TITLE: REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets 

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. 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
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
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
http://arxiv.org/abs/2004.07999v4