Document Type
Conference Proceeding
Publication Date
4-2020
Publication Title
WWW '20: Proceedings of The Web Conference 2020
Pages
2955–2961
Publisher Name
ACM
Abstract
Despite many exciting innovations in computer vision, recent studies reveal a number of risks in existing computer vision systems, suggesting results of such systems may be unfair and untrustworthy. Many of these risks can be partly attributed to the use of a training image dataset that exhibits sampling biases and thus does not accurately reflect the real visual world. Being able to detect potential sampling biases in the visual dataset prior to model development is thus essential for mitigating the fairness and trustworthy concerns in computer vision. In this paper, we propose a three-step crowdsourcing workflow to get humans into the loop for facilitating bias discovery in image datasets. Through two sets of evaluation studies, we find that the proposed workflow can effectively organize the crowd to detect sampling biases in both datasets that are artificially created with designed biases and real-world image datasets that are widely used in computer vision research and system development.
Recommended Citation
Xiao Hu, Haobo Wang, Anirudh Vegesana, Somesh Dube, Kaiwen Yu, Gore Kao, Shuo-Han Chen, Yung-Hsiang Lu, George K. Thiruvathukal, Ming Yin, Crowdsourcing Detection of Sampling Biases in Image Datasets, The Web Conference 2020.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Copyright Statement
© ACM, 2020.
Comments
Author Posting © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for personal use, not for redistribution. The definitive version was published in WWW '20: Proceedings of The Web Conference 2020, April 20xx. https://doi.org/10.1145/3366423.3380063