Context: I teach at Princeton and study social media and recommendation systems.
From a very quick skim of the repositories, this appears to be quite limited transparency. The documentation gives a decent high-level overview of how Tweet recommendation works—no surprises—and the code tracks that roadmap. Those are meaningful positive steps. But the underlying policies and models are almost entirely missing (there are a couple valuable components in [1]). Without those, we can't evaluate the behavior and possible effects of "the algorithm."
From a very quick skim of the repositories, this appears to be quite limited transparency. The documentation gives a decent high-level overview of how Tweet recommendation works—no surprises—and the code tracks that roadmap. Those are meaningful positive steps. But the underlying policies and models are almost entirely missing (there are a couple valuable components in [1]). Without those, we can't evaluate the behavior and possible effects of "the algorithm."
[1] https://github.com/twitter/the-algorithm-ml