C. Chiang

Susumu Saito, Chun-Wei Chiang, Saiph Savage et al.

Workers in crowd markets struggle to earn a living. One reason for this is that it is difficult for workers to accurately gauge the hourly wages of microtasks, and they consequently end up performing labor with little pay. In general, workers are provided with little information about tasks, and are left to rely on noisy signals, such as textual description of the task or rating of the requester. This study explores various computational methods for predicting the working times (and thus hourly wages) required for tasks based on data collected from other workers completing crowd work. We provide the following contributions. (i) A data collection method for gathering real-world training data on crowd-work tasks and the times required for workers to complete them; (ii) TurkScanner: a machine learning approach that predicts the necessary working time to complete a task (and can thus implicitly provide the expected hourly wage). We collected 9,155 data records using a web browser extension installed by 84 Amazon Mechanical Turk workers, and explored the challenge of accurately recording working times both automatically and by asking workers. TurkScanner was created using ~150 derived features, and was able to predict the hourly wages of 69.6% of all the tested microtasks within a 75% error. Directions for future research include observing the effects of tools on people's working practices, adapting this approach to a requester tool for better price setting, and predicting other elements of work (e.g., the acceptance likelihood and worker task preferences.)

Anna Kasunic, Chun-Wei Chiang, Geoff Kaufman et al.

In 2013, scholars laid out a framework for a sustainable, ethical future of crowd work, recommending career ladders so that crowd work can lead to career advancement and more economic mobility. Five years later, we consider this vision in the context of Amazon Mechanical Turk (AMT). To understand how workers currently view their experiences on AMT, and how they publicly present and share these experiences in their professional lives, we conducted a survey study with workers on AMT (n=98). The survey we administered included a combination of multiple choice, binary, and open-ended (short paragraph) items gauging Turkers' perceptions of their experiences on AMT within the context of their broader work experience and career goals. This work extends existing understandings of who crowd workers are and why they crowd work by seeking to better understand how crowd work factors into Turkers' professional profiles, and how we can subsequently better support crowd workers in their career advancement. Our survey results can inform the design of better tools to empower crowd workers in their professional development both inside and outside of AMT.

Chun-Wei Chiang, Anna Kasunic, Saiph Savage

Traditional employment usually provides mechanisms for workers to improve their skills to access better opportunities. However, crowd work platforms like Amazon Mechanical Turk (AMT) generally do not support skill development (i.e., becoming faster and better at work). While researchers have started to tackle this problem, most solutions are dependent on experts or requesters willing to help. However, requesters generally lack the necessary knowledge, and experts are rare and expensive. To further facilitate crowd workers' skill growth, we present Crowd Coach, a system that enables workers to receive peer coaching while on the job. We conduct a field experiment and real world deployment to study Crowd Coach in the wild. Hundreds of workers used Crowd Coach in a variety of tasks, including writing, doing surveys, and labeling images. We find that Crowd Coach enhances workers' speed without sacrificing their work quality, especially in audio transcription tasks. We posit that peer coaching systems hold potential for better supporting crowd workers' skill development while on the job. We finish with design implications from our research.

Chun-Wei Chiang, Caroline Anderson, Claudia Flores-Saviaga et al.

Mobile money can facilitate financial inclusion in developing countries, which usually have high mobile phone use and steady remittance activity. Many countries in Latin America meet the minimum technological requirements to use mobile money, however, the adoption in this region is relatively low. This paper investigates the different factors that lead people in Latin America to distrust and therefore not adopt mobile money. For this purpose, we analyzed 27 mobile money applications on the market and investigated the perceptions that people in Latin America have of such interfaces. From our study, we singled out the interface features that have the greatest influence in user adoption in developing countries. We identified that for the Latin America market it is crucial to create mobile applications that allow the user to visualize and understand the workflow through which their money is traveling to recipients. We examined the significance of these findings in the design of future mobile money applications that can effectively improve the use of electronic financial transactions in Latin America.