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

CHS：媒介：协作研究：超越黑匣子：理解和设计算法媒体的用户期望

• 批准号: 1564041
• 负责人: Karrie Karahalios
• 金额: $ 84.56万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564041&HistoricalAwards=false
• 关键词: CHS; Medium; Collaborative; Research; Beyond

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.

这项研究将研究在线新闻供稿的人与机器共策化，并开发新软件，以比较用户在理想化的供稿策展上的意图，当算法过滤在线新闻供稿中时出现的内容时。这将与研究相结合，该研究调查了用户对算法动作的看法以及用户对控制面板中饲料设置的看法。这些算法流程不仅对大多数用户不透明，而且许多用户甚至不知道算法代表他们做出决定。可能意识到工作中算法过程的人无法验证其存在。例如，用户搜索结果的更改可能是由于算法的更改或用户活动的更改而产生的。 这项工作有可能提高公共算法意识，这可能会导致更深入的探索，甚至可能导致学习算法，这对公众对科学和技术的理解产生了影响。这项研究将在五个主要领域中产生基于证据的知识：（a）社交媒体算法的意识水平，（b）当通过交互式社交饲料可视化工具接触到社交饲料方面时，意识水平和交互行为会如何变化，内容和馈送界面。这项工作的结果将帮助各个领域的研究人员和从业人员（人类计算机互动，社会科学，设计，工程，法律，道德）严格地重新考虑当前的计算和设计实践，并导致界面，并帮助人们了解塑造其生活的算法。将提供用于编程独特算法的代码模板，以鼓励创建算法，并将提供公共存储库来鼓励共享。