Reply & Supply: Efficient crowdsourcing when workers do more than answer questions
This addresses efficiency and bias issues in crowdsourcing for platforms where workers generate content, but it is incremental as it builds on existing models.
The paper tackles the problem of bias in crowdsourcing when workers contribute new questions, leading to a growing set where early questions get more answers. They introduce network-based algorithms to balance exploring new questions and addressing current ones, showing efficient exploration without losing answer confidence.
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.