# Does Confidence Reporting from the Crowd Benefit Crowdsourcing Performance?

## TL;DR

This paper investigates whether confidence reporting improves crowdsourcing classification accuracy and finds that allowing workers to reject microtasks without reporting confidence yields better performance.

## Contribution

The study introduces an aggregation method with optimized weighting and demonstrates that confidence reporting does not enhance classification performance in crowdsourcing.

## Key findings

- Confidence reporting does not improve classification accuracy.
- Reject option without confidence reporting is optimal.
- Weighted majority voting maximizes crowd performance.

## Abstract

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.