Incentivizing an Unknown Crowd

ACM Transactions on Intelligent Systems and Technology, 2016

We present an incremental Bayesian model that resolves key issues of crowd size and data quality for consensus labeling. We evaluate our method using data collected from a real-world citizen science program, B ee W atch , which invites members of the public in the United Kingdom to classify (label) photographs of bumblebees as one of 22 possible species. The biological recording domain poses two key and hitherto unaddressed challenges for consensus models of crowdsourcing: (1) the large number of potential species makes classification difficult, and (2) this is compounded by limited crowd availability, stemming from both the inherent difficulty of the task and the lack of relevant skills among the general public. We demonstrate that consensus labels can be reliably found in such circumstances with very small crowd sizes of around three to five users (i.e., through group sourcing). Our incremental Bayesian model, which minimizes crowd size by re-evaluating the quality of the consensu...

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016

Crowdsourcing allows many people to complete tasks of various difficulty with minimal recruitment and administration costs. However, the lack of participant accountability may entice people to complete as many tasks as possible without fully engaging in them, jeopardizing the quality of responses. In this paper, we present a dynamic and time efficient solution to the task assignment problem in crowdsourcing platforms. Our proposed approach, CrowdSelect, offers a theoretically proven algorithm to assign workers to tasks in a cost efficient manner, while ensuring high accuracy of the overall task. In contrast to existing works, our approach makes minimal assumptions on the probability of error for workers, and completely removes the assumptions that such probability is known apriori and that it remains consistent over time. Through experiments over real Amazon Mechanical Turk traces and synthetic data, we find that CrowdSelect has a significant gain in term of accuracy compared to state-of-the-art algorithms, and can provide a 17.5% gain in answers' accuracy compared to previous methods, even when there are over 50% malicious workers.

International conference on machine learning workshop on machine learning meets crowdsourcing, 2013

Crowdsourcing label generation has been a crucial component for many real-world machine learning applications. In this paper, we provide finite-sample exponential bounds on the error rate (in probability and in expectation) of hyperplane binary labeling rules for the Dawid-Skene (and Symmetric Dawid-Skene ) crowdsourcing model. The bounds can be applied to analyze many commonly used prediction methods, including the majority voting, weighted majority voting and maximum a posteriori (MAP) rules. These bound results can be used to control the error rate and design better algorithms. In particular, under the Symmetric Dawid-Skene model we use simulation to demonstrate that the data-driven EM-MAP rule is a good approximation to the oracle MAP rule which approximately optimizes our upper bound on the mean error rate for any hyperplane binary labeling rule. Meanwhile, the average error rate of the EM-MAP rule is bounded well by the upper bound on the mean error rate of the oracle MAP rule in the simulation.

ArXiv, 2015

Incentivizing effort and eliciting truthful responses from agents in the absence of verifiability is a major challenge faced while crowdsourcing many types of evaluation tasks like labeling images, grading assignments in online courses, etc. In this paper, we propose new reward mechanisms for such settings that, unlike most previously studied mechanisms, impose minimal assumptions on the structure and knowledge of the underlying generating model, can account for heterogeneity in the agents' abilities, require no extraneous elicitation from them, and furthermore allow their beliefs to be (almost) arbitrary. Moreover, these mechanisms have the simple and intuitive structure of output agreement mechanisms, which, despite not incentivizing truthful behavior, have nevertheless been quite popular in practice. We achieve this by leveraging a typical characteristic of many of these settings, which is the existence of a large number of similar tasks.

Proceedings of the AAAI Conference on Artificial Intelligence, 2019

With crowdsourcing systems, labels can be obtained with low cost, which facilitates the creation of training sets for prediction model learning. However, the labels obtained from crowdsourcing are often imperfect, which brings great challenges in model learning. Since 2008, the machine learning community has noticed the great opportunities brought by crowdsourcing and has developed a large number of techniques to deal with inaccuracy, randomness, and uncertainty issues when learning with crowdsourcing. This paper summarizes the technical progress in this field during past eleven years. We focus on two fundamental issues: the data (label) quality and the prediction model quality. For data quality, we summarize ground truth inference methods and some machine learning based methods to further improve data quality. For the prediction model quality, we summarize several learning paradigms developed under the crowdsourcing scenario. Finally, we further discuss several promising future res...

Proceedings of the 22nd international conference on World Wide Web, 2013

Crowdsourcing is now widely used to replace judgement or evaluation by an expert authority with an aggregate evaluation from a number of non-experts, in applications ranging from rating and categorizing online content all the way to evaluation of student assignments in massively open online courses (MOOCs) via peer grading. A key issue in these settings, where direct monitoring of both effort and accuracy is infeasible, is incentivizing agents in the 'crowd' to put in effort to make good evaluations, as well as to truthfully report their evaluations. We study the design of mechanisms for crowdsourced judgement elicitation when workers strategically choose both their reports and the effort they put into their evaluations. This leads to a new family of information elicitation problems with unobservable ground truth, where an agent's proficiency-the probability with which she correctly evaluates the underlying ground truth-is endogenously determined by her strategic choice of how much effort to put into the task. Our main contribution is a simple, new, mechanism for binary information elicitation for multiple tasks when agents have endogenous proficiencies, with the following properties: (i) Exerting maximum effort followed by truthful reporting of observations is a Nash equilibrium. (ii) This is the equilibrium with maximum payoff to all agents, even when agents have different maximum proficiencies, can use mixed strategies, and can choose a different strategy for each of their tasks. Our information elicitation mechanism requires only minimal bounds on the priors, asks agents to only report their own evaluations, and does not require any conditions on a diverging number of agent reports per task to achieve its incentive properties. The main idea behind our mechanism is to use the presence of multiple tasks and ratings to estimate a reporting statistic to identify and penalize low-effort agreement-the mechanism rewards agents for agreeing with another 'reference' agent report on the same task but also penalizes for blind agreement by subtracting out this statistic term, designed so that agents obtain rewards only when they put in effort into their observations.

2014

Recently, there has been a burst in the number of research projects on human computation via crowdsourcing. Multiple choice (or labeling) questions could be referred to as a common type of problem which is solved by this approach. As an application, crowd labeling is applied to find true labels for large machine learning datasets. Since crowds are not necessarily experts, the labels they provide are rather noisy and erroneous. This challenge is usually resolved by collecting multiple labels for each sample, and then aggregating them to estimate the true label. Although the mechanism leads to high-quality labels, it is not actually cost-effective. As a result, efforts are currently made to maximize the accuracy in estimating true labels, while fixing the number of acquired labels. This paper surveys methods to aggregate redundant crowd labels in order to estimate unknown true labels. It presents a unified statistical latent model where the differences among popular methods in the fie...

ArXiv, 2019

Modern machine learning algorithms need large datasets to be trained. Crowdsourcing has become a popular approach to label large datasets in a shorter time as well as at a lower cost comparing to that needed for a limited number of experts. However, as crowdsourcing performers are non-professional and vary in levels of expertise, such labels are much noisier than those obtained from experts. For this reason, in order to collect good quality data within a limited budget special techniques such as incremental relabelling, aggregation and pricing need to be used. We make an introduction to data labeling via public crowdsourcing marketplaces and present key components of efficient label collection. We show how to choose one of real label collection tasks, experiment with selecting settings for the labelling process, and launch label collection project at Yandex.Toloka, one of the largest crowdsourcing marketplace. The projects will be run on real crowds. We also present main algorithms ...

Autonomous Agents and Multi-Agent Systems, 2013

Crowdsourcing applications frequently employ many individual workers, each performing a small amount of work. In such settings, individually determining the reward for each assignment and worker may seem economically beneficial, but is inapplicable if manually performed. We thus consider the problem of designing automated agents for automatic reward determination and negotiation in such settings. We formally describe this problem and show that it is NP-hard. We therefore present two automated agents for the problem, based on two different models of human behavior. The first, the Reservation Price Based Agent (RPBA), is based on the concept of a reservation price, and the second, the No Bargaining Agent (NBA) which tries to avoid any negotiation.The performance of the agents is tested in extensive experiments with real human subjects, where both NBA and RPBA outperform strategies developed by human experts.

Consider a requester who wishes to crowdsource a series of identical binary labeling tasks to a pool of workers so as to achieve an assured accuracy for each task, in a cost optimal way. The workers are heterogeneous with unknown but fixed qualities and their costs are private. The problem is to select for each task an optimal subset of workers so that the outcome obtained after aggregating the labels from the selected workers guarantees a target accuracy level. The problem is a challenging one even in a non strategic setting since the accuracy of aggregated label depends on unknown qualities. We develop a novel multi-armed bandit (MAB) mechanism for solving this problem. First, we propose a framework, Assured Accuracy Bandit (AAB), which leads to a MAB algorithm, Constrained Confidence Bound for a Non Strategic setting (CCB-NS). We derive an upper bound on the number of time steps the algorithm chooses a suboptimal set that depends on the target accuracy level and true qualities. A more challenging situation arises when the requester not only has to learn the qualities of the workers but also elicit their true costs. We modify the CCB-NS algorithm to obtain an adaptive exploration separated algorithm which we call Constrained Confidence Bound for a Strategic setting (CCB-S). CCB-S algorithm produces an ex-post monotone allocation rule and thus can be transformed into an ex-post incentive compatible and ex-post individually rational mechanism that learns the qualities of the workers and guarantees a given target accuracy level in a cost optimal way. We also provide a lower bound on the number of times any algorithm should select a sub-optimal set and we see that the lower bound matches our upper bound upto a constant factor. We provide insights on the practical implementation of this framework through an illustrative example and we show the efficacy of our algorithms through simulations.