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ETHICS OF DATA AGGREGATION: PRIVACY, TRUST, AND FAIRNESS

数据聚合的道德规范：隐私、信任和公平

基本信息

批准号：
1649415
负责人：
Kirsten Martin
金额：
$ 14.9万
依托单位：
George Washington University
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：
2016-09-15 至 2019-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649415&HistoricalAwards=false
关键词：
ETHICS DATA AGGREGATION PRIVACY TRUST

项目摘要

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.

该项目仔细检查数据聚合，以了解哪些类型的聚合对于个人来说具有规范性和描述性重要性，以及不同类型和程度的聚合如何影响个人信任。这项拟议的研究将增进对大数据、信任和商业道德研究的知识和理解。对数据聚合的初步研究是为了确保准确性并减少不必要的偏差。这项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 - 这些对于用户来说通常很重要。该项目还通过关注数据聚合的规范判断，扩展了围绕大数据的伦理影响的工作。这些研究将为支持有关汇总、存储和删除消费者数据的政策决策提供经验证据。研究结果将通过商业、道德和隐私等跨学科会议广泛传播，以增强科学和技术知识。大数据依赖于聚合来自异构源的数据来创造新知识并识别新趋势：研究人员在社交网站中寻找模式，营销人员通过聚合在线上下文中的行为来了解消费者需求，网站可以跟踪消费者对设备的兴趣。迄今为止，人们的注意力集中在通过匹配记录、去（重新）识别和数据的潜在用途来聚合时间和空间数据的技术能力。这项研究将通过收集经验数据来了解个人对不同数据从业者不同类型和程度的数据聚合实践的行为反应，从而填补文献中的空白。该项目的目标首先是通过阶乘小插图调查方法，归纳探索重要的聚合类型 - 跨技术/设备、跨时间、跨位置、跨上下文、跨信息类型。这一探索阶段旨在确定判断数据聚合是否增强信任的最重要因素。第二阶段将通过实验衡量这些重要类型的数据聚合对个人信任的影响。