A student researcher has found that Twitter’s image-cropping algorithm prefers faces that are slim, young and light-skinned
A graduate student at Switzerland’s EFPL university has discovered a bias in Twitter’s image-cropping ‘saliency’ algorithm.
Bogdan Kulynyc proved that the algorithm preferred faces that are light-skinned, slim and young. Twitter’s saliency algorithm decides the most interesting part of an image to crop for preview.
The researcher tested how the software responded to AI-generated faces
Kulynyc found that by he could manipulate the algorithm to be prefer faces by “making the person’s skin lighter or warmer and smoother; and quite often changing the appearance to that of a younger, more slim, and more stereotypically feminine person”.
He achieved this by using an AI face generator to create artificial people with varying features. He was then able to run the images through the algorithm to see which faces the software preferred.
“We should not forget that algorithmic bias is only a part of a bigger picture. Addressing bias in general and in competitions like this should not end the conversation about the tech being harmful in other ways, or by design, or by fact of existing,” said Kulynyc.
“A lot of harmful tech is harmful not because of accidents, unintended mistakes, but rather by design”
Bogdan Kulynyc
“This shows how algorithmic models amplify real-world biases and societal expectations of beauty”
Twitter’s director of software engineering and head of AI Ethics Rumman Chowdhury says the findings “showcased how applying beauty filters could game the algorithm’s internal scoring model.
“We create these filters because we think that’s what ‘beautiful’ is, and that ends up training our models and driving these unrealistic notions of what it means to be attractive.”
We should not forget that "algorithmic bias" is only a part of a bigger picture. Addressing bias in general and in competitions like this should not end the conversation about the tech being harmful in other ways, or by design, or by fact of existing.
The findings mark the conclusion of Twitter’s first “algorithmic bug bounty”. The event was part of an in-house competition at the DEF CON security conference in LA.
Twitter rewarded the student $3500 for his efforts.
Last year, Twitter came under fire for cropping out Black faces
This comes after and incident last year, where the tech giant found that the preview crop was more likely to hide Black faces.
Twitter’s director of software engineering Rumman Chowdhury said the findings illustrated that “how to crop an image is a decision best made by people”.
Natasha is an Associate Producer at ticker NEWS with a Bachelor of arts from Monash University. She has previously worked at Sky News Australia and Monash University as an Online Content Producer.
The recent backlash against Musk’s proposed tweet cap could prove to be the perfect time to launch the competitor
Meta Platforms, the company behind Facebook, plans to launch a microblogging app called Threads on Thursday, to operate as a direct competitor to Twitter. The launch comes just days after Twitter boss Elon Musk attracted criticism by announcing limits to how many posts users could read on the platform.
Threads will allow users to directly port their followers from Instagram, another app owned by the company, and to keep the same username. This feature will allow users to supercharge the often slow process of building a following on the new site.
While other apps such as Mastodon and Blue Sky have failed to present much of a challenge to Twitter, Threads is launching after a period of intense criticism and scepticism of Twitter, which followed Musk’s US$44 billion purchase of the platform in 2022.
Musk has made significant staffing cuts in areas like content moderation which has caused a rift with advertisers. He has also implemented several elements such as an US$8 per month account verification system which has proved unpopular with users.
Instagram boasts roughly 2.3 billion users compared to Twitter’s almost 400 million, which poses a significant user advantage from which to draw from.