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Month: July 2020

FATE Research Group: From Why to What and How

FATE Research Group: From Why to What and How

When the public started getting access to the internet, search engines became common in daily usage. Services such as Yahoo, AltaVista, and Google were used to satisfy people’s curiosity. Although it was not comfortable using search engines because users had to go back and forth between all the search engines, it seemed like magic that users could get so much information in a very short time. At that time, users started using search engines without any previous training. Before search engines became popular, the public generally found information in libraries by reading the library catalog or asking a librarian for help. In contrast, typing a few keywords is enough to find answers on the internet. Not only that, but search engines have been continually developing their own algorithms and giving us great features, such as knowledge bases that enhance their search engine results with information gathered from various sources.

Soon enough, Google became the first choice for many people due to its accuracy and high-quality results. As a result, other search engines got dominated by Google. However, while Google results are high-quality, those results are biased. According to a recent study, the top web search results from search engines are typically shown to be biased. Some of the results on the first page are made to be there just to capture users’ attention. At the same time, users tend to click mostly on results that appear on the first page. The study gives an example about a normal topic: coffee and health. In the first 20 results, there are 17 results about the health benefits, while only 3 results mentioned the harms.

This problem led our team at the InfoSeeking Lab to start a new project known as Fairness, Accountability, Transparency, Ethics (FATE). In this project, we have been exploring ways to balance the inherent bias found in search engines and fulfill a sense of fair representation while effectively maintaining a high degree of utility.

We started this experiment with one big goal, which is to improve fairness. For that, we designed a new system that shows two sets of results, both of which are very similar to Google’s dashboard. (as illustrated by picture below).  We have collected 100 queries and top 100 results per query from Google in general topics such as sports, food, travel, etc. One of these sets is obtained from Google. The other one is generated through an algorithm that reduces bias. The system has 20 rounds. The system gives a user 30 seconds on each round to choose the set they prefer.

For this experiment, we asked around 300 participants to participate. The goal is to see if participants can notice a difference between our algorithms and Google. The early results show that participants preferred our algorithms more than Google. However, we will discuss more in detail as soon as we finish the analysis process. Furthermore, we are in the process of writing a technical paper and an academic article.

Also, we have designed a game that looks very similar to our system. This game tests the ability to notice bad results. It gives you a score and some advice. In this game, users can also challenge their friends or members of their families. To try this game, click here

For many years, the InfoSeeking Lab has worked on issues related to information retrieval, information behavior, data science, social media, and human-computer interaction. Visit the InfoSeeking Lab website to know more about our projects

For more information about the experiment visit FATE project website

Jonathan Pulliza successfully defends his dissertation

Jonathan Pulliza successfully defends his dissertation

Jonathan Pulliza, Ph.D. student

Our Ph.D. student, Jonathan Pulliza, has successfully defended his dissertation titled titled “Let the Robot Do It For Me: Assessing Voice As a Modality for Visual Analytics for Novice Users”. The committee included  Chirag Shah (University of Washington, Chair), Nina Wacholder (Rutgers University), Mark Aakhus (Rutgers University), and Melanie Tory (Tableau).

Pulliza’s study focuses on understanding how the voice system facilitates novice users in Visual Analytics (VA). He found that participants chose to use the voice system because of its convenience, ability to get a quick start on their work, and better access to some functions that they could not find in the traditional screen interface. Participants refrained from choosing voice because of their previous experiences. They felt that using the voice system would not provide then all access to the more complicated VA system. They then often chose to struggle with the visual interface instead of using the voice system for assistance.


The growth of Visual Analytics (VA) systems has been driven by the need to explore and understand large datasets across many domains. Applications such as Tableau were developed with the goal of better supporting novice users to generate data visualizations and complete their tasks. However, novice users still face many challenges in using VA systems, especially in complex tasks outside of simple trend identification, such as exploratory tasks. Many of the issues stem from the novice users’ inability to reconcile their questions or representations of the data with the visualizations presented using the interactions provided by the system.

With the improvement in natural language processing technology and the increased prevalence of voice interfaces, there is a renewed interest in developing voice interactions for VA systems. The goal is to enable users to ask questions directly to the system or to indicate specific actions using natural language, which may better facilitate access to functions available in the VA system. Previous approaches have tended to build systems in a screen-only environment in order to encourage interaction through voice. Though they did produce significant results and guidance for the technical challenges of voice in VA, it is important to understand how the use of a voice system would affect novice users within their most common context instead of moving them into new environments. It is also important to understand when a novice user would choose to use a voice modality when the traditional keyboard and mouse modality is also available.

This study is an attempt to understand the circumstances under which novice users of a VA system would choose to interact with using their voice in a traditional desktop environment, and whether the voice system better facilitates access to available functionalities. Given the users choose the voice system, do they choose different functions than those with only a keyboard and a mouse? Using a Wizard of Oz set up in the place of an automated voice system, we find that the participants chose to use the voice system because of its convenience, ability to get a quick start on their work, and in some situations where they could not find a specific function in the interface. Overall function choices were not found to be significantly different between those who had access to the voice system versus those who did not, though there were a few cases where participants were able to access less common functions compared to a control group. Participants refrained from choosing voice because their previous experiences with voice systems had led them to believe all voice systems were not capable of addressing their task needs. They also felt using the voice system was incongruent with gaining mastery of the underlying VA system, as the convenience of using the voice system could lead to its use as a crutch. Participants then often chose to struggle with the visual interface instead of using the voice system for assistance. In this way, they prioritized building a better mental model of the system over building a better sense of the data set and accomplishing the task.