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.”
Twitter’s “algorithmic bug bounty”
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.
Why outdated patching methods leave companies vulnerable and how AI can fix it
Automation is crucial in reducing cybersecurity vulnerabilities, says Vivek Bhandari, VP of Product Marketing at Tanium. Poor patching methods create a backlog of security issues, or “vulnerability debt,” which can leave organisations exposed.
Bhandari urges companies to modernise their processes and use AI and automation to quickly identify and fix vulnerabilities. This proactive approach can significantly reduce risk and keep systems secure. #tickernow
A recent survey reveals contrasting views on healthcare technology between U.S. consumers and healthcare professionals.
While consumers are optimistic about tech improving care, professionals remain cautious due to concerns about workflow disruption and patient interaction, according to Randy Boldyga, Founder & CEO of RXNT.
Boldyga emphasised the need for better communication to bridge the gap, with patients requiring more education on tech benefits and professionals seeking streamlined tools.
RXNT is focused on creating solutions that enhance both provider workflows and the patient experience in this evolving healthcare landscape.