In a hearing of the US Senate Commerce Committee’s Subcommittee on Communications, Technology, Innovation and the Internet, Stephen Wolfram suggests that instead of breaking up the platforms we need to open up the possibility for third-party algorithms for people to choose between. This is all a part of what Wolfram explains as a movement towards an AI constitution. This post is useful in that it not only unpacks what is involved in creating such an algorithm, but it also unpacks a range of computational terms, such as data deducibility, computational irreducibility, non-explainability and ethical incompleteness.
Why does every aspect of automated content selection have to be done by a single business? Why not open up the pipeline, and create a market in which users can make choices for themselves?
Social networks get their usefulness by being monolithic: by having “everyone” connected into them. But the point is that the network can prosper as a monolithic thing, but there doesn’t need to be just one monolithic AI that selects content for all the users on the network. Instead, there can be a whole market of AIs, that users can freely pick between
I don’t think it’s realistic that everyone will be able to set up everything in detail for themselves. So instead, I think the better idea is to have discrete third-party providers, who set things up in a way that appeals to some particular group of users.
I wish we were ready to really start creating an AI Constitution. But we’re not (and it doesn’t help that we don’t have an AI analog of the few thousand years of human political history that were available as a guide when the US Constitution was drafted). Still, issue by issue I suspect we’ll move closer to the point where having a coherent AI Constitution becomes a necessity
there’s a “final ranking” problem. Given features of videos, and features of people, which videos should be ranked “best” for which people? Often in practice, there’s an initial coarse ranking. But then, as soon as we have a specific definition of “best”—or enough examples of what we mean by “best”—we can use machine learning to learn a program that will look at the features of videos and people, and will effectively see how to use them to optimize the final ranking.
As a variant of the idea of blocking all personal information, one can imagine blocking just some information—or, say, allowing a third party to broker what information is provided. But if one wants to get the advantages of modern content selection methods, one’s going to have to leave a significant amount of information—and then there’s no point in blocking anything, because it’ll almost certainly be reproducible through the phenomenon of data deducibility.
One feature of my suggestions is that they allow fragmentation of users into groups with different preferences. At present, all users of a particular ACS business have content that is basically selected in the same way. With my suggestions, users of different persuasions could potentially receive completely different content, selected in different ways.