AI and problems of scale
There’s a story in one of Georges Simeon’s 1930s detective stories that I think about sometimes when talking about a certain kind of AI problem. Simenon’s hero, Inspector Maigret of the judicial police in Paris, scares a witness, and then goes across the street to a café and calls the telephone exchange. He tells them that someone will place a call from the ‘Pelican’ nightclub to Cannes: they are to hold the call until he gets there. Then he takes a taxi to the exchange, where they are indeed holding the call, and listens in.
I told this story to someone at a Three Letter Agency a few years ago, and got a wry smile - they can’t really do that now, but there are other things that they could do, and they could do them at the scale not of one phone call but of millions. That seems different. We accept the police listening to phone calls one at a time, with a warrant, but not listening to all of them, all of the time.
Something similar comes up when we talk about AI and face recognition by law enforcement today. We’re all (I think) comfortable with the idea of ‘Wanted’ posters. We understand that the police put them up in their offices, and maybe have some on the dashboard of their patrol car. In parallel, we have a pretty wide deployment today of licence plate recognition cameras for law enforcement (or just tolls), and no-one has really noticed. In parallel, public and private surveillance cameras have become a basic investigative tool, with a little more concern. But what if every police patrol car had a bank of cameras that scan not just every number plate but every face within a hundred yards against a national database of outstanding warrants? What if the cameras in the subway do that? All the connected cameras in the city? China is already trying to do this, and we seem to be pretty sure we don’t like that, but why? One could argue that there’s no difference in principle, only in scale, but a change in scale can itself be a change in principle.
