Feb 28, 2020

Alumnus Marc Faddoul Discovers Racial Biases in TikTok’s Algorithm

From Wired

Why is TikTok creating filter bubbles based on your race?

By Maria Mellor

On TikTok, all is not as it seems. Engage with dance videos and you’ll start seeing more people doing the Renegade. If you linger on a TikTok dog, it will give you puppies galore.

But TikTok’s algorithmic obsession with giving you more content that it thinks you will like is having an unintended consequence: it’s started recommending people new accounts to follow based on the physical appearance of the people they already follow.

This week Marc Faddoul, an AI researcher at UC Berkeley School of Information, found that TikTok was recommending him accounts with profile pictures that matched the same race, age or facial characteristics as the ones he already followed.

He created a fresh account to test his theory and followed people he found on his ‘For You’ page. Following the account of a black woman led to recommendations for three more black women. It gets weirdly specific — Faddoul found that hitting follow on an Asian man with dyed hair gave him more Asian men with dyed hair, and the same thing happened for men with visible disabilities.

TikTok denies that it uses profile pictures as part of its algorithm, and says it hasn’t been able to replicate the same results in its own tests. But the app uses collaborative filtering — where recommendations are made based on what other users have done...


Marc Faddoul graduated from the I School's Master of Information Management and Systems (MIMS) program in 2019. He currently works as an associate researcher with Professor Hany Farid.

Last updated:

June 12, 2020