FaceApp's race-changing filters under fire for promoting blackface

Yolanda Curtis
August 10, 2017

Furthermore, it would appear that app's developers weren't really sure themselves just which ethnicities they were pinpointing under "Asian"- the "Indian" filter got its own label, despite India being a part of Asia. "In addition to that, the list of those filters is shuffled for every photo, so each user sees them in a different order", he added.

"The ethnicity change filters have been created to be equal in all aspects", Goncharov said.

A social media app is under fire for an update enabled users to put on digital blackface. "They are even represented by the same icon", Goncharov wrote.


FaceApp sent a push alert to its users about the new filters on Wednesday in order to promote them.

"Im glad faceapp, that fun app we all used for 24 hours, just invented black face as a cool retro comeback attempt", Tweeted one user.

Yes, in its latest iteration the FaceApp tool now allows users to "look" black, white, Asian, or Indian. Lucy Yang/INSIDER FaceApp's "Black" filter.


FaceApp, which has already been massively criticized for having a "hot mode" filter that whitened people's skin, is making headlines again for offering filters that change users' skin tone, among other characteristics. The "Indian" filter was practically the same as the original selfie. (Ed. note: I initially included the results in this post, along with the results from some of my colleagues, but they were so bad and discomfiting that I removed them and am instead just describing the horror for you.) The "Caucasian" filter gave me an unnatural pinkish hue, and added wrinkles under my eyes and along my smile lines.

The Russian FaceApp creators have refused to pay attention to the accusations, saying that there's nothing racist with the feature's new filters. (There was also the 4/20 filter that gave users a "Bob Marley" mask for their selfes.) The company's CEO, Yaroslav Goncharov, quickly apologized for the earlier problems, and promised to fix the behavior. The app didn't use a diverse enough data set while training the filter to define "hotness", which essentially meant that the filter tried to make everyone look whiter to make them look more attractive.


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