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A team of computer scientists at Princeton University’s Visual AI Lab has developed a method to detect biases in sets of images and visual patterns. The method relies on an open-source tool to flag potential and clearly existing biases in images that are used to train AI systems, such as those that enable automated credit services and courtroom sentencing programs.
The tool specifically allows data set creators and users to correct issues of visual underrepresentation or stereotypical portrayals before image collections are used to train computer vision models.
Engineers use large sets of images, or compilations of images, that are collected from online sources to develop computer vision, which allows computers to…READ MORE