I can't stand the fact that organizations hide behind "algorithms" as an excuse for every unintended side-effect.
It's no surprise that algorithms create an information bubble for us. I like to use music as an example: the best music recommendations I've ever received have been from friends. I'll describe music I'm listening to and like, and they'll recommend something for me. I'd say 50% of the time I find it unobjectionable, 30% of the time I hate it, and 20% of the time I love it.
Sites have been trying to replicate this functionality using algorithms for a well over a decade. Last.fm used to be a great way for me find music. As far as I could tell, it would just pick music from people who listened to the same artists as you. I'd say I found 30% of the music unobjectionable, 50% of the music terrible, and 20% of the music great. Functionally, this was remarkably similar to my friends recommending me music, because I only care about the music I loved.
Over time their algorithm got 'better' and nearly stopped recommending music I disliked, however I found that it mostly just recommended me music that was in the unobjectionable middle ground of 'meh'. I stopped looking for music on Last.fm because it stopped giving me music I loved.
We've positioned machine learning as a replacement to curation, but what it really does is put forth the least objectionable content. Machine learning lacks the ability to deal with the nuances surrounding controversial or challenging content. It never takes the leap of faith that a person would when recommending you something.
I am really interested to see how this all plays out over the next few years, because I think people are beginning to realize they are addicted to the perpetual stream of 'good enough' content. The network effect will probably protect Facebook for the time being, I assume the first sites affected will be content-driven, like YouTube.
Of course, XKCD addressed the issues I have with algorithms far better than I could ever hope to:
https://xkcd.com/1831/