Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons

TL;DR

This paper introduces a novel crowdsourcing approach that leverages group-based rough estimates and machine learning to efficiently infer individual labels, especially for complex or continuous data, reducing costs and improving accuracy.

Contribution

It extends Ballpark Learning to regression, providing a convex optimization framework for group-based label inference in crowdsourcing, with robustness to outliers and practical evaluation.

Findings

Methods rival supervised models trained on true labels.

Outperforms standard label collection in accuracy and cost.

Effective for high-dimensional and continuous labels.

Abstract

Crowdsourcing has become a popular method for collecting labeled training data. However, in many practical scenarios traditional labeling can be difficult for crowdworkers (for example, if the data is high-dimensional or unintuitive, or the labels are continuous). In this work, we develop a novel model for crowdsourcing that can complement standard practices by exploiting people's intuitions about groups and relations between them. We employ a recent machine learning setting, called Ballpark Learning, that can estimate individual labels given only coarse, aggregated signal over groups of data points. To address the important case of continuous labels, we extend the Ballpark setting (which focused on classification) to regression problems. We formulate the problem as a convex optimization problem and propose fast, simple methods with an innate robustness to outliers. We evaluate our methods on real-world datasets, demonstrating how useful constraints about groups can be harnessed from a crowd of non-experts. Our methods can rival supervised models trained on many true labels, and can obtain considerably better results from the crowd than a standard label-collection process (for a lower price). By collecting rough guesses on groups of instances and using machine learning to infer the individual labels, our lightweight framework is able to address core crowdsourcing challenges and train machine learning models in a cost-effective way.