  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.)
