CRII: CHS: Humanizing Algorithms: Empirical and Design Investigations of Sensitive Algorithmic Encounters

这个项目将研究算法的相互作用，并开发以人为中心的系统设计策略，将算法及其基础数据结合在一起。各种平台的软件开发人员都在创造一些功能，这些功能利用了算法管理的内容，这些内容利用了关于人们的关系、行为和身份的数据。然而，算法通常基于易于计算的系统指标做出决定，例如点赞、播放和点击的数量。即使是更复杂的算法，也会受到明确提供或从提供的数据中推断出的社交信息的限制。因此，算法可能无法捕获对这些算法旨在支持的交互的可接受性和成功至关重要的社会背景和人类意义。这项研究将调查算法和人类对社交数据的理解，特别是当它们出现分歧时。通过关注分歧，这项研究可以检查人类对算法的期望，如何重新构建误解，以及这些分歧如何影响后续行动。具体地说，这个项目将确定(1)人们如何导航敏感的算法遭遇；(2)这些遭遇如何影响人们；(3)算法未能理解哪些社会概念；以及(4)在算法管理中需要哪些设计策略来解决敏感内容。为了集中这项工作，具体的调查背景将是与与生命损失有关的内容的算法相遇，因为它在社区和个人层面上都很普遍和敏感。这项工作的更广泛影响包括：(1)围绕与生命损失有关的社交数据的管理和互动制定指导方针，该指南也可以应用于算法应该敏感的其他群体和体验；(2)展示纳入社交数据的设计如何采用以人为中心的方法来敏感与经过算法管理的内容的遭遇；(3)促进设计实践的发展，包括交互、系统、算法和数据的设计；以及(4)通过以人为中心的方法研究和设计社交算法的研究和课程活动，让学生参与多个领域，包括信息科学、计算机科学、媒体研究和通信。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will study algorithmic interactions and develop strategies for the human-centered design of systems that incorporate algorithms and their underlying data. Software developers of platforms of all kinds are creating features that make use of algorithmically curated content that leverage data about people's relationships, behavior, and identities. However, algorithms usually make decisions based on system metrics that are readily calculable, such as the number of likes, plays, and clicks. Even more sophisticated algorithms are limited by the social information explicitly given or inferred from provided data. As a result, algorithms can fail to capture the social context and human meaning that is important to the acceptability and success of the interactions these algorithms are meant to support. The research will investigate both algorithmic and human understandings of social data, especially when they diverge. By attending to divergence, the research can examine human expectations of algorithms, how misunderstandings might be reframed, and how subsequent action is informed by those divergences. Specifically, this project will identify (1) how people navigate sensitive algorithmic encounters; (2) how these encounters impact people; (3) what social concepts algorithms are failing to understand; and (4) what design strategies are needed to address sensitive content in algorithmic curation. To focus this work, the specific context of inquiry will be algorithmic encounters with content related to loss of life, given its prevalence and sensitivity at both communal and individual levels. The broader impacts of the work include: (1) developing guidelines around the curation of and interactions with social data related to loss of life, which can also be applied to other groups and experiences where algorithms should be sensitive; (2) demonstrating how designs that incorporate social data can adopt human-centered approaches to sensitize encounters with algorithmically curated content; (3) contributing to the development of design practices that encompass the design of interactions, systems, algorithms, and data; and (4) engaging students in multiple fields, including Information Science, Computer Science, Media Studies, and Communication through research and curricular activities focused on human-centered approaches to studying and designing social algorithms.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.