FaceApp 'Racist' Filter Shows Users As Black, Asian, Caucasian And Indian

An array of ethnic filters on the photo-editing app, FaceApp, has stirred backlash as users decry the options for facial manipulation as racist.

The selfie-editing app, FaceApp, was updated earlier this month with four new filters: Asian, Black, Caucasian and Indian. The filters immediately drew criticism on Twitter by users who made comparisons to blackface and yellowface racial stereotypes. In addition to these blatantly racial face filters – which change everything from hair color to skin tone to eye color – other FaceApp users noted earlier this year that the “hot” filter consistently lightens people’s skin color.

"#FaceApp has a new feature where you can see yourself #CaucasianLiving. Look how privileged I look!" one of the app's users commented on Twitter.

"y tho? Can FaceApp troll any harder?" wrote another user, posting a photo of himself with the Asian, Black, Caucasian and Indian filters.

FaceApp CEO Yaroslav Goncharov defended the Asian, Black, Caucasian and Indian filters in an email to The Verge: “The ethnicity change filters have been designed to be equal in all aspects,” he told The Verge over email. “They don’t have any positive or negative connotations associated with them. They are even represented by the same icon. In addition to that, the list of those filters is shuffled for every photo, so each user sees them in a different order.”

FaceApp’s Russian creators describe the app as a tool to “Transform your face using Artificial Intelligence in just one tap! Add a beautiful smile. Get younger or older. Add magic to your selfie.”

The selfie-altering app also has the ability to change a user’s gender, age, facial hair and weight altogether.

And although FaceApp’s “digital blackface” filter is receiving backlash now, it isn’t the company or the industry’s first run-in with racial stereotyping. In 2016, Snapchat released a Bob Marley selfie mask filter to mark the 4/20 April holiday. The filter was pulled after controversy swirled around the ability to “dress up” as another ethnicity.

Goncharov explained the “hot” filter backlash as an “unfortunate side-effect of the underlying neural network caused by the training set bias.” In other words, the software used to change users’ appearance had only been given photos of white people and therefore tied “hotness” to a paler complexion.

“We are deeply sorry for this unquestionably serious issue,” Goncharov told The Guardian in April. “It is an unfortunate side-effect of the underlying neural network caused by the training set bias, not intended behavior.”