Reply & Supply: Efficient crowdsourcing when workers do more than answer questions

TL;DR

This paper presents algorithms for efficient crowdsourcing when workers contribute both answers and new questions, addressing growth bias in question sets modeled as networks to ensure reliable crowd answers.

Contribution

It introduces network-based models and algorithms to balance exploration and exploitation in crowdsourcing with growing question sets, reducing bias and maintaining answer confidence.

Findings

Algorithms effectively explore unbounded question sets

Balanced worker distribution reduces growth bias

Crowd answer confidence is maintained over growth

Abstract

Crowdsourcing works by distributing many small tasks to large numbers of workers, yet the true potential of crowdsourcing lies in workers doing more than performing simple tasks---they can apply their experience and creativity to provide new and unexpected information to the crowdsourcer. One such case is when workers not only answer a crowdsourcer's questions but also contribute new questions for subsequent crowd analysis, leading to a growing set of questions. This growth creates an inherent bias for early questions since a question introduced earlier by a worker can be answered by more subsequent workers than a question introduced later. Here we study how to perform efficient crowdsourcing with such growing question sets. By modeling question sets as networks of interrelated questions, we introduce algorithms to help curtail the growth bias by efficiently distributing workers between exploring new questions and addressing current questions. Experiments and simulations demonstrate that these algorithms can efficiently explore an unbounded set of questions without losing confidence in crowd answers.