Summary

Ngyuen et al’s paper discusses the rise of misinformation and the need to combat it via tools that can verify claims while also maintaining users’ trust of the tool. They designed an algorithm that finds sources that are similar to a given claim to determine whether or not the claim is accurate. They also weight the sources based on esteem. They then ran 3 studies (with over 100 participants in each) where users could interact with the tool and change settings (such as source weighting) in order to evaluate their design. The first study found that the participants trusted the system too much – when it was wrong, they tended to be inaccurate, and when it was right, they were more typically correct. The second study allowed participants to change the inputs and inject their own expertise into the scenario. This study found that the sliders did not significantly impact performance. The third study focused on gamification of the interface, and found no significant difference.

Personal Reflection

               I enjoyed this paper from a 50,000 foot perspective, as they tested many different interaction types and found what could be considered negative results. I think papers that show that all work is not necessarily good have a certain amount of extra relevance – they certainly show that there’s more at work than just novelty.

I especially appreciated the study on the effectiveness of gamification. Often, the prevailing theory is that gamification increases user engagement and increases the tools’ effectiveness. While the paper is not conclusive that gamification cannot do this, it certainly lends credence to the thought that gamification is not a cure-all.

However, I took some slight issue with their AI design. Particularly, the AI determined that the phrase “Tiger Woods” indicated a supportive position. While their stance was that AIs are flawed (true), I felt that this error was quite a bit more than we can expect from normal AIs, especially ones that are being tweaked to avoid these scenarios. I would have liked to see experiment 2 and 3 improved with a better AI, as it does not seem like they cross-compared studies anyway.

Questions

1. Does the interface design including a slider to adjust source reputations and user agreement on the fly seem like a good idea? Why or why not?
2.  What do you think about the attention check and its apparent failure to accurately check? Should they have removed the participants with incorrect answers to this check?
3. Should the study have included a pre-test to determine how the participants’ world view may have affected the likelihood of them agreeing with certain claims? I.E., should they have checked to see if the participants were impartial, or tended to agree with a certain world view? Why or why not?
4. What benefit do you think the third study brought to the paper? Was gamification proved to be ineffectual, or is it a design tool that sometimes doesn’t work?

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Authors: An T. Nguyen, Aditya Kharosekar , Saumyaa Krishnan, Siddhesh Krishnan, Elizabeth Tate, Byron C. Wallace, and Matthew Lease

Summary

This paper proposes a mixed-initiative approach to fact-checking, combining human and machine intelligence. The system automatically finds and retrieves relevant articles from a variety of sources. It then infers the degree to which each article supports or refutes the claim, as well as the reputation of each source. Finally, the system aggregates this body of evidence to predict the veracity of the claim. Users can adjust the source reputation and stance of each retrieved article to reflect their own beliefs and/or correct any errors according to them. This will, in turn, update the AI model. The paper evaluates this approach through a user study on Mechanical Turk. 

Reflection

This paper, in my opinion, succeeds as a nice implementation of all the design ideas we have been discussing in the class for mixed-initiative systems. It factors in user input, combined with an AI model output, and shows users a layer of transparency in terms of how the AI makes the decision. However, fact-checking, as a topic, is complex enough not to warrant a solution in the form of a simplistic single-user prototype. So, I view this paper as opening up doors for building future mixed-initiative systems that can rely on similar design principles, but also factor in the complexities of fact-checking (which may require multiple opinions, user-user collaboration, etc).

Therefore, for me, this paper contributes an interesting concept in the form of a mixed-initiative prototype, but beyond that, I think the paper falls short of making it clear who the intended users are (end-users or journalists) or the intended scenario it is designed for. The evaluation with Turkers seemed to indicate that anyone can use it, which opens up the possibility of creating individual echo-chambers very easily and essentially, making the current news consumption landscape worse. 

The results also showed the possibility of AI biasing users when it’s wrong, and therefore, a future design would have to factor in that. One of the users felt overwhelmed as there was a lot going on with the interface, and therefore, a future system needs to address the issue of information overdose. 

The authors, however, did a great job discussing these points in detail about the potential misuse and some of the limitations. Going forward, I would love to see this work forming the basis for a more complex socio-technical system, that allows for nuanced inputs from multiple users, interaction with a fact-checking AI model that can improve over time, and a longitudinal evaluation with journalists and end-users on actual dynamic data. The paper, despite the flaws arising due to the topic, succeeds in demonstrating human-AI interaction design principles.

Questions

1. What are some of the positive takeaways from the paper?
2. Did you feel that fact-checking, as a topic, was addressed in a very simple manner, and deserves more complex approaches?
3. How would you build a future system on top of this approach?
4. Can a similar idea be extended for social media posts (instead of news articles)? How would this work (or not work)?

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Summary:

This article discusses a system about fact detection. First of all, the article proposes that fact detection is a very important, challenging and time-sensitive. Usually, in this type of system, the human influence on the system is ignored, but the human influence is very important in this type of system. Therefore, this article establishes a hybrid startup system for fact checking. Enable users to interact with ML predictions to complete challenging fact checks. The author designed the interface through which the user can know the source of the prediction. In some applications, when the users know the prediction results but is not satisfied, the author also allows the user to use his own beliefs or inferences to cover these predictions. Through this system, the authors have come to a conclusion that when the model’s results are correct, these predictions will have a very positive impact on people. However, people should not overly trust the model’s predictions, when users think the predictions is wrong, the prediction result can be improved through interactive methods. And this also reflects the importance of a transparent, interactive system for fact detection from the side.

Reflection:

When I saw the title of this article, I thought that this article maybe have a same topic with my project, using crowdsourced workers to distinguish fake news, but when I read it to a certain extent, I found that this is not the case. But I think it affirmed my thinking in some aspects. First, fact detection is a very challenging project, especially when real-time is needed, so it is very necessary to rely on human power, and due to lack of Marked data, so if you want to directly complete the task through machine learning, in some cases, the prediction results will point in a completely opposite direction. For example, in my project, rumors and refuting rumors are both may be considered as a rumor, so we need crowd workers to distinguish it.

Secondly, for the project itself mentioned in the article, I think its method is a very good direction. First of all, human judgment is particularly important in this kind of system. This is also the main idea of many human-computer interaction systems to improve accuracy through humans. I think this method in the article is a good start. In a transparent system, let people decide whether to cover the forecast results. Not only do they not force people to participate in the system, but also let people make predictions There are very important weights.

But at the same time, I think the system also has some of the limitations described in the article. For example, the purpose of crowdsourcing workers and its own concerns may affect the results of the final system, so I think the article proposes a good direction, but we need to be more Careful research.

Problem:

1. Do you think users usually can find the prediction is incorrect and cover it when a system is wrong?
2. What role does the transparency of the system play in the interaction?
3. How to prevent users trust the prediction too far in other human and computer interaction systems?

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Summary

The paper emphasizes the importance of a mixed-initiative model for fact-checking. It points out the advantages of humans and machines working closely together to verify the veracity of the facts. The paper’s main aim from the mixed-initiative approach was to make the system, especially the user interface, more transparent. The UI presents a claim to the user along with a list of articles related to the statement. The paper also mentions all the prediction models that have been used to create the UI experience. Finally, the authors conducted three experiments using crowd workers who had to predict the correctness of claims presented to them. In the first experiment, the users were shown the results page without the prediction of the truthfulness of the claim. Users were subsequently divided into two subgroups, where one group was given slightly more information. In the second experiment, the crowdworkers were presented with interactive UI. They, too, were further divided into two subgroups, with one group having the power to change the initial predictions. The third experiment was a gamified version of the previous experiment. The authors concluded that human-ai collaboration could be useful, although the experiment brought into light some contradictory findings. 

Reflection

I agree with the author’s approach that the transparency of a system leads to the confidence of the user using a particular system. My favorite thing about the paper is that the authors describe the systems very well. They do a very good job of describing the AI models as well as the UI design and give a good explanation to their decisions. I also enjoyed reading about the experiments that they conducted with the crowdworkers. I had a slight doubt about how the project handled latency, especially when the related articles were presented to the workers in real-time.

I also liked how the experiments were conducted in sub-groups, with a group having information not presented to the other. This shows that a lot of use cases were thought of when the experimentation took place. I agree with most of the limitations that the authors wrote. I particularly agree that if the veracity of predictions is shown to the users, there is a high chance of that influencing people. We, as humans, have a tendency to believe machines and its prediction blindly. 

I would also want to see the work being performed on another dataset. Additionally, if the crowdworkers have knowledge about the domain in the discussion, how does that affect the performance? It is definite that having knowledge would improve detecting the claim of a statement. Nonetheless, this might help in determining to what extent. A potential use case could be researchers reading claims from research papers in their domain and assessing their correctness. 

Questions

1. How would you implement such systems in your course project?
2. Can you think of other applications of such systems?
3. Is there any latency associated when the user is produced with the associated articles? 
4. How would the veracity claim system extend to other domains (not news based)? How would it perform on other datasets? 
5. Would experience (in one domain) crowdworkers perform better? The answer is likely yes, but how much? And how can this help improve targeted systems (research paper acceptance, etc.)?

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In this paper, the authors state that the current fully automatic fact-checking systems are not good enough for three reasons: model transparency, taking world facts into consideration, and model uncertainty communication. So, the authors went on and built a system including humans in the loop. Their proposed system uses two classifiers that each predict the reliability of a supporting document of a claim and the veracity of the document. Using these weighted classifications, the confidence of the system’s prediction about a claim is shown to the user. The users can further manipulate the system by modifying the weights of the system. The authors conducted a user study of their system with Mturk workers. The authors found that their approach was effective, but also noted that too much information or misleading predictions can lead to big user errors.

First off, it was hilarious that the authors cited Wikipedia to introduce Information Literacy in a paper about evaluating information. I personally took it as a subtle joke left by the authors. However, it also led me to a question about the system. If I did not miss it, the authors did not explain where the relevant sources or articles came from that supported a claim. I was a little concerned if some of the articles used in the study were not reliable sources.

Also, the users conducted the user study using their own defined set of claims. While I understand this was needed for efficient study, I wanted to know how the system would work in the wild. If a user searched a claim that he or she knows is true, would the system agree with high confidence? If not, would the user have been able to correct the system using their interface? It seemed that some portion of the users were confused, especially with the error correction part of the system. I think these would have been valuable to know and would seriously need to be addressed if the system were to become a commercial product.

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

1. How much user intervention do you think is enough for these kinds of systems? I personally think if the users are given too much power over the system, users will apply their bias to the correction and get false positives.

2. What would be a good way for the system to only retrieve ‘reliable’ sources to reference? Stating that a claim is true based on a Wikipedia article would obviously not be so assuring. Also, academic papers cannot address all claims, especially if they are social claims. What would be a good threshold? How could this be detected?

3. Given the current system, would you believe the results that the system gives? Do you think the system addresses the three requirements that the authors introduced which all fact-checking systems should possess? I personally think that system transparency is still lacking. The system shows a lot about what kind of sources it used and how much weight it’s putting into them, but it does not really explain how it made the decision.

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The authors focused on fact-checking, which is the task of assessing the veracity of claims. They proposed a mixed-initiative approach to fact-checking. In this system, they combined human knowledge and experience with AI’s efficiency and scalability in information retrieval. They argue that if we want to use fact-checking models practically, the models should be transparent, supporting to integrating user knowledge and have quantification and communication of model uncertainty. Following these principles, they developed their mixed-initiative system and did experiments with participants from MTurk. They found that the system can help humans when they are giving correct predictions and could be harmful when they are giving wrong predictions. And the interaction between participants and the system is not as effective as expected. Finally, they found that making tasks to be games does not help in users’ performance. In conclusion, they found that users are intended to trust models, and may be affected by the models to make the wrong choice. For this reason, transparent models are important in mixed-initiative systems.

Reflection:

I have tried to use the system mentioned in the paper. It is quite interesting. However, for the first time, I used it, I am confused about what should I do to use it. Though the interface is similar to Google.com and I am quite sure I should type something into the text box, there are limited instructions about what should I type, how the system will work and what should I do after searching my typed claim. Also, after I searched for a claim, the result page is still confusing. I know the developers want to show me some findings of the claim and provide me with the prediction result of the system. However, I am still confused about what should I do, and some given searching results are not related to my typed claim.

After several times of use, I got familiar with the system and it does help in my judgement of whether a claim is correct or not. I agree with the authors that some feedbacks about not being able to interact with the system properly comes from the users’ unfamiliar of the system. But apart from this, the authors should provide more instructions to the users so that they can get familiar with the system quickly. I think this is related to the transparency of the system and may raise users’ trust.

Another issue I found during use is that there are no words like the results can only be used as a reference, you should make a judgement using your own mind, or similar explanations. I think this may be a reason that the error rate of users’ results increased significantly when the system made wrong predictions. Participants may change their own minds when they saw that the prediction result is different from their own results because they think know little about the system and may think that system would be more likely to get the correct answer. If the system is more transparent to the users, the users may be able to provide more correct answers to the claims.

Questions:

How to let the participants make correct judgements when the system provides wrong predictions?

What kinds of instructions should be added so that participants can get familiar with the system more quickly?

Can this system be used in areas other than fact-checking?

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