Tool helps clear biases from computer vision

Although the sources of bias in AI systems are varied, one major cause is stereotypical images contained in large sets of images collected from online sources that engineers use to develop computer vision, a branch of AI that allows computers to recognize people, objects and actions. Because the foundation of computer vision is built on these data sets, images that reflect societal stereotypes and biases can unintentionally influence computer vision models. To help stem this problem at its source, researchers in the Princeton Visual AI Lab have developed an open-source tool that automatically uncovers potential biases in visual data sets. The tool allows data set creators and users to correct issues of underrepresentation or stereotypical portrayals before image collections are used to train computer vision models. In related work, members of the Visual AI Lab published a comparison of existing methods for preventing biases in computer vision models themselves, and proposed a new, more effective approach to bias mitigation.