Comprehensive and Reliable Crowd Assessment Algorithms

TL;DR

This paper introduces new algorithms for evaluating crowd workers by calculating tight confidence intervals on their error rates, even under complex and realistic scenarios, improving reliability in crowdsourcing systems.

Contribution

It presents novel techniques for confidence interval estimation that are more concise and applicable to diverse, real-world crowdsourcing conditions, unlike prior methods.

Findings

Techniques produce tighter confidence intervals.

Methods work under partial worker-task overlaps.

Validated on multiple real-world datasets.

Abstract

Evaluating workers is a critical aspect of any crowdsourcing system. In this paper, we devise techniques for evaluating workers by finding confidence intervals on their error rates. Unlike prior work, we focus on "conciseness"---that is, giving as tight a confidence interval as possible. Conciseness is of utmost importance because it allows us to be sure that we have the best guarantee possible on worker error rate. Also unlike prior work, we provide techniques that work under very general scenarios, such as when not all workers have attempted every task (a fairly common scenario in practice), when tasks have non-boolean responses, and when workers have different biases for positive and negative tasks. We demonstrate conciseness as well as accuracy of our confidence intervals by testing them on a variety of conditions and multiple real-world datasets.