It's interesting; a line through the past years of my work is that it slowly went from "here's a fun analysis to help you foward!" to "I'm handcrafting the model that is gonna determine what you need to do". I'm staying far away from anything that even remotely resembles ML and have developed the skill to properly communicate what my results do and don't say, so it doesn't come close to any case she's describing.
The problem, however, is that for large and complex challenges of our time there generally aren't ways of dealing with it that avoid any biases and prejudices by those who create them. Now "college rankings" isn't among those challenges, but something like climate change totally is.
I wonder if the Facebook kerfuffle is maybe going to make this stuff more prominent, more front'n'center.
From my vantage point, there's been an increase in the number who realize that a) we can't trust these models on their face value, but b) we do still need large and automated solutions, so c) we need to be much more careful of how we use them, d) without throwing the baby out with the bathwater. There's still an awful long way to go, though.