Summary
The paper focuses on tackling the problem of quality control of the work done by crowdworkers. They created a system named CrowdScape to evaluate the work done by humans through mixed-initiative machine learning and interactive visualization. The provided details of quality control in crowdsourcing. This involved mentioning various methods that help evaluate the content. Some methods mentioned were post-hoc output evaluations, behavioral traces, and integrated quality control. CrowdScape is a system developed by the authors to capture worker behavior, also in the form of interactive data visualizations. The system incorporates various techniques to monitor user behavior. It helps to understand if the work was done diligently or was done in a rush. The output of the work is indeed a good indicator of the quality of the work; however, an in-depth review of the user behavior is needed to understand the method in which the worker completed the task.
Reflection
To be very honest, I found this paper fascinating and extremely important for research work in this domain. Ensuring the work submitted is of good quality not only helps legitimize the output of the experiment but also increases trust in the platform as a whole. I was astonished to read that about one-third of all submissions are of low quality. The stats suggest that we are wasting a significant amount of resources.
The paper mentions that the tool uses two sources of data, output, and worker behavior. I was intrigued by how they took into account the worker’s behavior, like accounting for the time taken to complete the task, the way the work was completed, including scrolling, key-press, and other activities. I was curious to know if the worker’s consent was explicitly taken. It would also be an interesting study to see if knowing that the behavior is being recorded affects performance. Additionally, dynamic feedback can also be incorporated. By feedback, I mean, if the worker is supposed to take “x” min, alerting them that the time taken on the task is too low. This will prompt them to take the work more seriously and avoid unnecessary rejection of the task.
I have a comment on the collection of YouTube video tutorials. One of the features taken into account was ‘Total Time’, that signified if the worker had seen the video completely first and then summarized the content. However, I would like to point out that sometimes videos can be watched at an increased playback speed. I sometimes end up watching most tutorial related videos at 1.5 times speed. Hence, if the total time taken is lesser than expected, it might simply signify that they might have watched it at a different speed. A simple check could help solve the problem. YouTube generally has a fixed number of playback speeds. Considering that into account, when calculating the total time might be a viable option.
Questions
- How are you ensuring the quality of the work completed by crowdworkers for your course project?
- Were the workers informed that their behavior was “watched”? Would the behavior and, subsequently, the performance change if they are aware of the situation?
- Workers might use different playback speeds to watch videos. How is that situation handled here?
Hello Bipasha,
to answer your questions, in our project we would like to record the crowdworker’s implicit feedback but we weren’t planning on recording their behavior as explained by this paper. We weren’t planning on telling the workers since we thought that might effect their behavior when answering but this paper has made me think about the ethical concerns behind this and we might be disclosing it. Still deciding on that.