media, we do need to know that a real human being is present. And here is the paradox: in this interconnected world, we're actually, in the end, having to use the face as the root of trust. And perversely, the more we try to do this, the more we try to locate that a human being is present, the more we have to make ourselves machine-readable.

The first era is the traditional data broker, and these are companies who are kind of sort of household names if you pay attention to tech and to advertising. They're companies like Thomson Reuters, Axiom, credit bureaus like TransUnion eventually got into this kind of market. And what they have historically done is gone out and taken either credit data from banks, commercial data from things like magazine subscriptions, public data from things like courthouses, marriage records, hunting licenses, driver's licenses, and created essentially lists of where people live in the world, what assets they own, and some basic demographic information, right? The second generation is social data brokers. So when social media became a big part of the way we communicate and the way we express ourselves in the early 2000s, there started to be these data brokers that began monitoring the social conversation. Governments also increasingly became interested in acquiring social media data and analyzing it and looking at people's connections with each other and who they talk to and what they say. Then there's location brokers. So these are brokers that started collecting information off of mobile phones. And then the weirdest kind of data I write about is what I call in the book gray data. You called it esoteric data, but you know, it is data that we do not normally associate with We don't even really think about it. It's this kind of weird digital exhaust. So I'm talking to you through a pair of Bluetooth headphones. Well, Bluetooth headphones are constantly pinging out a signal, and if you know how to collect that signal, you can say Byron's Bluetooth headphones were at this particular address. There's just all manner of datasets that you really need to be a technologist to understand, and governments are moving to collect that kind of data as well.

This is the work that I've been doing for the last few years, which is to really pinpoint the way in which we are turning ourselves into databases, and that's the work of the Database Self and the Database Brand, but even more broadly, just how we are becoming a set of signals, data, interpretations, digital relationships, and really the question now is not really be that big profound one we used to ask, "Who are you?" It's actually more, "Who or what now interprets you?" Thanks for watching, and please do subscribe and join us next time for another archive essay as we trawl through the fantastic conversations we've had over the years here on The Future of You.