ETHICS OF DATA AGGREGATION: PRIVACY, TRUST, AND FAIRNESS

该项目仔细研究数据聚合，以了解哪些类型的聚合对个人具有规范性和重复性重要性，以及不同类型和程度的聚合如何影响个人信任。 这项拟议中的研究将促进对大数据、信任和商业道德研究的认识和理解。 对数据汇总的初步调查是技术性的，以确保准确性和减少不必要的偏见。本研究探讨了概念上重要的聚合类型-信息类型，时间，位置，上下文，技术-这是规范的重要用户。该项目还通过关注数据聚合的规范性判断，扩展了围绕大数据伦理影响的工作。这些研究将提供经验证据，以支持有关聚合，存储和删除消费者数据的政策决策。研究结果将通过商业、道德和隐私等跨学科的会议广泛传播，以增进科学和技术知识。 大数据依赖于聚合来自不同来源的数据来创建新知识并识别新趋势：研究人员在社交网站中寻找模式，营销人员通过在线环境中聚合行为来了解消费者需求，网站可以通过设备跟踪消费者兴趣。 到目前为止，注意力集中在通过匹配记录、去识别（重新识别）和数据的潜在用途来汇总时间和空间数据的技术能力上。本研究将通过收集经验数据来了解不同数据从业者对不同类型和程度的数据聚合实践的个人行为反应，从而填补文献中的空白。该项目旨在首先通过因子小插曲调查方法，归纳探索重要的聚合类型-跨技术/设备、跨时间、跨地点、跨背景、跨信息类型。 这一探索阶段旨在确定判断数据聚合是否能加强信任的最重要因素。第二阶段将通过实验测量这些重要类型的数据聚合对个人信任的影响。

This project closely examines data aggregation to understand what types of aggregation are normatively and descriptively important to individuals and how do different types and degree of aggregation impact individual trust. This proposed research would advance knowledge and understanding within the study of big data, trust, and business ethics. Initial investigations into data aggregation have been technical to ensure accuracy and diminish unwanted bias. This research explores the conceptually important types of aggregation - information type, time, location, context, technology - which are normatively important for users. This project also extends the work around the ethical implications of big data by focusing on normative judgments of data aggregation. The studies will provide empirical evidence to support policy decisions around aggregating, storing, and deleting consumer data. The results will be disseminated broadly to enhance scientific and technological knowledge through conferences across disciplines in business, ethics, and privacy. Big data relies upon aggregating data from heterogeneous sources to create new knowledge and identify novel trends: researchers look for patterns in social networking sites, marketers understand consumer demands by aggregating behavior over contexts online, a website can track consumer interests over devices. Attention thus far has focused on technical capabilities of aggregating data over time and space through matching records, de(re)-identification, and the potential use of the data. This research will fill in a gap in the literature by collecting empirical data to understand individuals' behavioral responses towards different types and degrees of data aggregation practices by different data practitioners. This project aims to focus first on inductively exploring the important types of aggregation - across technology/devices, across time, across location, across contexts, across information types - through factorial vignette survey methodology. This exploratory phase aims to identify the most important factors in judging data aggregation as reinforcing trust. The second phase will measure the impact of these important types of data aggregation on individual trust through experiments.