Perfect example of how not proactively making an algorithm anti-racist can yield results that end up being racist. If you train a machine learning algorithm on photos with predominantly white faces, your algorithm is going to think people with lighter skin are more important thank people with darker skin.
> HN doesn't like talking about race or numerous other "controversial" topics.
There was plenty of discussions on HN when GitHub was renaming master/slave terminology [0] and when other open source projects were doing the same. Essentially they break the HN guidelines when they 'like' if it is a company / person they 'like'.
On the other hand, when a topic attacks their 'narrative' or attacks their side of the argument from someone they don't like, they downvote, flag and censor and make sure it is never seen. Even if it is factual and has evidence.
In case anyone else did not realize this and is as confused as I was, apparently you can attach more than one photo to a tweet, and Twitter will then show previews of all of them in a gallery. The previews are formed by taking some part of the original image. Clicking on any of the previews opens a view of the corresponding full image.
I knew about twitter taking a part of an image and using that for a preview, but had not realized that there could be more than one image in a tweet.
Believe me. I wish it was satire. But the irony is that it seems the so-called 'diversity' and 'anti-racists' virtue signallers are now becoming the actual racists. Especially on Twitter.
Consistent in the title is misleading because this is essentially n=1. The Twitter user did try to transform the image in order to produce a different result but we don’t know if there is something not race based in Mitch’s face that the algorithm prefer’s over Obama’s. We also don’t know if the user has cherry picked this example.
