An analysis of Instagram found that its vision model categorized more than 500 different 'concepts,' including age and gender, time of day, background images and even what foods people were eating in the photographs.
Digital privacy and security engineers at the University of Wisconsin–Madison have found that the artificial intelligence-based systems that TikTok and Instagram use to extract personal and demographic data from user images can misclassify aspects of the images. This could lead to mistakes in age verification systems or introduce other errors and biases into platforms that use these types of systems for digital services.
Led by Kassem Fawaz, an associate professor of electrical and computer engineering at UW–Madison, the researchers studied the two platforms’ mobile apps to understand what types of information their machine learning vision models collect about users from their photographs — and importantly, whether the models accurately recognize demographic differences and age.
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