Bogdan Kulynych, a graduate of the National University of Kyiv-Mohyla Academy, researcher at Switzerland's EPFL technical university, won a $3,500 prize in Twitter-sponsored contest to find problems in the "saliency" algorithm it uses to crop the photos it shows on timeline.
Kulynych determined that a key Twitter algorithm favors faces that look slim and young and with skin that is lighter-colored or with warmer tones, CNET informs.
"The target model is biased towards deeming more salient the depictions of people that appear slim, young, of light or warm skin color and smooth skin texture, and with stereotypically feminine facial traits. This bias could result in exclusion of minoritized populations and perpetuation of stereotypical beauty standards in thousands of images," Kulynych wrote.
Twitter praised the contest entry as important in a world where many of us use camera and editing apps that apply beauty filters before we share photos with friends or on social media. That can distort our expectations of attractiveness.
Earlier this year, Twitter itself confirmed its AI system showed bias when its cropping algorithm favored images of white people over Black people. But Kulynych found other problems in how the algorithm cropped photos to emphasize what it deemed most important.