# Doubly Robust Crowdsourcing

## TL;DR

This paper introduces a doubly robust statistical method for aggregating noisy crowdsourced labels, significantly reducing label costs while maintaining high accuracy through adaptive selection and robust estimation techniques.

## Contribution

It proposes a novel doubly robust estimation framework for label aggregation in crowdsourcing, improving efficiency and accuracy even with poor worker models.

## Key findings

- Variance of estimation is substantially reduced.
- Lower label costs achieve near-ideal accuracy.
- Adaptive worker/item selection enhances efficiency.

## Abstract

Large-scale labeled dataset is the indispensable fuel that ignites the AI revolution as we see today. Most such datasets are constructed using crowdsourcing services such as Amazon Mechanical Turk which provides noisy labels from non-experts at a fair price. The sheer size of such datasets mandates that it is only feasible to collect a few labels per data point. We formulate the problem of test-time label aggregation as a statistical estimation problem of inferring the expected voting score. By imitating workers with supervised learners and using them in a doubly robust estimation framework, we prove that the variance of estimation can be substantially reduced, even if the learner is a poor approximation. Synthetic and real-world experiments show that by combining the doubly robust approach with adaptive worker/item selection rules, we often need much lower label cost to achieve nearly the same accuracy as in the ideal world where all workers label all data points.