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Crowdsourcing Detection of Sampling Biases in Image Datasets
The Web Conference (WWW) 2020, Taipei, Taiwan
  • Xiao Hu, Purdue University
  • Haobo Wang, Purdue University
  • Anirudh Vegesana, Purdue University
  • Somesh Dube, Purdue University
  • Kaiwen Yu, Purdue University
  • Gore Kao, Purdue University
  • Shuo-Han Chen, Taiwan
  • Yung-Hsiang Lu, Purdue University
  • George K. Thiruvathukal, Loyola University Chicago
  • Ming Yin, Purdue University
Document Type
Conference Proceeding
Publication Date
1-1-2020
Publisher Name
ACM
Disciplines
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.

Creative Commons License
Creative Commons Attribution-Noncommercial 4.0
Citation Information
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.