Because the current recommender/ML systems model the past as identical to the future as a matter of structural equivalence (There is a profound assumption of l

Because the current recommender/ML systems model the past as identical to the future as a matter of structural equivalence (There is a profound assumption of linearity between past and future e.g. in time, and linearity in space). They must assume the world is a stable & stationary distribution. Which doesn't leave a lot of room for modeling change. This actually makes recommender systems things that train humans, that render them more predictable, by grouping them into categories base on 'latent variables'. It makes everyone more boring by reducing diversity instead of encouraging it. You can easily see this in your youtube recommendations. Jaron Larnier calls these kinds of algorithms manipulative - they train people to optimize engagement metrics, rather than sometime qualitative like quality. https://livestream.com/internetsociety2/cybercon/videos/166218044 https://livestream.com/internetsociety2/cybercon/videos/1662...