Cheaper and Better: Selecting Good Workers for Crowdsourcing

2015-11-12

Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 3

Volume

Vol. 3 (2015): Third AAAI Conference on Human Computation and Crowdsourcing

Works in Progress

Crowdsourcing provides a popular paradigm for data collection at scale. We study the problem of selecting subsets of workers from a given worker pool to maximize the accuracy under a budget constraint. One natural question is whether we should hire as many workers as the budget allows, or restrict on a small number of top-quality workers. By theoretically analyzing the error rate of a typical setting in crowdsourcing, we frame the worker selection problem into a combinatorial optimization problem and propose an algorithm to solve it efficiently. Empirical results on both simulated and real-world datasets show that our algorithm is able to select a small number of high-quality workers, and performs as good as, sometimes even better than, the much larger crowds as the budget allows.

10.1609/hcomp.v3i1.13248