Eliciting Worker Preference for Task Completion

TL;DR

This paper demonstrates that explicitly eliciting worker preferences in crowdsourcing improves task completion quality and worker modeling accuracy compared to implicit methods, through a generic framework and extensive experiments.

Contribution

It introduces a novel framework for explicit preference elicitation in crowdsourcing, enhancing worker models and task outcomes.

Findings

Explicit preference elicitation outperforms implicit methods statistically.

Iterative preference requests improve worker model accuracy.

Framework is effective in realistic crowdsourcing scenarios.

Abstract

Current crowdsourcing platforms provide little support for worker feedback. Workers are sometimes invited to post free text describing their experience and preferences in completing tasks. They can also use forums such as Turker Nation1 to exchange preferences on tasks and requesters. In fact, crowdsourcing platforms rely heavily on observing workers and inferring their preferences implicitly. In this work, we believe that asking workers to indicate their preferences explicitly improve their experience in task completion and hence, the quality of their contributions. Explicit elicitation can indeed help to build more accurate worker models for task completion that captures the evolving nature of worker preferences. We design a worker model whose accuracy is improved iteratively by requesting preferences for task factors such as required skills, task payment, and task relevance. We propose a generic framework, develop efficient solutions in realistic scenarios, and run extensive experiments that show the benefit of explicit preference elicitation over implicit ones with statistical significance.