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|Abstract:||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. To tackle this issue and to enable the preemptive analysis of large-scale dataset, we present our tool. REVISE (REvealing VIsual biaSEs) is a tool that assists in the investigation of a visual dataset, surfacing potential biases currently along three dimensions: (1) object-based, (2) gender-based, and (3) geography-based. Object-based biases relate to size, context, or diversity of object representation. Gender-based metrics aim to reveal the stereotypical portrayal of people of different genders. Geography-based analyses consider the representation of different geographic locations. REVISE sheds light on the dataset al.ong these dimensions; 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 then 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.|
|Citation:||Wang, Angelina, Arvind Narayanan, and Olga Russakovsky. "REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets." In European Conference on Computer Vision (2020): pp. 733-751. doi:10.1007/978-3-030-58580-8_43|
|Pages:||733 - 751|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||European Conference on Computer Vision|
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