Changing the focus: worker-centric optimization in human-in-the-loop computations

A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back to humans, and study different data analytics problems, by recognizing characteristics of the human workers, and how to incorporate those in a principled fashion inside the computation loop. The first contribution of this dissertation is to propose an optimization framework and a real world system to personalize worker’s behavior by developing a worker model and using that to better understand and estimate task completion time. The framework judiciously frames questions and solicits worker feedback on those to update the worker model. Next, improving workers skills through peer interaction during collaborative task completion is studied. A suite of optimization problems are identified in that context considering collaborativeness between the members as it plays a major role in peer learning. Finally, “diversified” sequence of work sessions for human workers is designed to improve worker satisfaction and engagement while completing tasks

Novel Methods for Designing Tasks in Crowdsourcing

Crowdsourcing is becoming more popular as a means for scalable data processing that requires human intelligence. The involvement of groups of people to accomplish tasks could be an effective success factor for data-driven businesses. Unlike in other technical systems, the quality of the results depends on human factors and how well crowd workers understand the requirements of the task, to produce high-quality results. Looking at previous studies in this area, we found that one of the main factors that affect workers’ performance is the design of the crowdsourcing tasks. Previous studies of crowdsourcing task design covered a limited set of factors. The main contribution of this research is the focus on some of the less-studied technical factors, such as examining the effect of task ordering and class balance and measuring the consistency of the same task design over time and on different crowdsourcing platforms. Furthermore, this study ambitiously extends work towards understanding workers’ point of view in terms of the quality of the task and the payment aspect by performing a qualitative study with crowd workers and shedding light on some of the ethical issues around payments for crowdsourcing tasks. To achieve our goal, we performed several crowdsourcing experiments on specific platforms and measured the factors that influenced the quality of the overall result

Human factors in crowdsourcing

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