SUMMARY

The authors of this paper design and evaluate a mixed-initiative fact-checking approach that blends prior human knowledge with the efficiency of automated ML systems. The authors found that users tend to over-trust the model which could degrade human accuracy. They conducted three randomized experiments – the first, compares user who perform the task with or without viewing ML predictions, the second, compares a static interface with an interactive one (that enables users to fix model predictions), and the third, compares a gamifies task design to a non-gamified one. The authors designed an interface that displays the claim, the predicted correctness, and relevant articles. For the first experiment, the authors considered responses from 113 participants with 58 assigned to Control and 55 to System. For the second experiment, the authors considered responses from 109 participants with 51 assigned to Control and 58 to Slider. For the third experiment, the authors considered responses from 106 participants, and found no significant differences between the two groups.

REFLECTION

I liked the idea of a mixed-initiative approach to fact checking that builds on the affordances of both humans and AI. I found that it was good that the authors designed the experiments such that the confidence scores (and therefore the fallibility) of the system was openly shown to the users. I also felt that the interface design was concise and appropriate without being overly complex. I also liked the design of the gamified approach and was surprised to learn that the game design did not impact participant performance.

I agree that for this case in particular, participant demographics may affect the results. Especially since the news articles considered were mainly related to American news. I wonder how much if a difference in the results would be observed in a follow-up study that considers different demographics as compared to this study. I also agree that caution must be exercised with such mixed-initiative systems as imperfect data sets would have a considerable impact on model predictions and that the humans should not blindly trust the AI predictions). It would definitely be interesting to see the results obtained when users check their own claims and interact with other user’s predictions.

QUESTIONS

1. The authors explain that the incorrect statement on Tiger Woods was due to the model having learnt the bi-gram ‘Tiger Woods’ incorrectly – something that a more sophisticated classifier may have avoided. How much of an impact would such a classifier have made on the results obtained overall? Have other complementary studies been conducted?
2. The authors found that a smaller percentage of users used the sliders than expected. They state that while the sliders were intended to be intuitive, they may require a learning curve causing lesser users to adopt it. Would the use of a tutorial that enabled users to familiarize themselves have helped in this case?
3. Were the experiments conducted in this study adequate? Are there any other experiments that the authors should have conducted in addition to the ones mentioned?

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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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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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Summary

In this paper, the authors propose a mixed initiative approach for fact checking that combine both human knowledge and experience with the automated information retrieval and machine learning. The paper discusses the challenges of the massive amount of information available today on the internet that some of them might not be accurate which introduce a risk to the information consumers. The proposed system retrieve relevant information about a certain topic and use machine learning and natural language processing to assess the factuality of the information and provide a confidence level to the users and let the user decide wither to use the information or do a manual research to validate the claims. The partnership between the artificial intelligence system and human interaction can offer effective fact checking that can support the human decision in a salable and effective way.

Reflection

I found the approach used by the authors to be very interesting. I personally had a discussion with a friend recently about a certain topic that was mentioned in Wikipedia, and I thought the numbers and facts mentioned were accurate but it turns out the information were wrong and he asked me to check an accreditable source. If I had the opportunity to use the proposed system on the paper, then accredited source could have ranked higher than Wikipedia.

The proposed system is very important in our digital age where so much information is generated on a daily bias and we are not only searching for information, but we are also receiving so much information through social media related to current events and some of these events have high impact on our life and we need to assess the factuality of these information and the proposed system can help a lot on that.

The proposed system is like a search engine that not only rank document based on relevance to the search query but also based on the fact-checking assessment of the information. The human interaction is like the relevance feedback in search engine which can improve the retrieval of the information which leads to a better ranking.

Questions

• The AI systems can be biased because the training data can be biased. How can we make the system unbiased?
• The proposed system use information retrieval to retrieve relevant articles about a certain topic and then use the machine learning to validate the source of the information and then present the confidence level of each article. Do you think the system should filter out articles with poor accuracy as they might confuse the user? Or they might be very valuable?

• With the increase usage of social networking, many individuals write or share fake news intentionally or unintentionally. Millions of people post information every day. Can we use the proposed system to assess the fake news? if yes, then can we scale the system to assess millions or billions of tweets or posts?

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In today’s era of fake news where new information is constantly spawning everywhere, the great importance of fact checking cannot be understated. The public has a right to remain informed and be able to obtain true information from accurate, reputable sources. But all too often, people are inundated with too much information and the cognitive load of fact checking everything would be too much. Automated fact checking has made strides but previous work has focused primarily on model accuracy and not on the people who need to use them. This paper is the first to study an interface for humans to use a fact checking tool. The tool is pretrained on the Emergent dataset of annotated articles and sources and uses two models, one that predicts article stance on a claim and the other that calculates the accuracy of the claim based on the reputation of the sources. The application works by taking a claim and retrieving articles that talk about the claim. It uses the article stance model to classify if the articles are for or against the given claim, and then predicts the claim’s accuracy based on the collective reputation of its sources. It conveys that its models are not accurate and provides confidence levels for its accuracy claims. It also provides sliders for the human verifiers to adjust the predicted stance of the articles and also to adjust the source reputation according to their beliefs or new information. The authors run three experiments to test the efficacy of the tool for human fact checkers. They find that the users tend to trust the system, which can be problematic when the system is inaccurate.

I find it interesting that for the first experiment, the System group’s error rate somewhat follows the stance classifiers error rate. The crowd workers are probably not going through the process of independently verifying the stance of the articles and simply trust the predicted stance they are shown. Potentially this could be mitigated by adding incentives (like extra reward) to have them actually read the articles in full. But on the flip side, we can see that their accuracy (supposedly) becomes better when they are given the sliders to modify the stances and reputation. Maybe that interactivity was the clue they needed to understand that the predicted values aren’t set in stone and could potentially be inaccurate. Though I find it strange that the Slider group in the second experiment did not adjust the sliders if they were questioning the sources. What I find even stranger though is the fact that the authors decided to keep the claim that allowing the users to use the sliders made them more accurate. This claim is what most readers would take away unless they were carefully reading the experiments and the riders. And I don’t like that they kept the second experiment results despite them not showing any useful signal. Ultimately, I don’t buy into their push that this tool is something that is useful for the general user as it stands now. And I also don’t really see how this tool could serve as a technological mediator for people with opposing views, at least not the way they described it. I find that this could serve as a useful automation tool for expert fact checkers as part of their work but not for the ordinary user, which is what they model by using crowdworkers. I like the ideas that the paper is going for, of having automated fact checking that helps for the ordinary user and I’m glad they acknowledge the drawbacks. But I think there are too many drawbacks that prevent me from fully buying into the claims of this paper. It’s poetic that I have my doubts about the claims of a paper describing a system that asks you to question claims.

1. Do you think this tool would actually be useful in the hands of an ordinary user? Or would it serve better in the hands of an expert fact checker?
2. What would you like to see added to the interface, in addition to what they already have?
3. This is a larger question, but is there value in having the transparency of the machine learning models in the way they have done (by having sliders that we can manipulate to see the final value change)? How much detail is too much? What about for more complex models where you can’t have that instantaneous feedback (like style transfer) how do you provide explainability there?
4. Do you find the experiments rigorous enough and conclusions significant enough to back up the claims they are making?

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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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