[xiii1408]

I think this is a valid point and a really interesting question. If that's the standard, we need to regulate all recommendation algorithms. (i.e. put limits on Twitter, Instagram, and YouTube as well.)

How could we regulate this? I can think of two ways:

- Results-based enforcement. i.e., companies are free to use whatever recommendation model they like, but have to recommend content within ideological bounds. i.e., you can't bias toward one partisanship more than X%. There's some precedent for this with the equal-time rule (https://en.wikipedia.org/wiki/Equal-time_rule) and FCC fairness doctrine (https://en.wikipedia.org/wiki/Fairness_doctrine).

- Algorithm-based enforcement. i.e., there are limits on the algorithm itself. Perhaps you have to present your algorithm to a government agency and provide a proof that it obeys certain properties. But the enforcement here is analytical rather than empirical.

[threeseed]

> Twitter, Instagram, and YouTube

Can you provide research for each of these.

Otherwise it's just muddying the waters to act like the bias is inherent to all platforms.

[xiii1408]

People do these same sock puppet studies on Twitter/YouTube/etc. and find biases there as well. There's a lot of literature out there.

Here's a recent study on YouTube from the same author as this TikTok study finding left-leaning bias in US recommendations: https://academic.oup.com/pnasnexus/article/2/8/pgad264/72424....

Here's a somewhat older study from Twitter itself where they determined that their recommendations were biased toward right-leaning accounts: https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter....

IMO the interesting question is not whether an individual platform is biased and what its biases are, but rather how we might regulate recommendations given that there is always a risk of bias.