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

TL;DR

This paper presents a new crowdsourcing method using pairwise comparisons and Elo scoring that reduces bias and error in subjective annotations compared to traditional majority voting, supported by theoretical and simulation results.

Contribution

It introduces a novel pipeline combining pairwise comparisons with Elo scoring and provides a theoretical framework and agent-based model to demonstrate its advantages over majority voting.

Findings

Comparison approach yields higher $f_1$ scores under subjectivity.

It reduces bias inflation common in majority voting.

Number of comparisons scales as $O(N \log N)$ with items.

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.