Does Confidence Reporting from the Crowd Benefit Crowdsourcing Performance?
This addresses crowdsourcing system design for classification tasks, providing a counterintuitive insight that is incremental in optimizing aggregation methods.
The paper tackles the problem of designing an effective crowdsourcing system for M-ary classification by allowing workers to skip tasks and report confidence levels, finding that confidence reporting does not improve classification performance, so designers should use only the reject option.
We explore the design of an effective crowdsourcing system for an $M$-ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final classification decision. We consider the scenario where the workers have a reject option so that they are allowed to skip microtasks when they are unable to or choose not to respond to binary microtasks. Additionally, the workers report quantized confidence levels when they are able to submit definitive answers. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize crowd's classification performance. We obtain a couterintuitive result that the classification performance does not benefit from workers reporting quantized confidence. Therefore, the crowdsourcing system designer should employ the reject option without requiring confidence reporting.