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

被引:28
作者
Abiteboul, Serge [1 ,2 ]
Stoyanovich, Julia [3 ]
机构
[1] ENS, INRIA, Paris, France
[2] PSL Univ, Ecole Normale Super, Inst Natl Rech Informat & Automat, F-75005 Paris, France
[3] NYU, Tandon Sch Engn, Dept Comp Sci & Engn, 370 Jay St, Brooklyn, NY 11201 USA
来源
ACM JOURNAL OF DATA AND INFORMATION QUALITY | 2019年 / 11卷 / 03期
基金
美国国家科学基金会;
关键词
Transparency; fairness; data protection; neutrality; responsible data science; DISCRIMINATION;
D O I
10.1145/3310231
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The data revolution continues to transform every sector of science, industry, and government. Due to the incredible impact of data-driven technology on society, we are becoming increasingly aware of the imperative to use data and algorithms responsibly-in accordance with laws and ethical norms. In this article, we discuss three recent regulatory frameworks: the European Union's General Data Protection Regulation (GDPR), the New York City Automated Decisions Systems (ADS) Law, and the Net Neutrality principle, which aim to protect the rights of individuals who are impacted by data collection and analysis. These frameworks are prominent examples of a global trend: Governments are starting to recognize the need to regulate data-driven algorithmic technology. Our goal in this article is to bring these regulatory frameworks to the attention of the data management community and to underscore the technical challenges they raise and that we, as a community, are well-equipped to address. The main takeaway of this article is that legal and ethical norms cannot be incorporated into data-driven systems as an afterthought. Rather, we must think in terms of responsibility by design, viewing it as a systems requirement.
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页数:9
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