Student proves Twitter algorithm ‘bias’ toward lighter, slimmer, younger faces

Twitter’s image cropping algorithm prefers younger, slimmer faces with lighter skin, an investigation into algorithmic bias at the company has found.

The finding, while embarrassing for the company, which had previously apologised to users after reports of bias, marks the successful conclusion of Twitter’s first ever “algorithmic bug bounty”.

The company has paid $3,500 to Bogdan Kulynych, a graduate student at Switzerland’s EFPL university, who demonstrated the bias in the algorithm, which is used to focus image previews on the most interesting parts of pictures, as part of a competition at the DEF CON security conference in Las Vegas.

Kulynych proved the bias by first artificially generating faces with varying features, and then running them through Twitter’s cropping algorithm to see which the software focused on.

Since the faces were themselves artificial, it was possible to generate faces that were almost identical, but at different points on spectrums of skin tone, width, gender presentation or age – and so demonstrate that the algorithm focused on younger, slimmer and lighter faces over those that were older, wider or darker.

“When we think about biases in our models, it’s not just about the academic or the experimental … but how that also works with the way we think in society,” said Rumman Chowdhury, the head of Twitter’s AI ethics team told the conference.

“I use the phrase ‘life imitating art imitating life’. 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 had come under fire in 2020 for its image cropping algorithm, after users noticed that it seemed to regularly focus on white faces over those of black people – and even on white dogs over black ones. The company initially apologised, saying: “Our team did test for bias before shipping the model and did not find evidence of racial or gender bias in our testing. But it’s clear from these examples that we’ve got more analysis to do. We’ll continue to share what we learn, what actions we take, and will open source our analysis so others can review and replicate.” In a later study, however, Twitter’s own researchers found only a very mild bias in favour of white faces, and of women’s faces.

The dispute prompted the company to launch the algorithmic harms bug bounty, which saw it promise thousands of dollars in prizes for researchers who could demonstrate harmful outcomes of the company’s image cropping algorithm.

Kulynych, the winner of the prize, said he had mixed feelings about the competition. “Algorithmic harms are not only ‘bugs’. Crucially, a lot of harmful tech is harmful not because of accidents, unintended mistakes, but rather by design. This comes from maximisation of engagement and, in general, profit externalising the costs to others. As an example, amplifying gentrification, driving down wages, spreading clickbait and misinformation are not necessarily due to ‘biased’ algorithms.”

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