Thanks very much, I really liked reading this essay. I concur with your arguments about why more optionality and privacy don’t solve the problem. I also came up with the idea of more competition. That sounds like the sort of solution the market is good at, but I can’t think of a schema for getting around the network effects that you talk about.
(Actually, I just thought of one. I think that if the recommender systems were open so that anyone could write an algorithm, that could lead to pretty good competition. I’d be excited to see alt FB or Twitter algorithms. That said it sounds like more optionality I.e. most people wouldn’t use it. So not sure.)
If there was a feasible way to make the algorithm open, I think that would be good (of course FB would probably strongly oppose this). As you say, people wouldn’t directly design / early adopt new algorithms, but once early adopters found an alternative algorithm that they really liked, word of mouth would lead many more people to adopt it. So I think you could eventually get widespread change this way.
You could have a meta-recommender system that aggregates recommendations from multiple algorithms, and shows which algorithm each recommendation came from. By default, when the user reinforces a recommendation’s algorithm, the meta-recommender system’s algorithm would also be shifted towards the reinforced approach.
Thanks very much, I really liked reading this essay. I concur with your arguments about why more optionality and privacy don’t solve the problem. I also came up with the idea of more competition. That sounds like the sort of solution the market is good at, but I can’t think of a schema for getting around the network effects that you talk about.
(Actually, I just thought of one. I think that if the recommender systems were open so that anyone could write an algorithm, that could lead to pretty good competition. I’d be excited to see alt FB or Twitter algorithms. That said it sounds like more optionality I.e. most people wouldn’t use it. So not sure.)
The auditing is an idea I hadn’t heard before. It reminds me of what Zvi Mowshowitz did for me when he “audited” the FB algorithm ( https://www.google.com/amp/s/thezvi.wordpress.com/2017/04/22/against-facebook/amp/ ). That was very helpful. I’d love to see more work like that.
If there was a feasible way to make the algorithm open, I think that would be good (of course FB would probably strongly oppose this). As you say, people wouldn’t directly design / early adopt new algorithms, but once early adopters found an alternative algorithm that they really liked, word of mouth would lead many more people to adopt it. So I think you could eventually get widespread change this way.
You could have a meta-recommender system that aggregates recommendations from multiple algorithms, and shows which algorithm each recommendation came from. By default, when the user reinforces a recommendation’s algorithm, the meta-recommender system’s algorithm would also be shifted towards the reinforced approach.