High-throughput crowdsourcing mechanisms for complex tasks
Crowdsourcing has been identified as a way to facilitate large-scale data processing that requires human input. However, working with a large anonymous user community also poses new challenges. In particular, both possible misjudgment and dishonesty threaten the quality of the results. Common countermeasures are based on redundancy, giving way to a tradeoff between result quality and throughput. Ideally, measures should (1) maintain high throughput and (2) ensure high result quality at the same time. Existing research on crowdsourcing mostly focuses on result quality and pays little attention to throughput or even to the tradeoff between the two. One reason is that the number of tasks (atomic units of work) is usually small. A further problem is that the tasks themselves are small as well. In consequence, existing result quality-improvement mechanisms do not scale to the number or complexity of tasks that arise, for instance, in proofreading and processing of digitized legacy literature. This paper proposes novel mechanisms that (1) are independent of the size and complexity of tasks and (2) allow to trade result quality for throughput to a significant extent. Both mathematical analyses and extensive simulations demonstrate the effectiveness of the proposed mechanisms.
throughput crowdsourcing data quality