CHS: Medium: Collaborative Research: Beyond the Black Box: Understanding and Designing for User Expectations of Algorithmic Media

这项研究将研究在线新闻源的人类和机器共同管理，并开发新的软件来比较用户的意图与理想化的feed管理，当算法过滤在线新闻源中出现的内容时。这将与调查用户对算法动作的感知以及用户对控制面板中的饲料设置的感知的研究相结合。这些算法过程不仅对大多数用户来说是不透明的，而且许多用户甚至不知道算法正在代表他们做出决定。可能知道工作中的算法过程的人无法验证它们的存在；例如，用户搜索结果的变化可能是由于算法的变化或用户活动的变化造成的。这项工作有可能提高公众对算法的认识，这可能会导致更深入的探索，并可能导致对算法的学习，对公众对科学和技术的理解产生影响。这项研究将在五个主要领域产生基于证据的知识：(a)一般人群的社交媒体算法意识水平，(b)通过交互式社交feed可视化工具暴露于社交feed的各个方面时，意识水平和交互行为是如何变化的，(c)使用feed设置是否会带来更好的feed感知，(d)人们希望在feed中看到什么，以及(e)我们如何通过feed内容和feed界面的设计来传达算法过程。这项工作的成果将帮助各个领域（人机交互、社会科学、设计、工程、法律、伦理）的研究人员和实践者批判性地重新思考当前的计算和设计实践，并导致界面，帮助人们理解塑造他们生活的算法。将提供编程独特算法的代码模板，以鼓励算法创建，并提供公共存储库，以鼓励共享。

This research will study human and machine co-curation of online news feeds and develop new software to compare the user's intent with respect to idealized feed curation, when algorithms filter what appears in online news feeds. This will be combined with research that investigates the user's perception of the algorithm's actions and the user's perceptions of feed settings in control panels. Not only are these algorithmic processes opaque to most users, but many users don't even know that algorithms are making decisions on their behalf. People who might be aware of algorithmic processes at work have no way to verify their existence; changes in a user's search results, for example, might have resulted from a change in the algorithm, or from a change in the user's activity. This work has the potential to increase public algorithm awareness that may lead to more in-depth exploration and possibly to learning about algorithms, with implications for the public understanding of science and technology. This research will generate evidence-based knowledge in five main areas: (a) the level of social media algorithm awareness across a general population, (b) how awareness levels and interaction behavior change when exposed to facets of social feeds via an interactive social feed visualization tool, (c) whether the use of feed settings results in better feed perception, (d) what people want to see in their feeds, and (e) how we can communicate algorithmic process through design of feed content and feed interfaces. The outcomes of this work will help researchers and practitioners in various fields (Human Computer Interaction, Social Science, Design, Engineering, Law, Ethics) critically rethink current computation and design practice and lead to interfaces that help people understand the algorithms that shape their lives. Code templates to program unique algorithms will be provided to encourage algorithm creation, and public repositories will be made available to encourage sharing.