TY - GEN
T1 - REVISE
T2 - 16th European Conference on Computer Vision, ECCV 2020
AU - Wang, Angelina
AU - Narayanan, Arvind
AU - Russakovsky, Olga
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Computer vision fairness
KW - Dataset analysis
KW - Dataset bias
UR - http://www.scopus.com/inward/record.url?scp=85097826341&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097826341&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-58580-8_43
DO - 10.1007/978-3-030-58580-8_43
M3 - Conference contribution
AN - SCOPUS:85097826341
SN - 9783030585792
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 733
EP - 751
BT - Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings
A2 - Vedaldi, Andrea
A2 - Bischof, Horst
A2 - Brox, Thomas
A2 - Frahm, Jan-Michael
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 August 2020 through 28 August 2020
ER -