Crowdsourcing subjective annotations using pairwise comparisons reduces bias and error compared to the majority-vote method

• URL: http://arxiv.org/abs/2305.20042v2
• Date: Thu, 1 Jun 2023 13:18:27 GMT
• Title: Crowdsourcing subjective annotations using pairwise comparisons reduces bias and error compared to the majority-vote method
• Authors: Hasti Narimanzadeh, Arash Badie-Modiri, Iuliia Smirnova, Ted Hsuan Yun Chen
• Abstract summary: We introduce a theoretical framework for understanding how random error and measurement bias enter into crowdsourced annotations of subjective constructs. We then propose a pipeline that combines pairwise comparison labelling with Elo scoring, and demonstrate that it outperforms the ubiquitous majority-voting method in reducing both types of measurement error.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How to better reduce measurement variability and bias introduced by subjectivity in crowdsourced labelling remains an open question. We introduce a theoretical framework for understanding how random error and measurement bias enter into crowdsourced annotations of subjective constructs. We then propose a pipeline that combines pairwise comparison labelling with Elo scoring, and demonstrate that it outperforms the ubiquitous majority-voting method in reducing both types of measurement error. To assess the performance of the labelling approaches, we constructed an agent-based model of crowdsourced labelling that lets us introduce different types of subjectivity into the tasks. We find that under most conditions with task subjectivity, the comparison approach produced higher $f_1$ scores. Further, the comparison approach is less susceptible to inflating bias, which majority voting tends to do. To facilitate applications, we show with simulated and real-world data that the number of required random comparisons for the same classification accuracy scales log-linearly $O(N \log N)$ with the number of labelled items. We also implemented the Elo system as an open-source Python package.

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