BIGDATA: F: Collaborative Research: Foundations of Responsible Data Management

大数据：F：协作研究：负责任的数据管理的基础

• 批准号: 1741047
• 负责人: Julia Stoyanovich
• 金额: $ 48.49万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1741047&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; Foundations; Responsible

Big Data technology promises to improve people's lives, accelerate scientific discovery and innovation, and bring about positive societal change. Yet, if not used responsibly, this same technology can reinforce inequity, limit accountability and infringe on the privacy of individuals: irreproducible results can influence global economic policy; algorithmic changes in search engines can sway elections and incite violence; models based on biased data can legitimize and amplify discrimination in the criminal justice system; algorithmic hiring practices can silently reinforce diversity issues and potentially violate the law; privacy and security violations can erode the trust of users and expose companies to legal and financial consequences. The focus of this project is on using Big Data technology responsibly -- in accordance with ethical and moral norms, and legal and policy considerations. This project establishes a foundational new role for data management technology, in which managing the responsible use of data across the lifecycle becomes a core system requirement. The broader goal of this project is to help usher in a new phase of data science, in which the technology considers not only the accuracy of the model but also ensures that the data on which it depends respect the relevant laws, societal norms, and impacts on humans. This project defines properties of responsible data management, which include fairness (and the related concepts of representativeness and diversity), transparency (and accountability), and data protection. It complements what is done in the data mining and machine learning communities, where the focus is on analyzing fairness, accountability and transparency of the final step in the data analysis lifecycle, and considers the problems that can be introduced upstream from data analysis: during dataset selection, cleaning, pre-processing, integration, and sharing. This project develops conceptual frameworks and algorithmic techniques that support fairness, transparency and data protection properties through all stages of the data usage lifecycle: beginning with data discovery and acquisition, through cleaning, integration, querying, and ultimately analysis. The contributions are structured along three aims. Aim 1 considers responsible dataset discovery, profiling, and integration. Aim 2 considers responsible query processing and develops a general framework for declarative specification, checking and enforcement of fairness, representativeness and diversity. Aim 3 incorporates data protection into the lifecycle, develops techniques to facilitate sharing of sensitive data, and considers the tradeoffs between privacy and transparency. This project is poised to establish a multidisciplinary research agenda around responsible data management as a critical factor in enabling fairness, accountability and transparency in decision-making and prediction systems. Additional information about the project is available at DataResponsibly.com.

大数据技术有望改善人们的生活，加快科学发现和创新，并带来积极的社会变革。但是，如果不负责任地使用，同样的技术可以加强不平等，限制对个人隐私的责任感和侵犯：不可再现的结果会影响全球经济政策；搜索引擎的算法变化可能会影响选举和煽动暴力；基于偏见数据的模型可以使刑事司法系统中的歧视合法化并扩大歧视；算法招聘实践可以默默地加强多样性问题，并可能违反法律；隐私和安全侵犯可能会侵蚀用户的信任，并使公司承受法律和财务后果。该项目的重点是按照道德和道德规范以及法律和政策考虑，负责任地使用大数据技术。该项目确立了数据管理技术的基础新作用，在该技术中，管理整个生命周期的数据使用成为核心系统的要求。该项目的更广泛的目标是帮助迎来一个新的数据科学阶段，在该阶段中，技术不仅考虑了模型的准确性，而且还确保其依赖其依赖相关法律，社会规范和对人类的影响的数据。该项目定义了负责任的数据管理的属性，其中包括公平性（以及代表性和多样性的相关概念），透明度（以及问责制）和数据保护。它补充了数据挖掘和机器学习社区中所做的事情，其中​​重点是分析数据分析生命周期最终步骤的公平性，问责制和透明度，并考虑了可以从数据分析中引入上游的问题：在数据集选择期间选择，清洁，预处理，预处理，集成，集成和共享。该项目开发了概念框架和算法技术，这些技术通过数据使用生命周期的所有阶段来支持公平，透明度和数据保护属性：从数据发现和获取开始，通过清洁，集成，查询，查询，最终分析。这些贡献是按三个目标结构的。 AIM 1考虑负责任的数据集发现，分析和集成。 AIM 2考虑了负责任的查询处理，并开发了宣告性规范，检查和执行公平，代表性和多样性的一般框架。 AIM 3将数据保护纳入生命周期，开发技术以促进敏感数据共享，并考虑隐私和透明度之间的权衡。该项目有望建立围绕负责数据管理的多学科研究议程，这是在决策和预测系统中实现公平，问责制和透明度的关键因素。有关该项目的其他信息，请访问dataresponsible.com。

项目成果

On Obtaining Stable Rankings

关于获得稳定的排名

• 发表时间: 2018
• 期刊: PVLDB
• 作者: Abolfazl Asudeh, H. V.

Transparency, Fairness, Data Protection, Neutrality: Data Management Challenges in the Face of New Regulation

透明、公平、数据保护、中立：新监管下的数据管理挑战

• DOI: 10.1145/3310231
• 发表时间: 2019
• 期刊: Journal of Data and Information Quality
• 作者: Abiteboul, Serge;Stoyanovich, Julia
• 通讯作者: Stoyanovich, Julia

Online Set Selection with Fairness and Diversity Constraints

• DOI: 10.5441/002/edbt.2018.22
• 发表时间: 2018
• 作者: Julia Stoyanovich;Ke Yang;H. V. Jagadish

A Query Engine for Probabilistic Preferences

概率偏好的查询引擎

• DOI: 10.1145/3183713.3196923
• 发表时间: 2018
• 期刊: ACM SIGMOD
• 作者: Cohen, Uzi;Kenig, Batya;Ping, Haoyue;Kimelfeld, Benny;Stoyanovich, Julia
• 通讯作者: Stoyanovich, Julia

Special Session: A Technical Research Agenda in Data Ethics and Responsible Data Management

特别会议：数据伦理和负责任的数据管理的技术研究议程

• DOI: 10.1145/3183713.3205185
• 发表时间: 2018
• 期刊: ACM SIGMOD
• 作者: Stoyanovich, Julia;Howe, Bill;Jagadish, HV
• 通讯作者: Jagadish, HV