Summary

Rzeszotarski and Kittur’s paper “CrowdScape: Interactively Visualizing User Behavior and Output” explores the research question of how to unify different quality control approaches to enhance the quality control of the work conducted by crowd workers. With the emerging crowdsourcing platforms, many tasks can be accomplished quickly by recruiting crowd workers to collaborate in a parallel manner. However, quality control is among the challenges faced by crowdsourcing paradigm. Previous works focus on designing algorithmic quality control approaches based on either worker outputs or worker behavior, but neither of the approaches is effective for complex or creative tasks. To fill in that research gap, the authors develop CrowdScape, a system that leverages interactive visualization and mixed initiative machine learning to support the human evaluation of complex crowd work. With experimentation on a variety of tasks, the authors present the incorporation of information about worker outputs and worker behavior with worker outputs has the potential to assist users to better understand the crowd and further identify reliable workers and outputs. 

Reflection

This paper conducts an interesting study by exploring the relationship between the outputs and behavior patterns of crowd workers to achieve better quality control in complex and creative tasks. The proposed method provides a unique angle of quality control and it has wide potential usage in crowdsourced tasks. Although there is a strong relationship between the final outputs and the behavior patterns, I feel the design of CrowdScape relies too heavily on the behavior analysis of crowd workers. In many situations, good behavior can lead to good results, but that cannot be guaranteed. From my understanding, behavior analysis is more suitable to be utilized as a screening mechanism in certain tasks. For example, in the video tagging task, workers that have gone through the whole video are more possible to provide accurate tagging. In this case, behavior such as watching is more like a necessary condition, not a sufficient condition. The group of workers who finish watching the videos may still reach disagreement on the tagging output. A different quality control mechanism is still needed in this round. In creative and open tasks, the behavior patterns are even more difficult to capture. By analyzing the behavior by metrics such as time spent on the task, we cannot directly connect the analysis with the measurement of creativity. 

In addition, I think the quality of a crowdsourced task discussed in paper is comparatively narrow. We need to be aware the quality control on crowdsourcing platforms is multifaceted, which depends on the knowledge of the workers on the specific task, the quality of the processes that govern the creation of tasks, the recruiting process of workers, the coordination of subtasks such as reviewing intermediate outputs, aggregating individual contributions, and so forth. A more comprehensive quality control circle needs to take the following aspects into consideration: (1) quality model that clearly defines the dimensions and attributes to control quality in crowdsourcing tasks. (2) assessment metrics which can be utilized to measure the values of the attributes identified by the quality model (3) assurance of quality, which requires a set of actions that aim to achieve expected levels of quality. To prevent low quality, it is important to understand how to design for quality and how to intervene if quality drops below expectations on crowdsourcing platforms.

Discussion

I think the following questions are worthy of further discussion.

• Can you think about some other tasks that may benefit from the proposed quality control method?
• Do you think the proposed method can perform good quality control on the complex and creative tasks as the paper suggested? Why or why not?
• Do you think that an analysis based on worker behavior can assist to determine the quality of the work conducted by crowd workers? Why or why not?
• What scenarios do you think would be more useful to trace worker behavior, informing them beforehand or tracing without advance notice? Can you think about some potential ethical issues?

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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

1. How are you ensuring the quality of the work completed by crowdworkers for your course project?
2. Were the workers informed that their behavior was “watched”? Would the behavior and, subsequently, the performance change if they are aware of the situation?
3. Workers might use different playback speeds to watch videos. How is that situation handled here?

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SUMMARY

There are multiple challenges that exist while ensuring quality control of crowdworkers that are not always easily resolved by employing simple methods such as the use of gold standards or worker agreement. Thus, the authors of this paper propose a new technique to ensure quality control in crowdsourcing for more complex tasks. By utilizing features from worker behavioral traces as well as worker outputs, they aid researchers to better understand the crowd. As part of this research, the authors propose novel visualizations to illustrate user behavior, new techniques to explore crowdworker products, tools to group as well as classify workers, and mixed initiative machine learning models that build on a user’s intuition about the crowd. They created CrowdScape – built on top of MTurk which captures data from the MTurk API as well as a Task Fingerprinting system in order to obtain worker behavioral traces. The authors discuss various case studies such as translation, picking a favorite color, writing about a favorite place, and tagging a video and describe the benefits of CrowdScape in each case.

REFLECTION

I found that CrowdScape is a very good system especially considering the difficulty in ensuring quality control among crowdworkers in case of more complex tasks. For example, in case of a summarization task, particularly for larger documents, there is no single gold standard that can be used and it would be rare that the answers of multiple workers would match for us to use majority vote as a quality control strategy. Thus, for applications  such as this, I think it is very good that the authors proposed a methodology that combines both behavioral traces as well as worker output and I agree that it provides more insight that using either alone. I found that the example of the requester intending to have summaries written for YouTube physics tutorials was an appropriate example.

I also liked the visualization design that the authors proposed. They aimed to combine multiple views and made the interface easy for requesters to use. I especially found the use of 1-D and 2-D matrix scatter plots showing distribution of features over the group of workers that also enabled dynamic exploration to be well thought out.

I found the case study on translation to be especially well thought out – given that the authors structured the study such that they included a sentence that did not parse well in computer generated translations. I feel that such a strategy can be used in multiple translation related activities in order to more easily discard submissions by lazy workers. I also liked the case study on ‘Writing about a Favorite Place’ as it indicated the performance of the CrowdScape system in a situation wherein no two workers would provide the same response and traditional quality control techniques would not be applicable.

QUESTIONS

1. The CrowdScape system was built on top of Mechanical Turk. How well does it extend to other crowdsourcing platforms? Is there any difference in the performance?
2. The authors mention that workers who may possibly work on their task in a separate text editor and paste the text in the end would have little trace information. Considering that this is a drawback of the system, what is the best way to overcome this limitation?
3. The authors the case study on ‘Translation’ to demonstrate the power of CrowdScape to identify outliers. Could an anomaly detection machine learning model be trained to identify such outliers and aid the researchers better?

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Summary

The paper presents a system supporting the evaluation of complex crowd work through mixed-initiative machine learning and interactive visualization. This system proposes a solution for challenges in quality control that occur in crowdsourcing platforms. Previous work shows quality control concepts based on worker output or behavior which was not effective in evaluating complex tasks. Their suggested system combines the behavior and output of a worker to show the evaluation of complex crowd work. Their system features allow users to develop hypotheses about their crowd, test them, and refine selections based on machine learning and visual feedback. They use MTurk and Rzeszotarski and Kittur’s Task Fingerprinting system to create an interactive data visualization of the crowd workers. They posted four varieties of complex tasks: translating text from Japanese to English, picking a favorite color using an HSV color picker and writing its name, writing about their favorite place, and tagging science tutorial videos from Youtube. They conclude that the information gathered from crowd workers behavior is beneficial in reinforcing or contradicting the conception of the cognitive process that crowd workers use to complete tasks and in developing and testing mental models of the behavior of crowd workers who have good or bad outputs. This model helps  its users identify further good workers and output in a sort of positive feedback loop.

Reflection

This paper presents an interesting approach in addressing how to discover low quality responses from crowd workers. It is an interesting way to combine these two methods and makes me think of our project and what limitations might arise from following their method in logging behaviors of crowd workers. I was not thinking of disclosing to the crowdworkers about their behavior while responding is being recorded and now it’s making me look at previous work if that has affected the crowd workers response or not. I found it interesting that crowdworkers used machine translation in the Japanese to English translation task even when they knew their behavior was being recorded. I assume that since there wasn’t a requirement of speaking Japanese or the requirements were relaxed that crowd workers were able to perform the task and use tools like Google Translate. If the requirements were there, then the workers won’t be paid for the task. This has also alerted me to the importance of task requirements and explanation for crowd workers. Since some Turkers could abuse the system and give us low quality results simply because the rules weren’t so clear.

Having the authors list their limitations was useful for me. It gave me another perspective to think about how to evaluate the responses that we get in our project and what we can do to make our feedback approach better.

Discussion

• Would you use behavioral traces as an element in your project? If yes, would you tell the crowd workers that you are collecting that data? Why? Why not?
• Do you think that implicit feedback and behavioral traces can help determine the quality of a crowd worker’s answer? Why? Or why not?
• Do you think that collecting such feedback is a privacy issue? Why? Or Why not?

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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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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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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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