Paper: Jeffrey Rzeszotarski and Aniket Kittur. 2012. CrowdScape: interactively visualizing user behavior and output. In Proceedings of the 25th annual ACM symposium on User interface software and technology (UIST ’12), 55–62. https://doi.org/10.1145/2380116.2380125

Summary:

Crowdsourcing has been used to do intelligent tasks/knowledge work at scale and for a lower price, all online. However, there are many challenges with controlling quality in crowdsourcing. This paper talks about how in prior approaches, quality control was done through algorithms evaluated against gold standard or looking at worker agreement and behavior. Yet, these approaches have many limitations, especially for creative tasks or other tasks that are highly complex in nature. This paper presents a system, called CrowdScape, to support manual or human evaluation of complex crowdsourcing task results through a visualization that is interactive and has a mixed initiative machine learning back-end. The paper describes features of the system as well as its uses through 4 very different case studies. First, a translation task from Japanese to English. The next one was a little unique, asking workers to pick their favorite color. The third was about writing about their favorite place, and finally the last one was tagging a video. Finally, the paper concludes with a discussion of the findings.

Reflection:

Overall, I really liked the paper and the CrowdScape system, and I found the multiple case studies really interesting. I especially liked the fact that the case studies varied in terms of complexity, creativity, and open-endedness. However, I found the color-picker task a little off-beat and wonder why the authors chose that task. 

I also appreciate that the system is built on top of existing work, e.g. Amazon Mechanical Turk (a necessity), as well as Rzeszotarski and Kittur’s Task Fingerprinting system to capture worker behavioral  traces. The scenario describing the more general use case was also very clear and concise. The fact that the system, CrowdScape, also utilizes two diverse data sources—as opposed to just one—is interesting. This makes triangulating the findings more easy, as well as observing and discrepancies in the data. More specifically, the CrowdScape system looks at worker’s behavioral traces as well as their output. This allows one to differentiate between workers in terms of their “laziness/eagerness” as well as the actual quality of the output. The system also provides an aggregation of the two features, and all of these are displayed as visualizations which makes it easy for a requester to view tasks and easily discard/include work.

However, I wonder how useful these visualizations might be for tasks such as surveys, or tasks that are less open-ended. Further, although the visualizations are useful, I wonder if they should be used in conjunction with gold standard datasets or not, and how useful that combination would be. Although the paper demonstrates the potential uses of the system via case studies, it does not demonstrate whether real users say it is useful. Thus, an evaluation by real-world users might help.

Questions:

1. What do you think about the case study evaluation? Are there ways to improve it? How?
2. What features of the system would you use as a requester?
3. What are some drawbacks to the system?

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Authors: Jeffrey M Rzeszotarski, Aniket Kittur

Summary

This paper proposes a system CrowdScape, that supports human evaluation of crowd work through interactive visualization of behavioral traces and worker output, combined with mixed-initiative machine learning. Different case studies are discussed to showcase the utility of CrowdScape.

Reflection

The paper addresses the issue of quality control, a long-standing problem in crowdsourcing, by combining two existing standalone approaches that researchers currently adopt: a) inference from worker behavior and b) analyzing worker output. Combining these factors is advantageous as it provides the complete picture, either by providing corroborating evidence towards ideal workers, or in some cases, may provide complementary evidence that can help infer ideal “good” workers. Just analyzing the worker output might not be enough as there’s an underlying chance that it might be as good as a random coin toss. 

Even though it was a short text in parentheses, I really liked the fact that the authors explicitly sought permission to record the worker interaction logs. 

Extrapolating other similar or dissimilar behavior using Machine Learning seems intuitive here as the data and the features used (i.e. the building blocks) of the model are very meaningful, perfectly relevant to the task and not a black-box model. As a result, it’s not surprising to see it work almost everywhere. The one case where it didn’t work, it made up for it by showing that the complementary case works. This sets a great example for designing predictive models on top of behavioral traces that actually works. 

Moreover, the whole system was built agnostic of the task, and the evaluations justified it. However, I am not sure if the best use case of the system is optimized towards recruiting multiple workers for a single task, or whether it is to identify a set of good workers to subsequently retain for other tasks in the pipeline. I am guessing it is the latter, as the former might seem like an expensive approach for getting high-quality responses. 

On the other hand, I feel the implications of this paper go beyond just crowdsourcing quality control. CrowdScape, or a similar system, can provide assistance for studying user behavior/experience in any interface (web for now), which is important for evaluating interfaces.

Questions

1. Does your evaluation include collecting behavioral trace logs? If so, what are some of your hypotheses regarding user behavior?
2. How do you plan on assessing quality control?
3. What kind of tasks do you see CrowdScape being best applicable for? (e.g. single task, multiple workers)

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Summary:
“CrowdScape: Interactively Visualizing User Behavior and Output” by Rzeszotarski and Kittur talks about crowdsourcing and the importance of interactive visualization using the complementary strengths and weaknesses of crowd workers and machine intelligence. Crowd sourcing helps work distribution. Quality control approaches for this are often not scalable. Crowd organizing algorithms like Partition-Map-Reduce, Find-Fix-Verify, and Price-Divide-Solve are used for easy distribution, merging and checking the work in crowd sourcing. However, they aren’t very accurate or useful in complex subjective tasks. CrowdScape assimilates worker behavior with worker input using interaction, visualization, and machine learning. This supports the human evaluation of crowd work. CrowdScape enables the user to hypothesize about and test the crowd to distill the selections by using a sensemaking loop. This paper proposes novel techniques for crowd worker’s product exploration and visualizations for crowd worker behavior. It also provides tools for classification or crowd workers and an interface for interactive exploration of these results using mixed-method machine learning.

Reflections:
There has been work done involving crowd behaviour centered on worker behaviour or worker output in isolation but combining them is very fruitful to generate mental models of the workers and build a feedback loop. Visualisation of the workers’ process helps us understand their cognitive process and thus perceive the end product better. CrowdScape can only be used in webpages online that allow the injection of JavaScript. It is not useful when this is blocked or for non-web offline interfaces. The set of aggregate features used might not always provide useful feedback. The already existing quality control measures are not very different from CrowdScape in case of clear, consensus ground truth exists, such as identifying a spelling error. In such cases, the effort put in learning and using CrowdSpace may not always be beneficial and hence may not be too advantageous. In some cases, the behavioral traces of the worker may not be very indicative. Such as when they work on a different editor and finally copy and paste the work in another one. Tasks that are heavily cognitive or totally offline are also not very compliant with the general methods supported by CrowdScape. This system heavily relies on the detailed level of behavioral traces such as mouse movement, scrolling, keypresses, focus events, and clicks. It should be ensured that this intrusiveness and the implied decrease in efficiency should be countered by the accuracy of the measurement of the behavior. An interesting point to note here is that this tool can become privacy-intrusive if care is not taken. We should ensure that changes are made to the tool as crowd work becomes increasingly relevant and the tool becomes vital to better understand the underlying data and crowd behaviour. Apart from these reflections, I would just like to point out that the graphs that the authors use in the paper help in conveying their results really well. I feel this is one detail that is vital but easily overlooked in most papers.

Questions:
1. What are your general thoughts about this paper?
2. Do you agree with the methodology followed?
3. Do you approve of the interface? Would you make any changes to the interface?

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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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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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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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