Computer algorithms are widely used by online sites to determine the content users see, for example, by curating news articles or recommending social media posts. These algorithms are primarily designed to improve user experience by showing to users the content that they are likely to be interested in. However, there is growing evidence that these algorithms may have unintended side effects. For example, by showing users only content that conforms with their preexisting perceptions and beliefs, users may receive a biased subset of all content, possibly increasing intellectual isolation, a phenomenon known as a "filter bubble." This project promotes the progress of computational science by investigating how and why filter bubbles form and developing new algorithms to prevent them. Additionally, this award supports the cross-disciplinary training of two PhD students at Illinois Tech, jointly advised by computer science and political science faculty, and will result in new curricula for courses in online social network analysis, algorithmic transparency, and public policy.
The technical approach of the project focuses on two enhancements to content recommendation algorithms: 1) improving transparency by informing the users of their reading habits, what the recommendation model thinks of them, and why particular items are recommended; and 2) supporting rich user interactions by enabling the user to provide feedback on model predictions and explanations. New algorithms are developed to support transparency and interaction for modern, neural network-based recommendation systems, scalable to high-dimensional text domains. The project conducts extensive user studies to measure the impact that transparency and interactions have on the formation and severity of filter bubbles. A key aspect of this project is the development of an open-source platform and accompanying datasets that will foster additional research to better understand and ultimately mitigate filter bubbles. This platform includes tools not only for identifying user preferences and making content recommendations, but also for conducting user studies to measure how changes to the recommendation system affect filter bubble formation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
