Approval Voting and Incentives in Crowdsourcing

TL;DR

This paper proposes an approval voting system combined with an incentive-compatible mechanism to improve the quality of crowdsourced labels by leveraging partial worker knowledge and aligning incentives.

Contribution

It introduces a novel approval voting method with a proper incentive mechanism and provides theoretical guarantees and empirical validation.

Findings

The mechanism is theoretically optimal under certain conditions.

Empirical studies on Amazon Mechanical Turk support the effectiveness of the approach.

Approval voting captures partial knowledge better than single-choice methods.

Abstract

The growing need for labeled training data has made crowdsourcing an important part of machine learning. The quality of crowdsourced labels is, however, adversely affected by three factors: (1) the workers are not experts; (2) the incentives of the workers are not aligned with those of the requesters; and (3) the interface does not allow workers to convey their knowledge accurately, by forcing them to make a single choice among a set of options. In this paper, we address these issues by introducing approval voting to utilize the expertise of workers who have partial knowledge of the true answer, and coupling it with a ("strictly proper") incentive-compatible compensation mechanism. We show rigorous theoretical guarantees of optimality of our mechanism together with a simple axiomatic characterization. We also conduct preliminary empirical studies on Amazon Mechanical Turk which validate our approach.