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ViBE: A Tool for Measuring and Mitigating Bias in Image Datasets

Machine learning models are known to perpetuate the biases present in the data, but oftentimes these biases aren't known until after the models are deployed. We present the Visual Bias Extraction (ViBE) Tool that assists in the investigation of a visual dataset, surfacing potential dataset biases along three dimensions: (1) object-based, (2) gender-based, and (3) geography-based. Object-based biases relate to things like size, context, or diversity of object representation in the dataset; gender-based metrics aim to reveal the stereotypical portrayal of people of different genders within the dataset, with future iterations of our tool extending the analysis to additional axes of identity; geography-based analysis considers the representation of different geographic locations. Our tool is designed to shed light on the dataset along these three axes, allowing both dataset creators and users to gain a better understanding of what exactly is portrayed in their dataset. The responsibility then lies with the tool user to determine which of the revealed biases may be problematic, taking into account the cultural and historical context, as this is difficult to determine automatically. Nevertheless, the tool also provides actionable insights that may be helpful for mitigating the revealed concerns. Overall, our work allows for the machine learning bias problem to be addressed early in the pipeline at the dataset stage. ViBE is available at https://github.com/princetonvisualai/vibe-tool.