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."
I previously served as CTO of the FCC Enforcement Bureau. A couple thoughts on the regulatory dimensions of this report.
* This could be a Federal Trade Commission problem. T-Mobile, like all major ISPs, has made public representations about upholding net neutrality principles [1]. These voluntary commitments were part of the Trump-era FCC's rationale for repealing net neutrality rules. Breaching the commitments could constitute a deceptive business practice under Section 5 of the Federal Trade Commission Act.
* This could also be a Federal Communications Commission problem. When repealing the Obama-era net neutrality rules, the Trump-era FCC left in place a set of transparency requirements [2]. Making an inaccurate statement about network management practices can be actionable under that remaining component of the FCC's net neutrality rules.
I haven't seen a comment from T-Mobile, so to be clear, that's just based on the report.
Hi, I previously served as CTO of the FCC's Enforcement Bureau, where I worked on then-Chairman Wheeler's Robocall Strike Force. I'd like to offer a few observations that might be of interest.
* T-Mobile, like the other carriers, is offering a numerator and not a denominator. These call filtering services are plainly valuable, but it's difficult to evaluate how effective they are based on current public evidence.
* It isn't a coincidence that the top robocall destinations include locations that are popular for retirement. These scams disproportionately target and take advantage of older customers.
* Call authentication (STIR/SHAKEN) is helping, and will continue to become more effective. The FCC did not push carriers to rapidly adopt call authentication during the last administration; Congress eventually stepped in with the TRACED Act, and the FCC has since made STIR/SHAKEN a top priority.
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