Crowds, not Drones: Modeling Human Factors in Interactive Crowdsourcing

International audienceIn this vision paper, we propose SmartCrowd, an intelligent and adaptive crowdsourcing framework. Contrary to existing crowdsourcing systems, where the process of hiring workers (crowd), learning their skills, and evaluating the accuracy of tasks they perform are fragmented, siloed, and often ad-hoc, SmartCrowd foresees a paradigm shift in that process, considering unpredictability of human nature, namely human factors. SmartCrowd offers opportunities in making crowdsourcing intelligent through iterative interaction with the workers, and adaptively learning and improving the underlying processes. Both existing (majority of which do not require longer engagement from volatile and mostly non-recurrent workers) and next generation crowdsourcing applications (which require longer engagement from the crowd) stand to benefit from SmartCrowd. We outline the opportunities in SmartCrowd, and discuss the challenges and directions, that can potentially revolutionize the existing crowdsourcing landscape

Crowds, not Drones: Modeling Human Factors in Interactive Crowdsourcing

International audienceIn this vision paper, we propose SmartCrowd, an intelligent and adaptive crowdsourcing framework. Contrary to existing crowdsourcing systems, where the process of hiring workers (crowd), learning their skills, and evaluating the accuracy of tasks they perform are fragmented, siloed, and often ad-hoc, SmartCrowd foresees a paradigm shift in that process, considering unpredictability of human nature, namely human factors. SmartCrowd offers opportunities in making crowdsourcing intelligent through iterative interaction with the workers, and adaptively learning and improving the underlying processes. Both existing (majority of which do not require longer engagement from volatile and mostly non-recurrent workers) and next generation crowdsourcing applications (which require longer engagement from the crowd) stand to benefit from SmartCrowd. We outline the opportunities in SmartCrowd, and discuss the challenges and directions, that can potentially revolutionize the existing crowdsourcing landscape

Empirical Methodology for Crowdsourcing Ground Truth

The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods for populating the Semantic Web. Crowdsourcing-based approaches are gaining popularity in the attempt to solve the issues related to volume of data and lack of annotators. Typically these practices use inter-annotator agreement as a measure of quality. However, in many domains, such as event detection, there is ambiguity in the data, as well as a multitude of perspectives of the information examples. We present an empirically derived methodology for efficiently gathering of ground truth data in a diverse set of use cases covering a variety of domains and annotation tasks. Central to our approach is the use of CrowdTruth metrics that capture inter-annotator disagreement. We show that measuring disagreement is essential for acquiring a high quality ground truth. We achieve this by comparing the quality of the data aggregated with CrowdTruth metrics with majority vote, over a set of diverse crowdsourcing tasks: Medical Relation Extraction, Twitter Event Identification, News Event Extraction and Sound Interpretation. We also show that an increased number of crowd workers leads to growth and stabilization in the quality of annotations, going against the usual practice of employing a small number of annotators.Comment: in publication at the Semantic Web Journa

Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa

This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted crowdsourcing approach, where multiple annotators with unknown expertise contribute an uncontrolled amount (often limited) of annotations. Our framework leverages the low-rank structure in annotations to learn individual annotator expertise, which then helps to infer the true labels from noisy and sparse annotations. It provides a unified Bayesian model to simultaneously infer the true labels and train the deep learning model in order to reach an optimal learning efficacy. Finally, our framework exploits the uncertainty of the deep learning model during prediction as well as the annotators' estimated expertise to minimize the number of required annotations and annotators for optimally training the deep learning model. We evaluate the effectiveness of our framework for intent classification in Alexa (Amazon's personal assistant), using both synthetic and real-world datasets. Experiments show that our framework can accurately learn annotator expertise, infer true labels, and effectively reduce the amount of annotations in model training as compared to state-of-the-art approaches. We further discuss the potential of our proposed framework in bridging machine learning and crowdsourcing towards improved human-in-the-loop systems

Privacy in crowdsourcing:a systematic review

The advent of crowdsourcing has brought with it multiple privacy challenges. For example, essential monitoring activities, while necessary and unavoidable, also potentially compromise contributor privacy. We conducted an extensive literature review of the research related to the privacy aspects of crowdsourcing. Our investigation revealed interesting gender differences and also differences in terms of individual perceptions. We conclude by suggesting a number of future research directions.</p

Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort