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Crowdsourcing Detection of Sampling Biases in Image Datasets

Published: 20 April 2020 Publication History

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.

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        cover image ACM Conferences
        WWW '20: Proceedings of The Web Conference 2020
        April 2020
        3143 pages
        ISBN:9781450370233
        DOI:10.1145/3366423
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Publication History

        Published: 20 April 2020

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        Author Tags

        1. crowdsourcing
        2. image dataset
        3. sampling bias
        4. workflow design

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        • Research-article
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        • Refereed limited

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        WWW '20
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        WWW '20: The Web Conference 2020
        April 20 - 24, 2020
        Taipei, Taiwan

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        Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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        Cited By

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        • (2024)To Err Is AI! Debugging as an Intervention to Facilitate Appropriate Reliance on AI SystemsProceedings of the 35th ACM Conference on Hypertext and Social Media10.1145/3648188.3675130(98-105)Online publication date: 10-Sep-2024
        • (2024)The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and GuidelinesProceedings of the ACM on Human-Computer Interaction10.1145/36410238:CSCW1(1-45)Online publication date: 26-Apr-2024
        • (2024)SymLearn: A Symbiotic Crowd-AI Collective Learning Framework to Web-based Healthcare Policy Adherence AssessmentProceedings of the ACM Web Conference 202410.1145/3589334.3645519(2497-2508)Online publication date: 13-May-2024
        • (2024)MindSet: A Bias-Detection Interface Using a Visual Human-in-the-Loop WorkflowArtificial Intelligence. ECAI 2023 International Workshops10.1007/978-3-031-50485-3_8(93-105)Online publication date: 25-Jan-2024
        • (2023)The effects of AI biases and explanations on human decision fairnessProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/343(3076-3084)Online publication date: 19-Aug-2023
        • (2023)Representation Bias in Data: A Survey on Identification and Resolution TechniquesACM Computing Surveys10.1145/358843355:13s(1-39)Online publication date: 13-Jul-2023
        • (2022)An Edge Detection Method Based on Local Gradient Estimation: Application to High-Temperature Metallic Droplet ImagesApplied Sciences10.3390/app1214697612:14(6976)Online publication date: 9-Jul-2022
        • (2022)A Survey on Task Assignment in CrowdsourcingACM Computing Surveys10.1145/349452255:3(1-35)Online publication date: 3-Feb-2022
        • (2022)What Should You Know? A Human-In-the-Loop Approach to Unknown Unknowns Characterization in Image RecognitionProceedings of the ACM Web Conference 202210.1145/3485447.3512040(882-892)Online publication date: 25-Apr-2022
        • (2022)A survey on bias in visual datasetsComputer Vision and Image Understanding10.1016/j.cviu.2022.103552223:COnline publication date: 1-Oct-2022
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