Hacker News Reader

Comment by akomtu | Hacker News Reader

It's a VP level thing. Some lowly offshore "content watcher" flags suspicious posts, the important flags bubble up in a email chain to a VP, who makes the decision. That's for high visibility dissenters. This email chain is discoverable if anyone bothers to sue and convince the judge.

View on HN

In the leaked Elon texts, there were mentions of a suspected cabal that would keep these kind of decisions from the board. So I'm not so sure that leaders trust their own censorship orgs to do the right thing.

Or ML.

If a user/keyword/whatever gets negative karma because previously-blocked nazis fawn over it, newly posted Giorgia Meloni videos will carry a heavy burden, even if that particular video isn't anything to complain about. The ML will guess that the nazis are going to like it, and load the new videos with negative karma.

Oh, please. I've seen this in action. If ML is used, it's only to flag suspicious posts: bans are handed by corp managers.

Yes, but it's a shadow ban.

Thinking aloud, I suppose there are three kinds of bans:

1 - Actual, explicit bans (formal)

2 - Intentional shadowbans (decided by humans)

3 - De-facto shadowbans (unintentional artifacts of ML vertex weighting)

Note that the possibility of 3 occurs even if human intervention is needed to act upon the posts flagged by the ML --- the ML is, in effect, driving the 'attentional map' of the content moderator.

So this specific incident would probably be a case of #3, but it makes people angrier if you suggest it's #2, so that's what people are doing.

Reply on news.ycombinator.com