REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
This work addresses bias issues in visual datasets for machine learning practitioners, though it is incremental as it builds on existing bias detection methods by providing a tool for analysis and mitigation.
The paper tackles the problem of biases in visual datasets by introducing REVISE, a tool that preemptively analyzes datasets for object-based, person-based, and geography-based biases, and suggests mitigation steps to address machine learning bias early in the pipeline.
Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become apparent until after the models are deployed. Our work tackles this issue and enables the preemptive analysis of large-scale datasets. REVISE (REvealing VIsual biaSEs) is a tool that assists in the investigation of a visual dataset, surfacing potential biases along three dimensions: (1) object-based, (2) person-based, and (3) geography-based. Object-based biases relate to the size, context, or diversity of the depicted objects. Person-based metrics focus on analyzing the portrayal of people within the dataset. Geography-based analyses consider the representation of different geographic locations. These three dimensions are deeply intertwined in how they interact to bias a dataset, and REVISE sheds light on this; the responsibility then lies with the user to consider the cultural and historical context, and to determine which of the revealed biases may be problematic. The tool further assists the user by suggesting actionable steps that may be taken to mitigate the revealed biases. Overall, the key aim of our work is to tackle the machine learning bias problem early in the pipeline. REVISE is available at https://github.com/princetonvisualai/revise-tool