EAGER:AI-DCL: Understanding the Relationship between Algorithmic Transparency and Filter Bubbles in Online Media

计算机算法被在线网站广泛用于确定用户看到的内容，例如，通过策划新闻文章或推荐社交媒体帖子。这些算法主要用于通过向用户显示他们可能感兴趣的内容来改善用户体验。然而，越来越多的证据表明，这些算法可能会产生意想不到的副作用。例如，通过仅向用户显示符合他们先前存在的感知和信念的内容，用户可能会接收到所有内容的有偏见的子集，这可能增加智力隔离，这是一种被称为“过滤气泡”的现象。“这个项目通过研究过滤器气泡形成的方式和原因，并开发新的算法来防止它们，从而促进了计算科学的进步。此外，该奖项还支持伊利诺伊理工大学的两名博士生的跨学科培训，由计算机科学和政治学教师共同提供建议，并将为在线社交网络分析，算法透明度和公共政策课程提供新的课程。该项目的技术方法侧重于内容推荐算法的两个增强：1）通过通知用户他们的阅读习惯、推荐模型对他们的看法以及为什么推荐特定项目来提高透明度;以及2）通过使用户能够提供关于模型预测和解释的反馈来支持丰富的用户交互。新算法的开发，以支持现代的，基于神经网络的推荐系统，可扩展到高维文本域的透明度和互动。该项目进行了广泛的用户研究，以衡量透明度和互动对过滤气泡的形成和严重程度的影响。该项目的一个关键方面是开发一个开源平台和相应的数据集，这将促进更多的研究，以更好地理解并最终减轻过滤器气泡。该平台不仅包括用于识别用户偏好和进行内容推荐的工具，还包括用于进行用户研究以衡量推荐系统的变化如何影响过滤气泡形成的工具。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估而被认为值得支持。

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.