Summary

In this paper, the authors propose a system that can evaluate complex tasks based on both workers output and behaviors. Other available systems are focus on once aspect of evaluation on either the worker output or behavior which can give poor results especially with complex or creative work. The proposed system combine works through interactive visualization and mixed initiative machine learning. The proposed system, CrowdScape, offers visualization to the users that allow them to filter out poor output to focus on a limited number of responses and use machine learning to measure the similarity of response with the best submission and that way the requester can get the best output and best behavior at the same time. The system provides time series data about for user actions like mouse move or scroll up and down to generate visual timeline for tracing user behavior. The system can work only with web pages and has some limitation, but the value that can give to the customer is high and can enable users to navigate through workers results easily and efficiently.

Reflection

I found the method used by the authors be very interesting. Requesters receive too much information about the workers and visualizing that data can help the requesters to know more about the data, and the use of machine learning can help a lot on classifying or clustering the optimal workers output and behaviors. Other approaches mentioned in the paper are also interesting especially for simple tasks that don’t need complex evaluation.

I also didn’t know that we can get detailed information about the workers output and behavior and found YouTube example mentioned in the paper to be very interesting. The example mentioned shows that MTurk returns everything related to the user actions using the help of JavaScript while working on the YouTube video which can be used in many scenarios. I agree with the authors about the approach which can combine the best of the two approaches. I think it will be interesting to know how many worker response are filtered out in the first phase of the process because that can tell us if sending the request even worthwhile. If too many responses are not considered, then it is possible that task need to be evaluated again.

Questions

• The authors mentioned that their proposed system can help to filter out poor outputs on the first phase. Do you think if to many responses are filtered out means that the guidelines are the selection criteria needs to be reevaluated?
• The authors depend on JavaScript to track information about the workers behaviors do you think MTurk needs to approve that or it is not necessary? And do you think that the workers also need to be notified before accepting the task?
• The authors mention that CrowdScape can be used to evaluate complex and creative tasks, do you think that they need to add some process to make sure that the task really need to be evaluated by their system, or you think the system can also work with simple tasks?

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Quality control in crowdwork is straightforward for straightforward tasks. Tasks like transcribing text on an image is fairly easy to evaluate the quality of because there is only one right answer. Requesters can use things like gold standard tests to evaluate the output of the crowdworkers directly in order to determine if they have done a good job, or use task fingerprinting to determine if the worker behavior indicates that they are making an effort. The authors propose CrowdScape as a way to combine both types of quality analysis, worker output and behavior, through a mix of machine learning and innovative visualization methods. CrowdScape includes a dashboard that provides a birds-eye view of the different aspects of the worker behavior in the form of graphs. These graphs showcase both aggregate behaviors of all the crowdworkers as well as the timeline of the individual actions a crowd worker takes on a particular task (scrolling, clicking, typing, and so on). They conduct multiple case studies on different kinds of tasks to show that their visualizations are beneficial in separating out the workers who make an effort in producing quality output versus those who are just phoning it in. Behavioral traces identify where the crowdworker spends their time by looking at their actions and how long they spend doing that action.

CrowdScape provides an interesting visual solution to the problem of “how to evaluate if the workers are being sincere in the completion of complex tasks”. Creative work especially, where you ask the crowd worker to write something on their own, is notoriously hard to determine because there is no gold standard test that you can do. So I find the inclusion of the behavior tracking visualizer where different colored lines along a timeline represent different actions done can be useful. Someone who makes an effort in typing out will show long blocks of typing with pauses for thinking. I can see how different behavioral heuristic can be applied for different tasks in order to determine if the workers are actually doing the work. I have to admit though that I find the scatter plots kind of obtuse and hard to parse. I’m not entirely sure how we’re supposed to read them and what information they are conveying. So I feel like the interface itself could do better in communicating exactly what the graphs are doing. There is promise for releasing this as a commercial or open source product (if it isn’t already one) once the polishing of the interface is done with. One last thing is the ability to group “good” submissions by the requester and then machine learning is used by CrowdScape to find other similar “good” submissions. However, the paper only makes mention of it and do not describe how it fits in with the interface as a whole. I felt this was another shortcoming of this design.

1. What would a good interface for the grouping of the “good” output and subsequent listing of other related “good” output look like?
2. In what kind of crowd work would CrowdScape not be useful (assuming you were able to get all the data that CrowdScape needs)?
3. Did you find all the elements of the interface intuitive and understandable? Were there parts of it that were hard to parse?

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The authors presented CrowdScape, which is a system used for supporting the human evaluation of increasing numbers of complex crowd work. The system used interactive visualization and mixed-initiative machine learning to combine information about worker behavior with the worker outputs. This system can help users to better understand the crowd workers and leverage their strength. They developed the system from three points to meet the requirement of quality control in crowdsourcing: output evaluation, behavioral traces, and integrated quality control. They visualized the workers’ behavior, quality of outputs and combined the findings of user behavior with user outputs to evaluate the work of the crowd workers. This system has some limitations, for example, it cannot work if the user completes the work in a separate text editor and the behavior traces are not detailed enough. However, this system is still good support for quality control.

Reflections:

How to evaluate the quality of the outputs made by the crowdsourcing workers? For those complex tasks, there is no single correct answer, and we can hardly evaluate the work of the workers. Previously, researchers proposed methods in which they traced the behavior of the workers and evaluated their work. However, this kind of method is still not accurate enough as workers may provide the same output while completing tasks in different ways. The authors provide us a novel approach that evaluates the workers from outputs, behavior traces and the combination of these two kinds of information. This combination increases the accuracy of their system and is able to do analysis on some of the complex tasks.

This system is valuable for crowdsourcing users. They can better understand the workers by building a mental model of them. As a result, they can distinguish good results from the poor ones. In projects related to crowdsourcing, developers will sometimes receive a poor response by inactive workers. With this system, they can only keep the valuable results for their research, which may increase the accuracy of their models, get a better view of their systems’ performance and get detailed feedback.

Also, for system designers, the visualization tool for behavioral traces is quite useful if they want to get detailed user feedback and user interactions. If they can analysis on these data, they can know what kinds of interactions are needed by their users and provide a better user experience.

However, I think there may be ethical issues in this system. Using this system, the hits publishers can obtain workers’ behavior while doing the hits. They can collect mouse movement, scrolling, keypresses, focus events and clicks information of the user. I think this may raise some privacy issues and these kinds of information may be used for crimes. The workers’ computers would be risky if their habits are collected by crackers.

Questions:

Can this system be applied to some more complex tasks other than purely generative tasks?

How can the designers use this system to design interfaces which can provide a better user experience?

How can we prevent crackers from using this system to collect user habits and do attacks on their computers?

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In this paper, the authors try to help Mturk requesters by providing them with an analysis tool called “Crowdscape.” Crowdscape is a ML + visualization tool for viewing and filtering Mturk worker submissions based on the workers’ behaviors. The user of the application can threshold based on certain behavioral attributes such as time spent or typing delay. The application takes in two inputs: Worker behavior and results. The behavior input is a timeseries data of user activity. The result is what the worker submitted for the Mturk work. The authors focused on finding similarities of the answers to graph on parallel coordinates. The authors conducted a user study by launching four different tasks and recording user behavior and result. The authors conclude that their approach is useful.

This paper’s approach of integrating user behavior and result to filter good output was interesting. Although, I think this system should overcome a problem for it to be effective. The problem lies in the ethics area. The authors explicitly stated that they obtained consent from their pool of workers to collect user behavior. However, some Mturk requesters may decide not to do so with some ill intentions. This may result in intrusion of private information and even end up to theft. On the other hand, upon obtaining consent from the Mturk worker, the worker becomes aware of him or her being monitored. This could also result in unnatural behavior which is undesired for system testing.

I thought the individual visualized graphs and figures were effective for better understanding and filtering by user behavior. However, the entire Crowdscape interface looked a bit overpacked with information. I think a small feature to show or hide some of the graphs would be desirable. The same problem existed with another information exploration system from a project that I’ve worked in. In my experience, an effective solution was to provide a set of menus that hierarchically sorted attributes.

These are the questions that I had while reading the paper:

1. A big purpose of Crowdscape is that it can be used to filter and retrieve a subset of the results (that are thought to be high quality results). What other ways could this system be used for? For example, I think this could be used for rejecting undesired results. Suppose you needed 1000 results and you launched 1000 HITs. You know you will get some ill-quality results. However, since there are too many submissions, it’ll take forever to filter by eye. Crowdscape would help accelerate the process.

2. Do you think you can use Crowdscape for your project? If so, how would you use it? Crowdscape is useful if you, the researcher, is the endpoint of the Mturk task (as in the result is ultimately used by you). My project uses the results from Mturk in a systematic way without ever reaching me, so I don’t think I’ll use Crowdscape.

3. Do you think the graphs available in Crowdscape is enough? What other features would you want? For one, I’d love to have a boxplot for the user behavior attributes.

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This work aims to address quality issues in the context of crowdsourcing and explores strategies to involve humans in the evaluation process via interactive visualizations and mixed-initiative machine learning. CrowdScape is a tool proposed that aims to ensure quality even in complex or creative settings. This aims to leverage both the end output as well as workers’ behavior patterns to develop insights about performance. CrowdScape is built on top of Mechanical Turk and obtains data from two sources: the MTurk API in order to obtain the products of work done and Rzeszotarski and Kittur’s Task Fingerprinting system in order to capture worker behavioral traces. The tool utilizes these two data sources and generates an interactive data visualization platform. With respect to worker behavior, raw event logs, and aggregate worker features are incorporated to provide diverse interactive visualizations. Four specific case studies were discussed and these included tasks relating to translation, color preference survey, writing, and video tagging. 

In the context of creative works and complex tasks where it is extremely difficult to evaluate the task results objectively, I feel that mixed-initiative approaches like the one described in the paper can be effective to gauge the worker’s performance.

I specifically liked the feature mentioned with respect to aggregating features of worker behavioral traces wherein the user is presented with capabilities to dynamically query the visualization system to support data analysis. This gives the user control over what features are important to them and allows users to focus on those specific behavioral traces as opposed to presenting the users with static visualizations which would have limited impact.

Another interesting feature provided by the system was that it enabled users to cluster submission based on aggregate event features and I feel that this would definitely help save time and effort from the user’s side and would thereby quicken the process.

In the translation case study presented, it was interesting to note that one of the factors that affected the study of lack of focus was tracking copy-paste keyboard usage. This would intuitively translate to the fact that the worker has used third-party software for translation. However, this alone might not be proof enough since it is possible that the worker translated the task locally and was copy-pasting his/her own work. This shows that while user behavior tracking can provide insights, it might not be sufficient to draw conclusions. Hence, coupling it with the output data and comparing and visualizing it would definitely help draw concrete conclusions.

• Apart from the techniques mentioned in the paper, what are some alternate techniques to gauge the quality of crowd workers in the context of complex or creative tasks?
• Apart from the case studies presented, what are some other domains where such systems can be developed and deployed?
• Given that the tool relies on worker’s behavior patterns and given that these may vary largely from worker to worker, are there situations in which the proposed tool would fail to produce reliable results with respect to performance and quality?

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Word count: 561

Summary of the Reading

This paper describes a system for dealing with crowdsourced work that needs to be evaluated by humans. For complex or especially creative tasks, it can be hard to evaluate the work of crowd workers, because there is some much of it and most of it needs to be evaluated by another human. If the evaluation takes too long, you lose the benefits of using crowd workers in the first place. 

To help with these issues, the researchers have developed a system that helps an evaluator deal with all of the data from the tasks. The system leans heavily on the data visualization. The interface for the system shows the user a slathering of different metrics about the crowd work and the workers to help the user determine quality. Specifically, the system helps the user to see information about worker output and behavior at the same time, giving a better indication of performance.

Reflections and Connections

I think that this paper tries to tackle a very important issue of crowd work: evaluation. Evaluation of tasks is not an easy process and for complicated tasks, it can be extremely difficult and, worst of all, hard to automate. If you need humans to review and evaluate work done by crowd workers, and it takes the reviewer a non-insignificant amount of time, then you are not really saving any effort by using the crowd in the first place. 

This paper is so important because it provides a way to make it easier for people to evaluate work done by crowd workers, making the use of crowd workers much more efficient, on the whole. If evaluation can be done more quickly, the data from the tasks can be used more quickly, and the whole process of using crowd workers has been made much faster than it was before. 

I also think this paper is important because it gives reviewers a new way to look at the work done by crowds: it shows the reviewer both worker output and worker behavior. This would make it much easier for reviewers to decide if a task was completed satisfactorily or not. If we can see that a worker did not spend a lot of time on a task and that their work was significantly different from other workers assigned to the same task, we may be able to tell that that worker did a bad job, and their data should be thrown out.

From the pictures of the system, it does look a little complicated and I would be concerned that the system is hard to use or overly complicated. Having a system that saves time, but that takes a long time to fully understand can be just as bad as not having the time saving system. So, I do think that some effort should be used to make the system look less intimidating and easier to use. 

Questions

1. What are some other possible applications for this type of software, besides the extra  one mentioned in the paper?
2. Do you think there is any way we could fully automate the evaluation of the creative and complex tasks focused on in this research?
3. Do you think that the large amount of information given to users of the system might overwhelm them?

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