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> Also, even if there _is_ a bias, it doesn't tell us why Because that is a political discussion that will inevitably derail the point. If there is identified

> Also, even if there _is_ a bias, it doesn't tell us why Because that is a political discussion that will inevitably derail the point. If there is identified bias then the platform must address it. Or it should be labelled a national security threat.

normalaccess 2y ago
The problem is that the algorithms are programed to show people what they want to see OR what the platform want's you to see. If it's orgasmic then this is no different than any other form of organic popularity. Seeing as Trump won the popular vote and the electoral collage, people were interested in republican content. On the same token it's very easy to AstroTurf and claim it was organic. From my personal conversations in meat-space I lean organic. Is there funny business going on? Absolutely, all the time everywhere in every way. Can we say this was funny business? Not without the code. TLDR: Popularity algorithms push popular content algorithmically.
- threeseed 2y ago
  > TLDR: Popularity algorithms push popular content algorithmically Not aware of any major platform that solely uses a popularity algorithm.
xiii1408 2y ago
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 https://en.wikipedia.org/wiki/Equal-time_rule) and FCC fairness doctrine (https://en.wikipedia.org/wiki/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 2y ago
  > 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 2y ago
    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/pgad264/7242446 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/official/en_us/company/2021/rml/Algorithmic-Amplification-of-Politics-on-Twitter.pdf 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.

normalaccess 2y ago

The problem is that the algorithms are programed to show people what they want to see OR what the platform want's you to see. If it's orgasmic then this is no different than any other form of organic popularity. Seeing as Trump won the popular vote and the electoral collage, people were interested in republican content. On the same token it's very easy to AstroTurf and claim it was organic. From my personal conversations in meat-space I lean organic. Is there funny business going on? Absolutely, all the time everywhere in every way. Can we say this was funny business? Not without the code. TLDR: Popularity algorithms push popular content algorithmically.

threeseed 2y ago
> TLDR: Popularity algorithms push popular content algorithmically Not aware of any major platform that solely uses a popularity algorithm.

xiii1408 2y ago

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 https://en.wikipedia.org/wiki/Equal-time_rule) and FCC fairness doctrine (https://en.wikipedia.org/wiki/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 2y ago
> 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 2y ago
  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/pgad264/7242446 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/official/en_us/company/2021/rml/Algorithmic-Amplification-of-Politics-on-Twitter.pdf 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.