BUOCA: Budget-Optimized Crowd Worker Allocation

Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We here show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze easy-to-label data and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the allocations of the number of workers to a task can be computed optimally based on task features alone, without using worker profiles. Our target tasks are delineating cells in microscopy images and analyzing the sentiment toward the 2016 U.S. presidential candidates in tweets. We first propose an algorithm that computes budget-optimized crowd worker allocation (BUOCA). We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling. We show that the computed allocation can yield large savings in the crowdsourcing budget (up to 49 percent points) while maintaining labeling accuracy. Finally, we envisage a human-machine system for performing budget-optimized data analysis at a scale beyond the feasibility of crowdsourcing.First author draf

BUOCA: Budget-Optimized Crowd Worker Allocation

Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We here show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze easy-to-label data and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the allocations of the number of workers to a task can be computed optimally based on task features alone, without using worker profiles. Our target tasks are delineating cells in microscopy images and analyzing the sentiment toward the 2016 U.S. presidential candidates in tweets. We first propose an algorithm that computes budget-optimized crowd worker allocation (BUOCA). We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling. We show that the computed allocation can yield large savings in the crowdsourcing budget (up to 49 percent points) while maintaining labeling accuracy. Finally, we envisage a human-machine system for performing budget-optimized data analysis at a scale beyond the feasibility of crowdsourcing

Accurate and budget-efficient text, image, and video analysis systems powered by the crowd

Crowdsourcing systems empower individuals and companies to outsource labor-intensive tasks that cannot currently be solved by automated methods and are expensive to tackle by domain experts. Crowdsourcing platforms are traditionally used to provide training labels for supervised machine learning algorithms. Crowdsourced tasks are distributed among internet workers who typically have a range of skills and knowledge, differing previous exposure to the task at hand, and biases that may influence their work. This inhomogeneity of the workforce makes the design of accurate and efficient crowdsourcing systems challenging. This dissertation presents solutions to improve existing crowdsourcing systems in terms of accuracy and efficiency. It explores crowdsourcing tasks in two application areas, political discourse and annotation of biomedical and everyday images. The first part of the dissertation investigates how workers' behavioral factors and their unfamiliarity with data can be leveraged by crowdsourcing systems to control quality. Through studies that involve familiar and unfamiliar image content, the thesis demonstrates the benefit of explicitly accounting for a worker's familiarity with the data when designing annotation systems powered by the crowd. The thesis next presents Crowd-O-Meter, a system that automatically predicts the vulnerability of crowd workers to believe \enquote{fake news} in text and video. The second part of the dissertation explores the reversed relationship between machine learning and crowdsourcing by incorporating machine learning techniques for quality control of crowdsourced end products. In particular, it investigates if machine learning can be used to improve the quality of crowdsourced results and also consider budget constraints. The thesis proposes an image analysis system called ICORD that utilizes behavioral cues of the crowd worker, augmented by automated evaluation of image features, to infer the quality of a worker-drawn outline of a cell in a microscope image dynamically. ICORD determines the need to seek additional annotations from other workers in a budget-efficient manner. Next, the thesis proposes a budget-efficient machine learning system that uses fewer workers to analyze easy-to-label data and more workers for data that require extra scrutiny. The system learns a mapping from data features to number of allocated crowd workers for two case studies, sentiment analysis of twitter messages and segmentation of biomedical images. Finally, the thesis uncovers the potential for design of hybrid crowd-algorithm methods by describing an interactive system for cell tracking in time-lapse microscopy videos, based on a prediction model that determines when automated cell tracking algorithms fail and human interaction is needed to ensure accurate tracking

A Statistical Analysis of the Aggregation of Crowdsourced Labels

Crowdsourcing, due to its inexpensive and timely nature, has become a popular method of collecting data that is difficult for computers to generate. We focus on using this method of human computation to gather labels for classification tasks, to be used for machine learning. However, data gathered this way may be of varying quality, ranging from spam to perfect. We aim to maintain the cost-effective property of crowdsourcing, while also obtaining quality results. Towards a solution, we have multiple workers label the same problem instance, aggregating the responses into one label afterwards. We study what aggregation method to use, and what guarantees we can provide on its estimates. Different crowdsourcing models call for different techniques – we outline and organize various directions taken in the literature, and focus on the Dawid-Skene model. In this setting each instance has a true label, workers are independent, and the performance of each individual is assumed to be uniform over all instances, in the sense that she has an inherent skill that governs the probability with which she labels correctly. Her skill is unknown to us. Aggregation methods aim to find the true label of each task based solely on the labels the workers reported. We measure the performance of these methods by the probability with which the estimates they output match the true label. In practice, a popular procedure is to run the EM algorithm to find estimates of the skills and labels. However, this method is not directly guaranteed to perform well in our measure. We collect and evaluate theoretical results that bound the error of various aggregation methods, including specific variants of EM. Finally, we prove a guarantee on the error suffered by the maximum likelihood estimator, the global optima of the function that EM aims to numerically optimize

Analyzing crowd workers' learning behavior to obtain more reliable labels

Crowdsourcing is a popular means to obtain high-quality labels for datasets at moderate costs. These crowdsourced datasets are then used for training supervised or semisupervised predictors. This implies that the performance of the resulting predictors depends on the quality/reliability of the labels that crowd workers assigned – low reliability usually leads to poorly performing predictors. In practice, label reliability in crowdsourced datasets varies substantially depending on multiple factors such as the difficulty of the labeling task at hand, the characteristics and motivation of the participating crowd workers, or the difficulty of the documents to be labeled. Different approaches exist to mitigate the effects of the aforementioned factors, for example by identifying spammers based on their annotation times and removing their submitted labels. To complement existing approaches for improving label reliability in crowdsourcing, this thesis explores label reliability from two perspectives: first, how the label reliability of crowd workers develops over time during an actual labeling task, and second how it is affected by the difficulty of the documents to be labeled. We find that label reliability of crowd workers increases after they labeled a certain number of documents. Motivated by our finding that the label reliability for more difficult documents is lower, we propose a new crowdsourcing methodology to improve label reliability: given an unlabeled dataset to be crowdsourced, we first train a difficulty predictor v on a small seed set and the predictor then estimates the difficulty level in the remaining unlabeled documents. This procedure might be repeated multiple times until the performance of the difficulty predictor is sufficient. Ultimately, difficult documents are separated from the rest, so that only the latter documents are crowdsourced. Our experiments demonstrate the feasibility of this method